BIM as a Tool for Developing Smart Buildings in Smart Cities: Potentialities and Challenges

Abstract

Building Information Modeling (BIM) has established itself as a strategic and indispensable tool for designing and implementing smart buildings within the context of smart cities. This study explores the potentialities and challenges of using BIM across the main stages of the smart building lifecycle: design, construction, and operation and maintenance. We conducted comprehensive, detailed, and interpretative literature research to extract the main concepts and knowledge, enabling us to identify the main potentialities and challenges and classify them by life-cycle phase for smart buildings. Potentialities and challenges were prioritized based on the number of projects that cited them. The inclusion criteria for identifying potentialities and challenges were based on their key attributes: significant impact, information modeling potential, integration capability with other tools and methods, and improved performance in processes and services across all life cycle phases and BIM dimensions. The findings reveal that the main potentials include optimizing information management, reducing operating costs, enhancing environmental sustainability, and enhancing decision-making processes. Furthermore, the study highlights BIM’s role in integrating technologies such as IoT, augmented reality, and energy simulations, contributing to the development of more sustainable and functional buildings. However, challenges to its full adoption persist, including financial constraints, interoperability issues between systems, a lack of specialized technical skills, and organizational resistance to change. The dependence on advanced technological infrastructure and robust connectivity poses an additional challenge, especially in developing countries, where such resources may be scarce or inconsistent. Finally, this study suggests that future research should explore the integration of BIM with emerging technologies, such as artificial intelligence and digital twins, further expanding its applicability in the smart urban context.

1. Introduction

With the advancement of global urbanization, smart cities have become a global trend, being among the most studied topics in academic and scientific circles. Considering the field of urban development, they are seen as the evolution of urban development. Although there are still divergences about their general concept, smart cities have at least four characteristics: urbanization, intelligence, sustainability, and quality of life. Furthermore, focus is typically placed on managing health, transport, energy, and governance to meet people’s needs and improve their quality of life [1]. As stated by Hajduk [2], smart cities aim to drive innovation and technologies focused on solutions to problems arising from increasing population density.

Some of these problems are related to public spaces, urbanization, and old buildings with envelopes that do not contribute to the sustainability, comfort, or well-being of their users. In this sense, Carrasco et al. [3] argued that adapting buildings to the environment should be one of the main objectives of urban planning, considering relevant climate variables. Charoenporn et al. [4] emphasized that smart buildings for education, tourism, health, and public safety have a direct impact on people’s quality of life, which is referred to as “smart living”.

The development of smart buildings has as its main milestone the emergence of the BIM methodology in the construction industry, which, according to Ngoc et al. [5], occurred in 1992 as a result of the efforts of software companies to eliminate or minimize deficiencies in information interoperability within the construction sector [6]. The emergence of BIM allowed the stakeholders of a project to exchange data through a more complete technology that has parametric modeling and interoperability [7]. Since then, BIM has evolved in data integration characterized by its dimensions, which currently range from 3D to 10D. Although the dimensions from 7D to 10D are considered relatively new and there are few studies on them, they are also recognized as significant developments in the architecture, engineering and construction (AEC) industry. Another important point is that the BIM methodology and its dimensions support all stages of a building’s life cycle, from the planning phase to the demolition phase. Thus, the definition of the most well-known dimensions of BIM are (3D) design and materials, (4D) time management, (5D) cost management and (6D) sustainability [8,9,10,11,12]. Other lesser-known dimensions include (7D) facilities management, (8D) safety in the design and construction phase, (9D) agile construction, and (10D) construction industrialization [13,14,15,16]. In short, BIM is an intelligent methodology that aims to facilitate the efficient exchange of data at all stages of the project, thereby interconnecting (AEC) industries [11].

Several authors have conducted studies on the applications of BIM, specifically addressing one or another phase of the building’s lifecycle, seeking to highlight possible technological integrations, potentialities, and challenges. For example, Montiel-Santiago et al. [8] highlighted the potential to control, direct, and monitor information across various stages of a project, and [1] discussed the potential and challenges, including information integration and management, where weak approaches limit the use of BIM. We also identified studies that, although not intended to identify barriers, cited barriers or presented situations that could be considered barriers. However, we did not identify any scientific articles in the literature that aimed to comprehensively and integratively address the potentialities and challenges of BIM, specifically as a tool for the development of smart buildings in the context of smart and sustainable cities, across the main lifecycle phases.

To help fill this gap, this work aims to identify, systematize, categorize, and classify the potentialities and challenges of BIM in the implementation of smart buildings, in the context of smart and sustainable cities.

This work presents the following innovations and contributions:

(a)

The work adopts a thematic framework, which uses a holistic and structured approach to comprehensively research the potentialities and challenges of BIM, specifically as a tool for the development of smart buildings in the context of smart and sustainable cities. There is no work with this objective in the literature reviewed.

(b)

The work adds a layer of analysis, systematization, and consolidation not found in the existing literature, specifically in the intersection of themes.

(c)

The work identifies and prioritizes the potentialities and challenges of using BIM, which helps to recognize technological trends, methodological gaps, and integration opportunities that are not evident in isolated studies. Recurrence is used as a criterion to prioritize findings, highlighting the most critical and promising aspects of applying BIM in smart buildings.

(d)

The work structures the findings by life cycle phases, offering a longitudinal and integrated view of BIM use, which allows mapping the most significant challenges and potentialities at each stage, facilitating decision-making by professionals and urban managers.

(e)

The work provides diagrams that summarize the main findings by life cycle phase.

(f)

The structuring by life cycle phases enables professionals to optimize the application of BIM at specific moments (design, construction, maintenance), based on the knowledge of which potentialities (such as performance simulations in the design phase) and challenges (such as interoperability or lack of standardization) are most relevant in each phase. It also helps professionals focus their efforts on the areas of greatest return and consolidated application (most cited potentialities) and those that require more attention, training, and protocol development (most cited challenges).

(g)

The work helps identify the need for skills development, since the identified challenges point to knowledge and skill gaps.

(h)

The work helps decision-makers prioritize BIM implementation and technology adoption across lifecycle phases, considering the most frequently cited potentialities that are presumably those with the most significant impact or acceptance in academic and professional circles. It also helps investors, decision-makers, and policymakers identify opportunities to overcome the limitations imposed by these challenges and establish strategies to leverage the growth of BIM use and the sustainable development of the sector.

This article is structured as follows: Section 2 presents the methodological procedures for conducting the literature research, the identification of the potentialities and challenges of BIM, and the categorization of these according to the main phases of the smart building lifecycle. Section 3 presents the potentialities and challenges identified in the literature. Section 4 presents and discusses the research results. The conclusions are given in Section 5.

2. Materials and Methods

This study has three main objectives: (1) to research the main potentialities and challenges of BIM that impact the implementation of smart and sustainable buildings, (2) to categorize and classify these potentialities and challenges across the life cycle of smart and sustainable buildings, and (3) the creation of an analytical framework that evidences patterns, recurrences and research gaps, aiming to serve as a guide for researchers, professionals and managers for future studies and work. An exploratory approach was structured in three phases: (1) Bibliographic Research, (2) Identification of the Potentialities and Challenges of BIM, (3) Categorization of the Potentialities and Challenges of BIM according to the Main Phases of the Life Cycle of Smart Buildings.

2.1. Bibliographic Research

Extensive and detailed bibliographic research was carried out by the authors from 11 November 2024 to 7 March 2025 in Web of Science, Scopus, and SciELO, covering works published over the last 10 years, to reflect the current realities of potentialities and challenges.

To carry out bibliographic research, the keywords “smart cities”, “intelligent cities”, “smart buildings”, “intelligent buildings”, “challenges”, “potentiality”, “benefits”, and “Sustainability”, combined with the keyword “building information modeling”, were used. The terms “Challenges,” “Benefits,” and “Sustainability,” when combined with the main keyword, yielded extensive, generic results. Thus, to refine and narrow these results, the term “smart buildings” was added to the searches, significantly improving cohesion with the addressed topic.

Table 1. Results of research carried out on the CAPES periodicals portal.

Search Terms	Number of Publications
“Building Information Modeling” AND “Smart Cities”	289
“Building Information Modeling” AND “Intelligent Cities”	8
“Building Information Modeling” AND “Smart Buildings”	112
“Building Information Modeling” AND “Intelligent Buildings”	86
“Building Information Modeling” AND “Challenges”	3086
“Building Information Modeling” AND “Challenges” AND “Smart Buildings”	36
“Building Information Modeling” AND “Potentiality”	48
“Building Information Modeling” AND “Benefits”	2548
“Building Information Modeling” AND “Benefits” AND “Smart Buildings”	21
“Building Information Modeling” AND “Sustainability”	2990
“Building Information Modeling” AND “Sustainability” AND “Smart Buildings”	20
“Building Information Modeling” AND “Smart cities” AND “Smart buildings”	23
“Building Information Modeling” AND “Intelligent cities” AND “Intelligent buildings”	0

summarizes the number of publications for the set of keywords.

