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Review

Digital Intelligence in Building Lifecycle Management: A Mixed-Methods Approach

by
Jianying Lai
1,2,
Runnan Wan
1,
Heap-Yih Chong
1,2,* and
Xiaofeng Liao
3,*
1
School of Engineering Audit, Nanjing Audit University, Nanjing 211815, China
2
Jiangsu Key Laboratory of Public Project Audit, Nanjing 211815, China
3
School of Civil and Hydraulic Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
*
Authors to whom correspondence should be addressed.
Sustainability 2025, 17(11), 5121; https://doi.org/10.3390/su17115121
Submission received: 8 April 2025 / Revised: 27 May 2025 / Accepted: 29 May 2025 / Published: 3 June 2025
(This article belongs to the Special Issue AI Innovations and Digital Transformation for Risk Assessment and Management in Sustainable Construction)
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Abstract

:
The rapid evolution of information technology has positioned digital intelligence as a transformative force across socio-economic domains, necessitating rigorous scholarly examination of its applications and implications. This paper systematically explores the digital intelligence empowerment in Building Lifecycle Management (BLM) under the framework of Construction 4.0. Employing a mixed-methods approach, the research combines a systematic literature review with bibliometric visualization analysis using CiteSpace to map the intellectual landscape, identify key technological drivers (for example, Building Information Modeling, Internet of Things, artificial intelligence, and blockchain), and elucidate integration mechanisms across planning, design, construction, and operational phases. A comparative case study of BLM adoption further demonstrates the transformative impacts of digital intelligence on project efficiency, sustainability, and safety. The research highlights the role of digital intelligence in addressing industry challenges, including resource waste (global construction generates 30% of total waste), safety risks, and stagnant productivity, while fostering innovation and sustainable development. This study advances a holistic model for digital transformation in BLM, offering actionable insights for stakeholders to bridge the academia–industry divide and prioritize strategic investments in interoperable systems, workforce upskilling, and governance frameworks.

1. Introduction

In the era of Industry 4.0, marked by the Fourth Industrial Revolution’s convergence of digital technologies, the global construction industry has been compelled to redefine its practices under the banner of Construction 4.0 [1]. Originating from Germany’s Industry 4.0 strategy, which emphasized cyber–physical systems [2] and smart manufacturing, Construction 4.0 emerged as a parallel movement in the mid-2010s, aiming to address the sector’s long-standing inefficiencies through digital transformation. Key drivers included stagnant productivity growth (e.g., construction labor productivity has grown at only 1% annually over the past two decades compared to 3.6% in manufacturing [McKinsey, New York, NY, USA, 2017]), aging workforces, and escalating demands for sustainability and safety. Construction 4.0 advocates for integrating technologies, such as Building Information Modeling (BIM), the Internet of Things (IoT), artificial intelligence (AI), and automation to create smarter, data-driven workflows across project lifecycles.
As urbanization accelerates and sustainability becomes a universal priority, traditional construction practices are increasingly challenged by inefficiencies, resource waste, and safety risks. Digital intelligence empowerment—integrating advanced technologies, such as BIM, IoT, AI, and blockchain—has emerged as a critical pathway to address these challenges and elevate the sector’s performance across the entire project lifecycle. It serves as a transformative force in Building Lifecycle Management (BLM), driven by advancements in BIM, Geographic Information Systems (GIS), digital twins (DTs), and so on. Previous studies show that these technologies enable real-time monitoring, predictive maintenance, and lifecycle sustainability assessments, significantly enhancing efficiency and decision making in construction and asset management [3], as well as urban facility management and sustainable urban planning [4]. Digital twins, in particular, have revolutionized BLM by enabling continuous permitting, compliance checking, and energy optimization, thereby driving economic growth and operational transparency [5,6].
However, there remains a lack of comprehensive studies on the integration mechanisms and collaborative potential of these technologies across different lifecycle stages, particularly in real-world applications. Against this backdrop, this paper systematically explores the theoretical frameworks and practical applications of digital intelligence in Building Lifecycle Management (BLM). By bridging gaps between academic research and industry practice, the study aims to achieve the following:
(1) Identify key technologies enabling digital intelligence at each lifecycle stage (planning, design, construction, and operation);
(2) Analyze their integration mechanisms and collaborative potential;
(3) Evaluate their impacts on project efficiency, sustainability, and safety through a real-world case study.
Addressing this gap would provide valuable insights for both academic research and industry practice, supporting the development of more holistic and integrated approaches to BLM.