The research using the keywords returned 643 articles from the last 10 years, indicating the relevance of the topic addressed. The selection process followed the steps outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select documents that explicitly addressed the use of BIM in smart buildings within the context of smart cities, with theoretical or practical contributions to potentiality and challenges, even if these terms were not explicitly defined.

Thus, through PRISMA, the screening began with the reading of titles and abstracts, during which 464 articles were excluded due to duplicates, unclear abstracts, articles published in journals without a peer-review system, and full texts unavailable. Next, an in-depth analysis of the complete texts was conducted, resulting in the discarding of another 122 documents that did not contribute to the study, were unrelated to the topic, or had methodology that did not support their validity. Finally, 57 articles were used to develop this research. From these articles, a spreadsheet was created containing the most relevant elements to support and address the research problem. Figure 1 summarizes the bibliographic research using the PRISMA flowchart.

The main works that contributed to the identification of Potentialities and Challenges are presented in the Summary Table in Appendix A. It addresses the main objective, summarizes key points, presents the main findings, and covers other technologies explored by these researchers.

2.2. Identification of BIM’s Potentialities and Challenges and Categorization According to the Main Phases of the Smart Building Lifecycle

The process of identifying BIM’s potentialities and challenges was conducted through an in-depth textual analysis based on the systematic analysis and interpretation of the articles selected from the bibliographic research. To ensure a solid theoretical foundation, each article was evaluated for the diversity of approaches and perspectives. Subsequently, the findings were correlated with the existing body of knowledge on the topic. This systematization enabled categorizing the findings into “Potentials” and “Challenges,” thereby establishing the contributions and limitations of BIM in the studied context.

The analysis of BIM’s potentials and challenges was structured and discussed considering the three main phases of the smart building lifecycle: design, construction, and operation and maintenance. The analysis was conducted without assigning any distinction of importance to these phases. The inclusion criteria used to identify what would be considered a potential or challenge of BIM in smart buildings were based on their key attributes, which should demonstrate:

(a)

Relevant Impact: Across all dimensions and phases of the BIM lifecycle.

(b)

Information Modeling: Potential for data management and representation.

(c)

Integration: Ability to interoperate with other tools and methods.

(d)

Performance: Improvement of processes and services.

This approach enabled specific detailing for each phase, providing in-depth insights into the context of smart buildings.

Table 2. Potentialities Identified in the Literature.

Potentialities	References	Life Cycle
Provides the development of projects through multidisciplinary teams simultaneously	[8,11,17,18,19]	Project and Construction
Improves project interoperability by providing real-time communication and monitoring	[18,20,21,22,23]	Project, Construction and Operation and Maintenance
Facilitates the analysis of the envelope and components of the structure in the search for a more sustainable model	[1,3,8]	Project and Operation and Maintenance
Integrates geospatial methodologies and data into BIM, promoting intelligent modeling and communication of the built environment	[11,17,18,24,25,26]	Project, Construction and Operation and Maintenance
Creates an automatic spreadsheet of material quantities	[19,22,27]	Project and Construction
Optimizes planning, management, decision-making, and resource management through dynamic, multidimensional analysis of project data	[14,21,22,28,29,30]	Project and Construction
Significantly reduces rework and waste in civil construction	[11,17,26]	Construction and Operation and Maintenance
Improves safety in construction	[1,14,22]	Project and Construction
Increases productivity in design and construction	[10,14,26]	Project and Construction
Allows performance simulation and real-time interventions based on building operational data	[8,22,31,32,33]	Project, Construction and Operation and Maintenance
Increases sustainability in construction	[1,8,32]	Project, Construction and Operation and Maintenance
Allows you to perform energy simulation of the structure and reduces modeling time by automating the extraction of geometric data directly from the BIM model	[3,8,32,34]	Project, Construction and Operation and Maintenance
Effectively contributes to certifications such as LEED, BREEAM, and DGNB	[8,10,32]	Project and Operation and Maintenance
Allows the simulation of environmental impacts, such as carbon emissions and water consumption	[10,11,19]	Project, Construction and Operation and Maintenance
Improves building lifecycle management with real-time updates	[5,12,21]	Project, Construction and Operation and Maintenance
It is possible to create plugins that are integrated with BIM to speed up specific processes within the project	[27,35,36]	Project, Construction and Operation and Maintenance
Allows the customization of templates and visual interfaces to meet construction methodologies and different user profiles in real time	[14,37,38]	Project and Operation and Maintenance
Generates information that can optimize the predictive maintenance of the building	[22,35,39]	Project and Operation and Maintenance
Allows you to integrate sustainability and performance parameters in the early phases of architectural design	[1,11,40]	Project

and

Table 3. Challenges identified in the literature.

Challenges	References	Life Cycle
High cost and financial barriers	[12,16,18]	Construction and Operation and Maintenance
Interoperability and lack of global standardization	[16,19,20,35,41]	Project, Construction and Operation and Maintenance
Reliance on internet connections and advanced technology	[6,18,38]	Project, Construction and Operation and Maintenance
Loss of information in drawings and difficulty preserving cloud work sets	[13,18,27]	Project, Construction and Operation and Maintenance
Lack of specialized capacity building and training	[11,12,18,22]	Project, Construction and Operation and Maintenance
Lack of BIM data or models in existing buildings	[11,35,41]	Operation and Maintenance
Cultural and organizational resistance to BIM adoption	[11,12,18]	Project and Construction
Technical interoperability issues in existing BIM models	[18,27,35]	Project, Construction and Operation and Maintenance
It has integration limitations between BIM software and environmental simulation tools	[1]	Project
Faces institutional barriers due to limited adoption of BIM in public policy	[29]	Project, Construction and Operation and Maintenance
Absence of a consolidated framework for integration between BIM and digital twins in operational environments	[33]	Project, Construction and Operation and Maintenance
Presents technical difficulties in linking physical IoT devices and BIM models due to the variety of protocols and manufacturers	[42]	Project, Construction and Operation and Maintenance
It faces challenges related to the lack of specialized professionals, insufficient empirical validation, and homogeneous technical maturity in multidisciplinary teams for effective BIM integration and operation	[40,43,44]	Project, Construction and Operation and Maintenance
It presents challenges in the representation and integrated management of multiscale and multidisciplinary data in urban environments and collaborative platforms	[25,45]	Project and Construction
Limitations for direct and efficient visualization of CityGML models on the web due to semantic complexity	[46]	Project
Has difficulty synchronizing and automatically updating large volumes of data between different project agents	[28]	Project, Construction and Operation and Maintenance
Requires assumptions and inferences about materials and interior characteristics due to the absence of detailed information in old buildings	[47]	Project and Operation and Maintenance
It presents complexity and high costs for the implementation of integrated BIM-blockchain solutions on a large scale	[48]	Project, Construction and Operation and Maintenance
Limitations in detail and accuracy when scanning small utilities or those located in hard-to-reach areas	[17]	Project and Construction

present the potentialities and challenges identified in the literature.

The potentialities and challenges were classified according to their recurrence in the literature. Thus, a citation frequency classification was performed, where the number of works that cited each potential or challenge was counted. This procedure allowed us to measure the relevance of each theme and rank them.

3. Potentiality and Challenges of BIM and Its Dimensions

BIM represents a milestone in project development and management, according to Ngoc et al. [5], which was developed in 1992 and has evolved over the years, enabling modeling, planning, and management of all phases of a building’s life cycle and providing a more technologically advanced way to execute projects.

Some authors worldwide have explored BIM across various contexts, highlighting its potential, integration, and challenges. In the literature, 10 dimensions of BIM can be identified, which are summarized and specified in

Table 4. BIM Dimensions and their Specifications.

Dimensions	Specifications
3D	3D is the BIM environment where project modeling processes take place, namely the layout and design of the construction, using software such as Revit, Vectorworks, SketchUp, ArchiCAD, and others [19,26]. As well as integration with augmented and virtual reality techniques for communication and enhanced spatial analysis [9,22,36].
4D	The incorporation of constructive data into 3D modeling directly improves planning and time management in a project developed in the 4D dimension. Thus, it is possible to use BIM to simulate schedules, coordinate construction stages, and identify risks, strengthening the management of construction processes [14,31,39,47].
5D	5D BIM enables cost efficiency through automated quantitative surveys of labor, materials, and locations. For this, 3D modeling must incorporate all project construction data, improving decision-making at different scales [9,12,19,49].
6D	The sixth dimension of BIM aims to incorporate sustainability information into the model, promoting environmental, energy, and water efficiency, and reducing and controlling CO2. Its use optimizes processes such as performance simulation and monitoring in the promoted areas, collaborating with all phases of the life cycle, in the quest to make buildings smarter and more sustainable [1,10,50,51].
7D	According to Pavón et al. [16], the seventh dimension of BIM is related to the life-cycle phase of “operation and maintenance,” assisting operational management of existing buildings and leveraging georeferenced models, BIM-FM integration, and asset utility databases. It also centralizes the collection, preservation, updating, and sharing of related documents [12,17,22,52].
8D	The eighth dimension has been used to optimize safety in civil construction. Allowing the simulation of field activities, integrating preventive measures into the model, and, with the incorporation of sensors, monitoring risks in real time [14,29,52].
9D	9D BIM aims to optimize construction across the life cycle by using parametric components and automated rules, reducing rework and improving resource efficiency. It encompasses socioeconomic issues and lean construction, related to digitalization and integration with Big Data and IoT to improve overall processes [1,10,16,37].
10D	The 10D dimension seeks to provide standardization and prefabrication in construction through digitization and data integration, aiming to contribute to the industrialization of off-site construction and assembly. Technology integration involves digital manufacturing, convergence with IoT-based digital twins, and real-time data [13,15,36,37].

.

Through a literature review, we identified the following potentiality in the use of BIM applied to smart buildings: -It enables project development by multidisciplinary teams simultaneously; -It improves project interoperability by providing real-time communication and monitoring; -It facilitates the analysis of the envelope and structural components in the search for a more sustainable model; -It integrates methodologies and geospatial data with BIM, promoting intelligent modeling and communication of the built environment; -It creates automatic spreadsheets for material quantities; -It optimizes planning, management, decision-making, and resource management through dynamic and multidimensional analysis of project data; -Significantly reduces rework and waste in civil construction; -Improves construction safety; -Increases productivity in design and construction; -Enables real-time performance simulation and interventions based on building operational data; -Increases construction sustainability; -Enables structural energy simulation and reduces modeling time by automating the extraction of geometric data directly from the BIM model; -Effectively contributes to certifications such as LEED, BREEAM, and DGNB; -Enables the simulation of environmental impacts, such as carbon emissions and water consumption; -Improves building lifecycle management with real-time updates; -Allows for the creation of plugins that integrate with BIM to accelerate specific processes within the project; -Enables the customization of templates and visual interfaces to meet construction methodologies and different user profiles in real time; -Generates information that can optimize predictive building maintenance; -Allows for the integration of sustainability and performance parameters in the early stages of architectural design; -It allows incorporating life cycle assessments and cost–benefit analyses throughout the project phases; -It allows integrating dynamic energy data into 3D urban models with interactive web visualization; -It allows supporting smart decisions between renovation, maintenance, and retrofit, evaluating alternatives in real time based on sustainability indicators and parametric BIM models; -It allows continuously monitoring and evaluating thermal comfort and indoor environmental conditions through sensors integrated with BIM; -It allows tracking, auditing, and protecting critical sustainability information throughout the building’s life cycle, using blockchain in integration with BIM; -It allows managing building utilities through a georeferenced BIM database, integrating semantic attributes and spatial coordinates for smart maintenance.

Regarding ‘It enables project development by multidisciplinary teams simultaneously,’ Montiel-Santiago et al. and Chen et al. [8,11] emphasized that BIM provides integrated management of all stages of the project lifecycle, from initial planning to structural design and installations, optimizing coordination between disciplines and reducing conflicts. Saptari et al. [17] emphasized that BIM enables greater efficiency in the planning and management of buildings and infrastructure by providing advanced tools and insights. Additionally, according to Onungwa et al. [18], the use of BIM in the cloud enhances real-time collaboration between consultants and managers, democratizing access to technology for smaller companies and facilitating data sharing and project monitoring in the field. This integration also enables strategic decision-making based on reliable data, which is essential for smart building projects [11,19].

Regarding the potentiality ‘Improve project interoperability by providing real-time communication and monitoring,’ the adoption of BIM as an integrated data structure throughout the building lifecycle enhances interoperability between disciplines and systems, enabling fluid communication among agents and real-time monitoring of components [20]. Cloud-based BIM enables continuous communication, real-time monitoring, and file visualization, optimizing collaboration between teams [18]. According to Lokshina et al. [21], integrating BIM with IoT and business management systems (BMS) provides real-time access to data, improves interoperability, accelerates decision-making, and detects critical failures. Almatared et al. [22] highlighted that BIM-IoT data fusion enables the collection of information from physical buildings, their use in virtual environments, and the improvement of real-time decision-making. Zhu and Wu [23] noted that the efficient conversion of IFC models to GIS formats enhances interoperability between platforms, facilitating integrated visualization and seamless communication in smart cities.

Regarding the potentiality ‘facilitating the analysis of the envelope and structural components in the search for a more sustainable model,’Carrasco et al. [3] argued that the BIM methodology enables simulations that aid in the analysis of the building envelope, an essential factor for energy savings in tropical climates, where there is a greater demand for thermal comfort due to significant climate impacts. Furthermore, BIM is an effective tool for determining the ideal orientation and envelope of buildings, optimizing energy performance. Simulations in the BIM-6D dimension enable more assertive decisions to improve energy and lighting efficiency in new or existing buildings [1]. Montiel-Santiago et al. [8] further noted that after creating the energy model, it is possible to explore sustainable alternatives, such as utilizing natural light and renewable energy, to optimize energy consumption.

Regarding the potentiality of ‘Integrating geospatial methodologies and data into BIM, promoting intelligent modeling and communication of the built environment,’ Chen et al. and Thompson et al. [11,24] agree that integrating BIM with Geographic Information Systems (GIS) is a fundamental approach for smart building planning, enabling the creation of comprehensive databases and detailed infrastructure analyses. This combination facilitates spatial planning and urban management by efficiently integrating geospatial and construction data, enabling intelligent representations of the built environment, and laying the foundations for the urban digital twin [25]. In this context, the City Information Model (CIM) emerges, expanding the potential of BIM and GIS by enabling the integrated modeling of cities and buildings. Silva et al. [26] emphasized that BIM promotes improved urban quality of life by optimizing project management and meeting infrastructure demands.

Interoperability is also enhanced by implementing standards such as IFC, which enables the exchange of geometric and non-geometric data between BIM tools. In Onungwa et al. [18] it was noted that, although the format is widely used, it still has limitations for real-time queries, underscoring the need for ongoing technological advancements to meet the demands of collaborative projects.

Furthermore, integrating BIM with Life Cycle Assessment (LCA) contributes significantly to sustainable development. This combination enables mathematical optimizations in the design of energy-efficient buildings, helping to reduce environmental impacts throughout the project lifecycle [1]. The use of tools such as LiDAR (laser scanning) further enhances this process, providing detailed spatial data for accurate 3D mapping and modeling, as highlighted by [17].

Another relevant advancement is the convergence of BIM with the Internet of Things (IoT), which enables real-time collaborative management and dynamic control of project activities. Chen et al. [1] emphasized that this integration allows for continuous monitoring of data, such as energy consumption and air quality, thereby optimizing the efficiency of smart buildings and facilitating informed strategic decisions.

The synergy between BIM and blockchain, in turn, offers greater security and traceability of information throughout the project lifecycle, improving process transparency and reliability [11]. Additionally, the use of Digital Twins (DTs) with BIM enhances predictive and simulation capabilities for the operation, maintenance, and performance management of smart infrastructures, promoting greater efficiency in transportation projects and urban buildings.

Regarding the potentiality ‘Creates automatic material quantity spreadsheet,’ Elsehrawy et al. [19] explained that the 3D model is used to detail all construction activities, allowing for the automatic calculation of quantities of materials and elements required for the project. Furthermore, according to Almatared et al. [22], BIM models provide information about assets, including geometric data, quantities, and schedules, facilitating cost estimation and inventory management. Bruno and Fatiguso [27] highlighted that structural software products support the IFC format or plugins, allowing data to be imported into spreadsheets to facilitate analysis. Subsequently, the schedule is linked to the 3D model and updated to a 4D model, incorporating cost and resource information to enhance accuracy [19].

Regarding the potentiality to ‘Optimize planning, management, decision-making, and resource management through dynamic and multidimensional analysis of project data,’ Du [28] demonstrates that BIM enables dynamic, multidimensional analysis of project data, optimizing resource management and facilitating managers’ access to information. Furthermore, it provides a shared information resource throughout a facility’s lifecycle—from conception to demolition—ensuring a reliable basis for strategic decisions [21]. BIM-IoT integration enables the collection of data from physical buildings, utilizing it in virtual environments, and enhancing real-time decision-making, thereby improving the accuracy of planning and management [22,30]. Rodrigues et al. [14] highlighted that BIM, as a collaborative platform based on 3D models, optimizes planning, generates management procedures, and improves the efficiency of quality control and safety measures. Combined with financial planning and sustainable performance, this approach contributes to more informed decision-making, including in social housing projects [29].

Regarding the potentiality ‘significantly reduce rework and waste in civil construction,’ Silva et al. [26] emphasized that BIM, as an integrated digital model, significantly reduces the time and costs associated with construction by improving the coordination and organization of processes through its 3D visualization. Chen et al. and Saptari et al. [11,17] added that this approach also minimizes rework, delays, and conflicts by applying analytical methods that identify critical points in planning and execution. Furthermore, BIM promotes economic sustainability by reducing errors, omissions, and misinterpreted specifications, which often lead to waste and cost increases in construction projects [26].

Regarding the potentiality ‘Improves construction safety,’ BIM has emerged as an effective solution for fire risk management, enabling visualizations, simulations, escape route planning, and early warnings, improving building safety [22]. Chen et al. [1] emphasized that BIM has the potential to enhance occupational safety in construction, notably through training, quantitative risk analysis, and initiatives focused on resilience and safety climate. Furthermore, 4D BIM establishes a comprehensive model that combines data from objects, processes, and activities, aiding in risk analysis and accident prevention [14].

Regarding the potentiality ‘Increases productivity in design and construction,’ according to [26], rapid developments in areas such as environmental and urban planning have driven the use of BIM, which increases productivity, reduces errors, and lowers costs, meeting the demand for agility and efficiency. Rodrigues et al. [14] emphasized that BIM, when applied across all phases of a building’s lifecycle, integrates design and construction, thereby reducing costs and time while improving functionality and safety. It was noted in Alecrim et al. [10] that the processes associated with BIM meet the growing demand for quality and sustainability, promoting the rapid and efficient development of buildings, reducing resources, and improving performance.

Regarding the potentiality ‘It allows for real-time performance simulation and interventions based on building operational data,’ Montiel-Santiago et al. [8] stated that BIM promotes the evolution of traditional systems by integrating dimensions such as geometry, time, costs, and sustainability, providing a comprehensive view of the project. Yang et al. [33] indicated that integrating digital twins with BIM enables real-time testing of interventions based on operational data, thereby improving decision-making. Thus, it enables anticipating critical project scenarios, testing execution strategies, and real-time adjustments to planning, which contribute to more informed decisions and reduced failures during construction [31]. Habib et al. [32] emphasized that BIM supports solutions that identify alternatives to reduce energy and resource consumption, thereby optimizing structural performance. Furthermore, Almatared et al. [22] highlighted that integrating BIM and IoT sensors offers an innovative approach to monitoring and decision-making, thereby expanding the capabilities of structural assessment.

Regarding the potentiality ‘Increase sustainability in construction,’ BIM plays a crucial role by integrating energy-efficiency criteria and reducing environmental impacts from the early planning and design phases onward. Habib et al. [32] emphasized that informed, early decisions can lead to more sustainable, cost-effective projects. The 6D dimension of BIM, focused on sustainability, enables the automatic modeling and evaluation of a building’s energy performance, providing designers with tools to select more efficient and appropriate options [8]. Furthermore, Chen et al. [1] added that BIM contributes to the digital transformation of large infrastructure projects, such as rail and road systems, helping to mitigate the effects of climate change through more sustainable strategies.

Regarding the potentiality ‘It allows for energy simulation of the structure and reduces modeling time by automating the extraction of geometric data directly from the BIM model,’ Carrasco et al. [3] stated that BIM allows for detailed energy simulations, which are essential for optimizing the energy performance of buildings, automating geometric extraction to reduce time and errors in the process [34]. Using tools like Autodesk Insight, it is possible to model and analyze variables such as energy consumption, heating, cooling, solar radiation, and natural lighting. Habib et al. [32] emphasized that this capability enables the identification of efficient alternatives to reduce energy and resource consumption. Furthermore, BIM’s 6D dimension, focused on sustainability, enables the creation of detailed energy models, allowing evaluation of energy-efficiency strategies and exploration of renewable energy sources, such as solar. This approach also facilitates lighting analysis, accounting for factors such as occupancy control, building orientation, and glazed area ratios, thereby promoting greater comfort and resource savings [8].

Regarding the potentiality ‘Effectively contribute to certifications such as LEED, BREEAM, and DGNB,’ 6D BIM plays a crucial role in supporting Green Building Certifications, such as LEED, BREEAM, and DGNB, by integrating sustainability criteria and tools such as Revit Insight, which verifies compliance with daylight credits [8]. Habib et al. [32] emphasized that the 6D dimension of BIM provides a structured approach for managing sustainability data throughout the project lifecycle, aiding in informed decision-making. Furthermore, the use of the LCA method in BIM promotes sustainability assessment from the early stages of the project, facilitating comparisons across scenarios and encouraging holistic, environmentally conscious design [10].

Regarding the potential to ‘Allow the simulation of environmental impacts, such as carbon emissions and water consumption,’ Elsehrawy et al. [19] stated that 6D BIM enables the integration of carbon emissions data into the construction schedule, thereby optimizing environmental planning and control during project execution. They emphasize that this application is still in its early stages and requires advancement to achieve long-term environmental goals. Chen et al. [11] noted that BIM-based frameworks can enhance water conservation, while Alecrim [10] suggested that BIM facilitates the practical application of LCA, promoting more sustainable practices and cradle-to-cradle design approaches.

Regarding the potentiality ‘Improves building lifecycle management with real-time updates,’ BIM provides a reliable basis for decisions throughout the building lifecycle, integrating spatial, material, and operational data [5]. Wang and Tian [12] emphasized that although the operation and maintenance phase accounts for 75% of building costs, BIM offers significant support for managing its complexity. Furthermore, Lokshina et al. [21] emphasized that BIM functions as a shared resource, from initial conception to demolition, optimizing management throughout the lifecycle.

Regarding the potentiality ‘It is possible to create plugins that integrate with BIM to accelerate specific processes within the project,’ tools such as Dynamo allow the development of scripts to automate data sharing and management in BIM, and can act as standalone plugins or integrated with other platforms [35]. Caetano e Leitão [36] pointed out that tools such as RhinoBIM and RosettaBIM facilitate the exploration of automated approaches in BIM environments. Bruno e Fatiguso [27] further noted that the use of plugins can mitigate attribute losses during data import and export.

Regarding the potentiality ‘Allows the customization of templates and visual interfaces to meet construction methodologies and different user profiles in real time’, Singh et al. [37] highlighted that the parameterization and use of specific rules in BIM allow the creation of personalized models that reduce design efforts and simplify the generation of schedules and coordinated drawings, while the integration between BIM and IoT enables the creation of personalized visual interfaces at different levels, adapted to the profile of each user [38]. Rodrigues et al. [14] emphasized that attention to parameterization is crucial for adapting BIM to various objectives and contexts, thereby ensuring greater precision and efficiency in projects. Regarding the potential “Generates information that can optimize predictive building maintenance,” BIM automates data modeling and improves its quality, thereby making energy consumption forecasting more accurate and enabling the identification of improvement interventions [35]. BIM- and AR-based systems facilitate equipment inspection via mobile devices, overcoming the limitations of traditional methods [22]. Han et al. [39] emphasized that BIM promotes integrated delivery and data-driven forecasting, improving management during the operations and maintenance phases.

Regarding the potential to ‘Allow the integration of sustainability and performance parameters into the early stages of architectural design,’ Chen et al. [1] emphasized that BIM enables the direct incorporation of environmental parameters into the early design stages, thereby optimizing energy efficiency and facilitating sustainable decision-making. Mazzoli et al. [40] emphasized that adopting BIM from project inception facilitates the integration of sustainability and performance parameters, thereby enhancing environmental decision-making from the initial stages.

Regarding the potentiality ‘Allows incorporating life cycle assessments and cost–benefit analyses throughout the project phases,’ Sertyesilisik et al. [29] argued that BIM enables automated life-cycle and cost–benefit analyses, optimizing sustainable decision-making from project inception through operation. Chen et al. [1] stated that integrating BIM throughout the building life cycle enables continuous lifecycle assessments and cost–benefit analyses to optimize decision-making.

Regarding the potentiality ‘Allow connecting smart devices to the BIM model, performing full-scale visual simulations, and automating decisions in real time,’ Chen et al. [42] demonstrated that integrating BIM with IoT devices enables automated, real-time decision-making, such as environmental control and the immediate detection of operational failures. BIM integrated with virtual reality helps predict routes, adjust resources, and reduce field failures [43]. Jia et al. [44] emphasized that integrating BIM and IoT enables connecting smart devices to digital models, facilitating automated decision-making and real-time responses.

Regarding the potentiality ‘Allows the integration of dynamic energy data into 3D urban models with interactive visualization via the web,’ Chatzinikolaou et al. [46] highlighted that the combined use of CityGML and Dynamizer ADE enables the interactive visualization of dynamic energy data in 3D urban models accessible via the web. Chen et al. [1] emphasized that adopting BIM from the early stages of architectural design enables the effective integration of environmental criteria and sustainability parameters into design decisions.

Regarding the potential ‘Allows the support of intelligent decisions between renovation, maintenance, and retrofit, evaluating alternatives in real time based on sustainability indicators and parametric BIM models,’ Mazzoli et al. [40] emphasized that the use of parametric BIM models allows for the customization of retrofit scenarios and the evaluation of alternatives in real time, supporting intelligent user-centered decisions between the renovation and reconstruction of old buildings and sustainable performance [47]. Colucci et al. [45] presented a web platform that enables integrated planning and execution of specific cultural heritage maintenance actions by dynamically combining BIM and GIS data.

Regarding the potential ‘Allows continuous monitoring and assessment of thermal comfort and indoor environmental conditions through sensors integrated with BIM,’ Jia et al. [44] stated that the integration between BIM and IoT enables continuous monitoring of thermal comfort and indoor environmental conditions using data collected by sensors connected to the BIM model.

Regarding the potentiality ‘Allows the tracking, auditing, and protection of critical sustainability information throughout the building lifecycle using blockchain integrated with BIM,’ Liu et al. [48] highlighted that the integration between blockchain and BIM enables the tracking, auditing, and protection of critical sustainability information, ensuring transparency and trust throughout the building’s lifecycle.

Regarding the potentiality ‘Allows management of building utilities through a georeferenced BIM database, integrating semantic attributes and spatial coordinates for smart maintenance,’ Saptari et al. [17] demonstrated that the use of georeferenced BIM databases with semantic and spatial attributes enables smart utility management and location-based building maintenance.

Likewise, we were able to identify important challenges in the use of BIM for the implementation of smart buildings, such as: -High cost and financial barriers; -Interoperability and lack of global standardization; -Reliance on internet connections and advanced technology; -Loss of information in drawings and difficulty in preserving sets of work in the cloud; -Lack of specialized training and training; -Lack of BIM data or models in existing buildings; -Cultural and organizational resistance to the adoption of BIM; -Technical interoperability issues in existing BIM models; -It has limitations of integration between BIM software and environmental simulation tools; -Faces institutional barriers due to limited adoption of BIM in public policy; -Absence of a consolidated framework for integration between BIM and digital twins in operational environments; -It presents technical difficulties in linking physical IoT devices and BIM models due to the variety of protocols and manufacturers; -It faces challenges related to the lack of specialized professionals, insufficient empirical validation, and homogeneous technical maturity in multidisciplinary teams for the integration and effective operation of BIM; -It presents challenges in the representation and integrated management of multiscale and multidisciplinary data in urban environments and collaborative platforms; -Limitations for direct and efficient visualization of CityGML models on the web due to semantic complexity; -It has difficulty in synchronizing and automatically updating large volumes of data between different project agents; -Requires assumptions and inferences about materials and interior characteristics due to the absence of detailed information in old buildings; -It presents complexity and high costs for the implementation of integrated BIM-blockchain solutions on a large scale; -Limitations of detail and accuracy in the digitization of small utilities or located in hard-to-reach areas;

Regarding the ‘High Cost and Financial Barriers’ challenge, high cost is a significant barrier to BIM adoption, especially given the initial investments in setup, maintenance, and training [18]. Wang and Tian [12] highlighted that, during the operation and maintenance phase, costs represent the main obstacle, with a negative impact of up to 41.57%. In addition, Pavón et al. [16] identified that, in addition to cost, the skills required and data interoperability also hinder the implementation of BIM.

Regarding the challenge ‘Interoperability and lack of global standardization’, commercial BIM platforms face limitations in integrating real-time sensor data due to commercial constraints [16]. Liu and Zhang [20] noted that the lack of standardization in the use of the IFC format makes it difficult to accurately integration of geometric and non-geometric data across submodels. Del Duca et al. [41] noted that the coordination between BIM and GIS lacks a unified standard, necessitating protocols for the management and exchange of information. On the other hand, Elsehrawy et al. [19] suggested developing tools or plugins that consolidate structures into a single platform, thereby promoting greater interoperability and efficiency. However, Doukari et al. [35] noted that the exchange of data between BIM tools for continuous simulation is hampered by significant technical challenges such as interoperability limitations, including IFC in the role of “glue”, still incomplete, in gaps of validated workflows for devices by telemetry in the model, and in the lack of a mature integration architecture that connects BIM–IoT–simulators in a seamless manner.

Regarding the challenge of ‘Dependence on internet connections and advanced technology’, the use of BIM in the cloud faces significant challenges, such as reliance on internet connectivity and interoperability of services, which are especially critical problems in developing countries where infrastructure is limited [18]. In addition, Isikdag [6] noted that integrating IoT sensors into BIM requires continuous connectivity to deliver real-time data, a fundamental condition for transforming BIM into an active information model. While Dave et al. [38] also highlighted that this integration depends on reliable, stable networks, such as those based on TCP/IP, LoRa, and MQTT protocols, capable of sustaining real-time communication between sensors and digital models. They also describe that distributed architectures, such as client-server, middleware, and cloud-edge, which, while expanding interoperability, introduce technical risks of latency and data loss in environments with unstable network infrastructure. This reliance on advanced technologies such as common data environments, SaaS platforms, and automated synchronization systems also exposes the process to data loss during simultaneous updates when connectivity is unstable. In these cases, geometric attributes, metadata, and revision histories can be corrupted or deleted, compromising the traceability and integrity of project information [18].

Concerning the challenge ‘Loss of information in drawings and difficulty in preserving sets of works in the cloud’, Onungwa et al. [18] indicated that the use of BIM in the cloud is impacted by problems such as loss of information in drawings, due to the fragmentation of BIM models hosted on different platforms and the absence of a unified version and backup protocols, in addition to the difficulty in preserving sets of large-scale projects and works, which depend on the constant updating of interconnected submodels. Volkov and Batov [13] observed that outdated BIM models become unusable for efficient operational management. Bruno and Fatiguso [27] observed the loss of BIM object attributes when importing data manually or semi-automatically into spreadsheets or tabular databases.

Regarding the challenge of ‘Lack of specialized capacity building and training’, the effective application of BIM depends on highly qualified specialists to analyze and determine data requirements, which poses a significant challenge [22]. Wang and Tian [12] highlighted that the talent market lacks qualified professionals, and technical training significantly increases companies’ costs. In turn, Onungwa et al. [18] reinforced that the reliance on training, along with the need for an internet connection, hinders the adoption of BIM, especially in developing countries. Chen et al. [11] complemented this by pointing out that there is a demand for more practical training focused on safety and resilience in the civil construction sector.

Regarding the challenge ‘Lack of BIM data or models in existing buildings’, Doukari et al. [35] found that the use of BIM in energy studies of existing buildings is rare, with digital models often unavailable or developed in isolation by companies, limiting collaboration. Chen et al. [11] noted that retrofitting existing buildings with BIM is challenging due to the multidisciplinary nature and the need for comprehensive information exchange. In addition, Del Duca et al. [41] highlighted the lack of unified standards for integrating BIM and GIS as another barrier.

Regarding the challenge ‘Cultural and organizational resistance to BIM adoption’, Onungwa et al. [18] argued that this is a significant obstacle to BIM adoption, with factors such as market readiness, technology acceptance, and implementation mechanisms influencing its adoption. Wang and Tian [12] noted that while BIM offers significant advantages for commercial buildings, such as multidisciplinary integration, energy simulation, cost control, and lifecycle management, its practical application is limited. Most projects use BIM only in the initial phases, restricted to 3D modeling and interference detection, without fully exploring more advanced dimensions such as costs, energy, and maintenance. In addition, Chen et al. [11] highlighted that modernizing existing buildings is challenging due to the need to integrate multidisciplinary information, including architectural, structural, MEP, energy, and operational, into a single BIM model. Matching this data requires complex technical components, such as reverse modeling, interoperability via IFC, parametric libraries, and cloud platforms. Factors that initially contributed to managers’ resistance to BIM included a lack of familiarity with the technology. However, gradual adoption has demonstrated benefits, including infrastructure coordination and real-time progress, which improves project monitoring [18].

Concerning the challenge ‘Technical interoperability problems in existing BIM models’, the transfer of information between agents and systems becomes partial and prone to failures, compromising the integrated vision necessary for the management of smart and sustainable buildings. Interoperability between different cloud platforms is constrained by the lack of standardization among major vendors such as Autodesk BIM 360, Trimble Connect, Graphisoft BIMcloud, and Bentley ProjectWise, each of which uses proprietary formats and unique APIs. This fragmentation creates “digital silos” that hinder integration between multidisciplinary models and reduce the efficiency of collaborative workflows [18]. Doukari et al. [35] highlighted real-time data modeling and management. The incompatibility between software, formats, and protocols prevents fluid integration between the digital model and IoT systems, compromising the collection, storage, and continuous updating of building information. In addition, Bruno and Fatiguso [27] reported incompatibilities among geometric, topological, and semantic data in complex masonry, highlighting the need to improve synchronization between architectural and structural models.

Regarding the challenge ‘Risks associated with data privacy and security in BIM-connected platforms’, Dave et al. [38] warned that BIM systems connected to IoT devices must consider user privacy and protection against malicious access in distributed and open environments. Yang et al. [33] noted that the integration of BIM models and digital twins on cloud platforms increases security risks and requires measures to protect against data leaks. Chen et al. [42] warned that integrated platforms combining BIM and IoT are susceptible to unauthorized access, requiring encryption, authentication, and access control to maintain data integrity. Jia et al. [44] highlighted that BIM-IoT integration raises significant concerns regarding data privacy and security, necessitating robust encryption and access control strategies.

Regarding the challenge ‘Limitations of energy analysis plugins to correctly interpret complex geometries and custom components of the BIM model’, Ugliotti et al. [34] reported that curved geometries and ventilated facades are not correctly interpreted by energy analysis plugins, requiring manual adjustments and complementary checks. Chen et al. [1] highlighted that energy analysis plugins often struggle to interpret complex geometries and custom components in BIM models, thus compromising the accuracy of the results.

Regarding the challenge ‘Presents integration limitations between BIM software and environmental simulation tools’, Chen [1] noted that the integration between BIM platforms and environmental simulation tools remains limited, making complete real-time energy analysis difficult.

Regarding the challenge ‘Faces institutional barriers due to the limited adoption of BIM in public policies’, Sertyesilisik et al. [29] highlighted that the non-mandatory use of BIM in public policies in several countries, such as Turkey and several developing countries in the Middle East, Africa, and Asia, compromises its large-scale adherence to social housing projects and directly contributes to the maintenance of the cultural resistance challenge in these countries.

Regarding the challenge ‘Absence of a consolidated framework for integration between BIM and digital twins in operational environments’, Yang et al. [33] highlighted that the absence of a consolidated framework between BIM and digital twins compromises the practical adoption of these technologies in smart buildings. This integration should allow full interoperability between the two systems, with continuous data flow, a common ontology, and automatic real-time updating, which is not yet a reality.

Regarding the challenge ‘Presents technical difficulties in linking physical IoT devices and BIM models due to the variety of protocols and manufacturers’, Chen et al. [42] highlighted that the variety of IoT device protocols and manufacturers imposes technical barriers to the stable linking between physical sensors and BIM models.

Regarding the challenge ‘Lack of professionals specialized in integration between BIM and GIS in the construction sector’, Mazzoli et al. [40] stated that the lack of uniform maturity among project participants is a critical challenge for the success of collaborative BIM adoption. For example, the lack of professionals trained in BIM-GIS integration impedes the practical adoption of these technologies on construction sites, resulting in reduced efficiency in spatial planning and site logistics and hindering real-time monitoring of execution. In addition, it hinders the use of sensors and drones integrated into the models, limits the application of predictive safety analysis, and reduces waste, especially in contexts of low digital maturity [43]. Jia et al. [44] highlighted that insufficient empirical validation of large-scale integrated BIM-IoT systems represents one of the main obstacles to the practical adoption of the technology.

Regarding the challenge ‘It presents challenges in the representation and integrated management of multiscale and multidisciplinary data in urban environments and collaborative platforms,’ Mao et al. [25] stated that BIM, because it was designed with a focus on buildings, faces limitations when representing higher levels of the urban environment, such as neighborhoods and entire cities. Colucci et al. [45] noted that efficiently integrating multiscale and multidisciplinary data is a key challenge in the collaborative management of BIM-GIS platforms for planned maintenance.

Regarding the challenge ‘Limitations to direct and efficient visualization of CityGML models on the web due to semantic complexity’, Chatzinikolaou et al. [46] stated that the inherent semantic complexity of CityGML models makes them difficult to view directly on web platforms, requiring specialized solutions such as converting the models to optimized formats, such as glTF and 3D Tiles, and the use of frameworks based on CesiumJS and 3DCityDB, which allow progressive loading and interactive visualization through RESTful APIs.

Regarding the challenge ‘It presents difficulty in the synchronization and automatic updating of large volumes of data between different project agents,’ Du [28] pointed out that the automatic and synchronized update of large volumes of BIM data between project agents remains a significant challenge for efficient collaboration.

Regarding the challenge ‘Requires assumptions and inferences about materials and internal characteristics due to the absence of detailed information about old buildings’, Wu and Maalek [47] pointed out that in the absence of complete information about old buildings, the use of BIM relies on assumptions and inferences, which can affect the accuracy of the assessment.

Regarding the challenge ‘Presents complexity and high costs for implementing large-scale integrated BIM–blockchain solutions’, Liu et al. [48] highlighted that the integration of BIM and blockchain, although promising for ensuring traceability, data security, and transparency in construction sector transactions, still faces significant cost and complexity barriers. The large-scale implementation of these solutions requires robust computing infrastructure, interoperability between platforms, and professionals specialized in both technologies, factors that increase the initial investment and limit their practical application. This complexity directly affects approaches such as the automated control of smart contracts, the decentralized management of project information, and the validation of responsibilities between agents, making their widespread adoption in conventional civil construction projects unfeasible for now.

Regarding the challenge ‘Limitations of detailing and accuracy in the digitization of small utilities or located in hard-to-reach areas’, Saptari et al. [17] noted that limited accuracy and gaps in the digitization of small utilities or those located in hard-to-reach areas make it difficult to obtain complete, detailed BIM models for efficient management.

4. Results and Discussion

4.1. Implementing BIM in Smart Buildings

The application of BIM in the development of smart cities represents a significant advancement in integrating and optimizing urban data, which is crucial for addressing the challenges posed by accelerated urbanization and ensuring sustainability. As a methodology, BIM enables detailed modeling that benefits all parties involved in the building construction and maintenance process [5,26]. This platform is used throughout the project lifecycle, from initial planning and design through construction and operation.

In building smart cities, BIM plays a crucial role by enabling urban planners to visualize the impact of each structure and infrastructure within the city’s overall context. Through its multiple dimensions, BIM enables detailed control of variables such as energy consumption and CO2 generation (6D), budgeting (5D), and schedules (4D), facilitating the creation of efficient and sustainable urban environments [8]. In

Table 5. Application of BIM Dimensions in Smart Buildings.

Dimensions	Specific Contributions to Smart Buildings
3D	Detailed graphical modeling of materials and systems serves as the basis for energy simulations.
4D	Management of schedules, forecasting of deadlines, and control of the execution of the work.
5D	Cost control, detailed budgeting, and integration with physical and financial planning.
6D	Simulations of sustainability, energy efficiency, carbon emissions, and natural resources.
7D	Real estate asset management with an updated and predictive database.
8D	Planning and control of safety on-site, utilizing simulations and risk analysis.
9D	Lean construction and process optimization to reduce waste.
10D	Industrialization of construction and automation of production processes.

, we highlight key points for each dimension that can contribute to transforming buildings, promoting sustainability, and intelligence.

With the 6D dimension, for example, it is possible to perform energy analysis and simulate each building’s performance, supporting the creation of buildings that meet environmental and sustainability standards. These resources are significant in the context of smart cities, where energy efficiency and emission reduction are priorities.

BIM also promotes the concept of the “digital city”, in which all urban elements are modeled in 3D, with linked information that allows for more accurate and adaptable urban management. For example, by using BIM to create models of interconnected buildings within a shared data environment, planners can visualize the impact of various infrastructure options and respond to growing mobility and energy demands with more sustainable solutions [24,53]. In this way, BIM supports smart governance, enabling detailed, real-time analysis of the city’s behavior, which is crucial for managing and optimizing services such as transportation, healthcare, and energy networks.

Additionally, BIM facilitates the integration of emerging technologies, such as IoT and CIM, promoting a more connected and interactive urban infrastructure. By combining BIM with the Internet of Things (IoT), for example, sensors installed in buildings and urban infrastructure can provide real-time data on energy consumption, thereby enabling efficient resource management and reduced environmental impact.

4.2. Potentialities and Challenges of BIM in the Project Phase of Smart Buildings

The use of BIM in the design phase of smart buildings has proven to be a crucial tool for improving project efficiency and accuracy. Lokshina et al. [21] emphasized that BIM serves as a shared information resource throughout a facility’s lifecycle, providing a reliable basis for informed decision-making. This characteristic is particularly relevant in the initial phase of projects, where critical decisions directly impact the results of subsequent stages.

The integration of BIM with technologies such as the Internet of Things (IoT) enables real-time data fusion, optimizing access to information about both physical and virtual construction, fostering collaboration among teams, and accelerating decision-making [22]. This potential is further amplified by the use of cloud-based BIM, which facilitates seamless communication and real-time monitoring, thereby reducing errors and promoting a more integrated work environment [18].

In addition, BIM’s 3D modeling enables detailed elaboration of construction activities, automates quantitative surveying, and reduces rework related to incorrect measurements and specifications [19]. This capability significantly reduces costs and increases sustainability in projects, aligning with smart cities’ demands for more effective, environmentally conscious solutions.

However, this phase is not without challenges. Interoperability between platforms and the lack of global standardization represent significant obstacles, hindering the exchange of information between software and limiting the reach of technological integrations [41]. Additionally, the high initial implementation cost and the need for specialized training are frequently cited barriers, especially in regions with limited resources [12].

In summary, BIM offers a robust set of tools and methodologies that strengthen the design phase of smart buildings. At the same time, it faces technical and organizational challenges that require ongoing attention to ensure full implementation.

The conceptual diagram presented in Figure 2 visually synthesizes the main BIM potentialities and challenges identified in the design phase of smart buildings, along with their respective impact vectors. It functions as an analytical tool that connects the literature findings to the practical and dynamic applications of BIM, highlighting how this methodology contributes to sustainability, interoperability, multidisciplinary planning, and predictive maintenance, while simultaneously facing obstacles such as the need for specialized training, interoperability gaps, high implementation costs, and the absence of global standardization.

4.3. Potentialities and Challenges of BIM in the Construction Phase of Smart Buildings

The construction phase of smart buildings requires high efficiency in coordinating and executing activities, areas where BIM is an essential tool. Montiel-Santiago et al. [8] emphasized that BIM optimizes this step by integrating planning, cost, and sustainability data, promoting the digitalization of processes, and minimizing waste. This approach, aligned with lean construction, prioritizes the efficient management of resources—labor and materials—thereby reducing environmental impact and operating costs [10].

Additionally, BIM helps reduce rework by identifying potential problems through simulations and predictive analysis, thereby avoiding delays and financial losses [1]. Its ability to link schedules to the three-dimensional (3D) model, transforming it into a 4D model and later into a 5D model with cost data, allows for precise management of construction activities, ensuring greater control and predictability [19].

Real-time visualization and the detection of conflicts between different systems and disciplines are other significant potentialities. BIM enables quick adjustments and instant information sharing, promoting collaboration among all project stakeholders. These characteristics are particularly relevant in highly complex constructions, where synchrony between the parts is essential to avoid interruptions [14].

On the other hand, the construction phase presents technical and operational challenges. One of the main challenges is integrating BIM with different technologies, such as sensors and IoT platforms, due to interoperability and data standardization issues [35]. Additionally, insufficient team training and resistance to adopting new working methods are significant barriers that hinder the efficiency of BIM at this stage [12].

Although the challenges are evident, BIM demonstrates great potential to transform the construction phase into a more sustainable, collaborative, and efficient process. Overcoming the identified barriers can further consolidate its role as an indispensable tool in the development of smart buildings.

The conceptual diagram presented in Figure 3 provides a visual synthesis of the main BIM potentialities and challenges identified during the construction phase of smart buildings and their corresponding impact vectors. It serves as an analytical representation that relates the findings from the literature to the operational and managerial dimensions of construction, illustrating how BIM enhances coordination, sustainability, and multidisciplinary planning while addressing persistent issues such as limited interoperability, high implementation costs, insufficient training, and cultural resistance.

4.4. Potentialities and Challenges of BIM in the Operation and Maintenance Phase of Smart Buildings

The operation and maintenance phase of smart buildings represents the most extended period of use in a building’s life cycle and, consequently, the stage with the greatest financial impact. Wang and Tian [12] noted that the costs of this phase can reach 75% of the total, underscoring the importance of leveraging BIM to optimize processes and reduce costs.

One of the primary advantages of BIM at this stage is its ability to serve as a comprehensive database, integrating information on maintenance, energy performance, and structural conditions. Ngoc et al. [5] emphasized that this integration enables real-time updates, facilitating the management of systems and equipment throughout the building’s lifespan. In this context, systems based on BIM and augmented reality (AR) enable visual and predictive inspections of equipment and facilities, overcoming the limitations of traditional paper-based methods [22]. Complementing this approach, Jiang [31] proposed a construction management model based on a BIM digital twin, structured into four layers (physical entity, virtual model, digital linkage, and service application), enabling predictive performance simulation, real-time tracking, and automated maintenance planning. Such a structure significantly enhances the operational efficiency of smart buildings by linking physical sensors to dynamic virtual processes, anticipating failures, and optimizing overall building performance. Ciribini et al. [50], on the other hand, expanded the vision of smart buildings by introducing the concept of “cognitive buildings”, structures capable of learning from the behavior of users and reacting autonomously to changes in the internal and external environment. Through the integration of BIM, IoT, BMS, and user-friendly interfaces (such as apps and augmented reality), it becomes possible not only to optimize operational performance but also to enable two-way communication between users and built assets.

Additionally, 6D BIM plays a significant role in controlling carbon emissions and energy consumption. Energy models based on BIM enable simulations that help identify more efficient alternatives to reduce the environmental impact of operations [32]. These tools are indispensable for smart buildings, where sustainability and efficient resource use are a priority.

However, the operation and maintenance phase also faces notable challenges. Onungwa et al. [18] noted that reliance on advanced technologies, such as IoT sensors and cloud connectivity, can be problematic in areas with limited infrastructure. In addition, Doukari et al. [35] noted that many existing buildings lack adequate BIM models, which hinders the integration of information for efficient management. Organizational resistance and the lack of specialized training further exacerbate these limitations, compromising the full utilization of BIM’s potential [12].

In summary, BIM provides a comprehensive set of solutions to enhance the operation and maintenance of smart buildings, promoting sustainability, efficiency, and predictive management. Overcoming challenges in connectivity, technical enablement, and model integration is key to maximizing the benefits of this methodology.

Figure 4 presents a conceptual diagram that synthesizes the main BIM potentialities and challenges in the operation and maintenance phase of smart buildings, highlighting their impact vectors on building life-cycle management. The diagram shows how BIM, when integrated with emerging technologies such as IoT, BMS, and digital twins, functions as a dynamic and interconnected database, enabling real-time monitoring, predictive maintenance, and energy optimization.

This representation reinforces the strategic role of BIM in this stage, the longest and most financially significant phase of the life cycle, by demonstrating how its application enhances the operational efficiency and sustainability of buildings. At the same time, the diagram also illustrates recurring challenges, such as limited interoperability between systems, high digital infrastructure costs, a shortage of skilled professionals, and dependence on advanced technologies that are not yet widely accessible.

4.5. Classification of Potentialities and Challenges from the Perspective of Researchers Who Publish on the Subject

To quantify the relevance of the identified themes, a citation frequency classification method was adopted. Each BIM strength and challenge was recorded and measured based on the total number of works in the literature review that mentioned it. This frequency served as the primary classification criterion, allowing the themes to be ordered from most to least cited. Themes with the highest citation frequency were considered to be more prominent and more widely accepted in the analyzed literature.

Table 6. Classification of Potentialities by citation frequency.

Potentialities	Quotes
Improves project interoperability by providing real-time communication and monitoring	12
Increases sustainability in construction	9
Allows you to integrate sustainability and performance parameters in the early phases of architectural design	8
Integrates geospatial methodologies and data into BIM, promoting intelligent modeling and communication of the built environment	6
Optimizes planning, management, decision-making, and resource management through dynamic, multi-dimensional analysis of project data	6
Generates information that can optimize predictive building maintenance	6
Enables real-time performance simulation and interventions based on operational data construction	5
Provides the development of projects through multidisciplinary teams simultaneously	5
Improve safety in construction	4
It allows you to carry out an energetic simulation of the structure and reduces modeling time by automating the extraction of geometric data directly from the BIM model	4
It is possible to create plugins integrated with BIM to streamline specific processes within the project	4
Facilitates the analysis of the envelope and components of the structure in the search for a more sustainable model	3
Creates automatic spreadsheets of material quantities	3
Significantly reduces rework and waste in construction	3
Increase productivity in design and construction	3
Effectively contributes to certifications such as LEED, BREEAM, and DGNB	3
Allows simulation of environmental impacts such as carbon emissions and water consumption	3
Improves building lifecycle management with real-time updates	3
Allows customization of templates and visual interfaces to meet construction methodologies and different user profiles in real time	3
It allows you to support smart decisions between renovation, maintenance and retrofit, evaluating alternatives in real time based on sustainability indicators and parametric BIM models	3
Allows you to incorporate life cycle assessments and cost–benefit analyses into all phases of the project	2
Allows you to integrate dynamic energy data into 3D urban models with interactive visualization via the web	2
Allows you to continuously monitor and evaluate thermal comfort and indoor environmental conditions through sensors integrated with BIM	1
Allow tracking, auditing, and protecting critical sustainability information throughout the building lifecycle, using blockchain in integration with BIM	1
Allows you to manage building utilities through a georeferenced BIM database, integrating semantic attributes and spatial coordinates for intelligent maintenance	1

and

Table 7. Classification of Challenges by citation frequency.

Challenges	Quotes
Insufficient specialized training and capabilities	9
Interoperability issues between BIM models	9
Cultural and organizational resistance to BIM adoption	8
High cost	6
Lack of global standardization	6
Shortage of BIM models in smart buildings	5
Reliance on internet connections and advanced technology	3
Loss of information in drawings and difficulty preserving cloud work sets	3
Faces challenges related to the shortage of specialized professionals, insufficient empirical validation, and homogeneous technical maturity in multidisciplinary teams for effective BIM integration and operation	3
It presents challenges in the representation and integrated management of multi-scale and multidisciplinary data in urban environments and collaborative platforms	2
It has integration limitations between BIM software and environmental simulation tools	1
Faces institutional barriers due to limited adoption of BIM in public policy	1
Absence of a consolidated framework for integration between BIM and digital twins in operational environments	1
Presents technical difficulties in linking physical IoT devices and BIM models due to the variety of protocols and manufacturers	1
Limitations for direct and efficient visualization of CityGML models on the web due to semantic complexity	1
It has difficulty synchronizing and automatically updating large volumes of data between different project agents	1
Requires assumptions and inferences about materials and interior characteristics due to the absence of detailed information in old buildings	1
It presents complexity and high costs for the implementation of integrated BIM-blockchain solutions on a large scale	1
Limitations in detail and accuracy when scanning small utilities or those located in hard-to-reach areas	1

present the strengths and challenges classified by citation frequency.

The most cited capability (12 citations) focuses on collaboration and workflow and reflects BIM’s ability to facilitate real-time communication and coordination, a capability demanded by complex projects. Considering the complexity of smart buildings, this result is fully justified. The second and third potentialities (9 and 8 citations) focus on sustainability and BIM’s ability to incorporate performance and sustainability analyses early in the project, a strategic benefit that enables the optimization of design decisions. It is important to emphasize that sustainability and performance improvement are central themes of smart buildings.

The following three potentialities received six citations and are fundamental to the integration of smart buildings and smart cities. The fourth potentiality focuses on integrating the detailed building model provided by BIM with the city map (infrastructure) provided by GIS, which is essential to overcoming BIM’s limitation to the building scale. It enables more complex analyses considering the building’s interaction with the city. The fifth potentiality focuses on multidimensional analysis and management optimization, activities enabled by BIM dimensions that transform modeling into a multidimensional database, supporting data-driven management. It enables smart buildings to operate with accurate and up-to-date data. Smart buildings depend heavily on dynamic data analysis to optimize their operations and interact with smart cities.

The sixth potential focuses on information for predictive maintenance, a strategic benefit of BIM for the longest and most expensive phase of the life cycle: the operation and maintenance phase. The information provided by BIM dimensions, combined with that generated by integration with other technologies, is essential for the functioning of predictive and preventive maintenance algorithms.

Regarding the main challenges, these reflect human, organizational, and technical barriers. The two most cited challenges (9 citations each) concern professional competence and interoperability. Regarding professional competence, this reflects the lack of professionals specialized in the most advanced dimensions of BIM. As previously mentioned, the design, operation, and maintenance of smart buildings integrated with smart cities require the production and management of information generated by BIM dimensions, which transform the model into a multidimensional database. Regarding interoperability, this reflects the difficulties BIM users face when exchanging information across different platforms. This problem occurs both during data migration between BIM software and during data exchange with other technology platforms, such as those that manage IoT sensors.

The third challenge (8 citations) reflects a barrier to adopting BIM dimensions: resistance to changes in work processes, the transition to collaborative workflows, and the incorporation of new skills and knowledge. Furthermore, there is also the difficulty in adequately assessing the cost–benefit ratio, as transition costs are often high and the benefits are not always fully understood.

The fourth and fifth challenges received six mentions and are related to the previous challenges. The fourth challenge focuses on the high cost of acquiring software, restructuring IT processes and infrastructure, and training/hiring professionals. Furthermore, the savings generated by BIM over time are not always adequately measured by decision-makers. The fifth challenge, related to the lack of standardization, is intrinsically linked to interoperability issues. The information structure still lacks unified global standards, making information exchange complex and expensive.

It is also important to emphasize that there is an interconnection between the most cited challenges and potentialities. For example, interoperability is a topic that tops both potentialities (12 mentions) and challenges (9 mentions). Interoperability issues stemming from the lack of global standardization (6 mentions) imply more complex solutions, which in turn contribute to higher costs (6 mentions). However, even if interoperability issues are resolved, professionals may lack the skills required by the software, which characterizes insufficient training (9 citations) and, consequently, the need for specialized training.

Another important point is that each country’s socioeconomic context can influence the potentialities and challenges. In developing countries, structural problems stemming from limited investment resources mean the primary focus remains on improving productivity and reducing losses. In this context, potentialities such as increased productivity in design and construction and a significant reduction in rework and waste (both cited three times) may be more significant than the challenges related to training/capacity (nine citations) and high costs (six citations).

In developed countries, many of the structural problems faced by developing countries have already been resolved. These countries also have greater financing capacity and technological maturity. In this context, the use of BIM may be more focused, for example, on the potential of lifecycle management (6 citations) and real-time performance simulation (5 citations), and on more technical and governance challenges, such as the lack of global standardization (6 citations) and the complexity of integrating data at multiple scales (2 citations).

The results of this study are consistent with findings in the literature related to the practice of BIM use. The EU BIM Handbook [54] and the UK BIM Framework [55] highlight how interoperability and standardization both facilitate and impede digital transformation. Furthermore, they emphasize the importance of open data standards and shared information environments, necessary for cooperation throughout the lifecycle. The Nova BIM-BR Work Plan [56] in Brazil emphasizes that professional training and institutional strengthening are essential for modernizing the public sector through BIM, reinforcing the cross-cutting impact of training across the challenges identified. The UK’s High Speed 2 [57] project further supports this study’s impact drivers, demonstrating how BIM-FM integration improves data traceability, reduces rework, and strengthens maintenance management in complex infrastructure. Furthermore, the buildingSMART Electronic Submission Report [58] broadens the applicability of these findings to operational and regulatory frameworks, advancing the discussion on compliance and maturity levels.

The framework presented in Figure 5 and Table 6 and Table 7 offers complementary layers of analysis: the framework classifies and connects potentialities, challenges, and impacts across the building lifecycle, while the tables prioritize these themes. This dual framework helps identify pathways for professional practice, policy, and research toward smarter and more sustainable buildings.

5. Conclusions

This study analyzed the potentialities and challenges of Building Information Modeling (BIM) in the implementation of smart buildings within smart cities, encompassing the design, construction, operation, and maintenance phases.

The work consolidated an integrated analytical framework that classifies and connects the main potentialities and challenges, including interoperability, sustainability, training, and cost, to their respective impact vectors across the main phases of the building life cycle, making visible the relationships and interdependencies that explain both the success and the limitations of BIM adoption in different contexts.

The results indicate that BIM serves as a structuring framework for the digital transition of the construction sector, enabling more sustainable, efficient, and collaborative processes. The classification based on citation frequency reveals a global consensus on BIM’s role in integrating data and stakeholders, predictive asset management, and reducing waste and rework, particularly in the design and construction phases. However, challenges persist, primarily centered on the shortage of technical training, lack of international standardization, and financial constraints that directly affect the digital maturity of developing countries.

The diagrams and frameworks developed in this study represent original and applicable contributions, as they translate complex evidence into a visual and interpretive structure that facilitates the practical use of BIM in strategic decision-making. This analytical structure not only synthesizes the findings but also provides a visual prioritization guide, allowing stakeholders to identify, in each phase of the life cycle, which potentialities to leverage and which challenges to mitigate immediately. Such an approach enhances the research’s usefulness, transforming it into a decision-support tool for public managers, designers, and policymakers.

From a practical and social perspective, the research demonstrates that the integrated application of BIM can deliver tangible benefits across critical societal sectors, including public health, education, and social housing. In hospitals and clinics, for instance, BIM can optimize predictive maintenance and ensure the uninterrupted functioning of vital systems; in schools and affordable housing, it can reduce costs and accelerate sustainable interventions, promoting energy efficiency and environmental comfort. These applications reinforce BIM’s role as a tool for technological inclusion, transforming technical data into accessible information for participatory urban planning.

Nevertheless, the challenges identified reveal the need for structural and institutional advancements. Financial barriers, limited interoperability among platforms, lack of technical training, and organizational resistance continue to hinder the widespread adoption of BIM, especially in developing countries. The absence of standardization in the use of the IFC format, for example, compromises the integration of technical submodels and hinders data exchange between disciplines. Furthermore, the dependence on advanced technologies and robust connectivity underscores the urgency of public policies that democratize access to digital tools.

The main contribution of this study, therefore, lies in consolidating a replicable interpretive model that integrates theoretical and practical findings and guides technological adoption policies within the construction sector. This framework enables governments, companies, and educational institutions to identify investment priorities and training strategies aligned with local realities, fostering the progressive and sustainable adoption of BIM. At the same time, it paves the way for future research exploring the integration of BIM with digital twins, artificial intelligence, and blockchain—technologies that are already emerging as catalysts for the next generation of resilient and self-adaptive smart cities.

In summary, BIM ceases to be merely a modeling tool and becomes an instrument of systemic transformation, capable of connecting data, people, and policies to pursue a more efficient, sustainable, and intelligent built environment. The strategic and coordinated adoption of this methodology has the potential to redefine the future of construction and urban management, transforming challenges into opportunities for innovation and social equity. Future research may focus on developing technical frameworks for full interoperability, as well as proposing training and standardization strategies that enhance digital maturity in the civil construction sector. The integrated and strategic application of BIM in this scenario becomes a fundamental step in transforming complex urban challenges into smart, sustainable, and connected solutions.