2. Data Sources and Visual Analysis

2.1. Data Source and Processing

In order to study the development of digital intelligence-enabling literature, this paper selected the Web of Science (WoS) database. WoS was prioritized due to its comprehensive coverage of engineering journals [7], though future studies should incorporate multi-database validation (e.g., Scopus/Engineering Village). It is one of the most extensive research repositories, allowing users to search and filter papers covering different research areas. This is particularly useful, because the paper requires a lot of relevant literature for our review, which will cover many areas of research and application. As an approach, we followed the systematic literature review process shown in Figure 1.
Step1: Literature search. In the Web of Science Core Collection database, the literature search process is conducted by setting the following keywords: (“digital” or “intelligence”) and (“empowerment” or “enablement” or “enabling”) and (“construction” or “building*”). Since the concept of Construction 4.0 has been evolving, and the absence of a universally accepted definition, we adopt reference [7] and choose 2014 as the starting point. Then, the time span was selected from 1 January 2014 to 31 December 2024, and a total of 2598 relevant literatures were retrieved.
Step2: Filtering on the basis of the title and abstract. The process of the filtration includes the following: (1) Examine the “title, abstract, and keywords” in each article to select articles that are relevant to the topic and exclude irrelevant articles (e.g., those focused solely on manufacturing digitization, n = 1205; or lacking empirical BLM applications, n = 1180) (Table 1). (2) Read and analyze the articles’ content in detail to ensure that the selected articles are closely related to the research objectives. Two researchers independently screened the articles with a Kappa coefficient of 0.85, indicating strong inter-rater reliability. Discrepancies arose primarily in classifying articles that partially addressed BLM or ambiguously integrated digital intelligence technologies. To resolve disagreements, the researchers conducted iterative discussions to refine inclusion criteria interpretations, referencing the predefined framework in Table 1. For unresolved cases, a third senior researcher adjudicated the final decision. This reconciliation process reduced initial discrepancies by 92%, ensuring consensus on the 213 articles selected (Table 2).
Step3: Removing duplicated articles. After removing duplicated articles, 206 articles were finally selected for further analysis. Table 3 shows the distribution of selected articles from each journal.

2.2. Visual Analysis Results

In this part, CiteSpace is chosen to perform visual analysis. According to the articles selected above, the data downloaded from Web of Science needs to be converted to plain text format. Open the CiteSpace 6.3.R1 software, select import data in the Data Import/Export module, and import the converted text data into CiteSpace as prompted to ensure that the data is correctly recorded into the analysis software. In CiteSpace’s ‘Node Types’ option, select ‘Keyword’ as the analysis node type to focus on keyword analysis. In terms of time slice setting, considering the requirements of data amount and research accuracy, the time span was divided into 2014–2024 time slices, and the time interval of each slice was 1 year.

2.2.1. Keyword Cluster Analysis

As shown in Figure 2, the keyword cluster map was obtained, which clearly showed the core research hotspots in the field of digital intelligence empowerment in construction and their mutual relations. The clustering of keywords, such as “life circle assessment”, “building information modeling” and “blockchain”, is obvious.
The keyword cluster analysis reveals several prominent thematic clusters within the construction domain. Each cluster is represented by a distinct color-coded area, highlighting the correlative of keywords. Cluster #0 “life cycle assessment” [8,9] encompasses keywords such as “circular economy” [10] and “building information modelling (BIM)” [11], indicating that research in this cluster focuses on the comprehensive evaluation of construction projects throughout their life cycles, with a strong emphasis on sustainable practices enabled by BIM. Cluster #2 “building information modeling” [12,13,14] is another significant cluster, with related terms like “augmented reality” [7], “construction management” [15], and “implementation” [16]. This demonstrates the centrality of BIM in modern construction management and its integration with emerging technologies [17,18,19]. Clusters related to digital transformation, such as #5 “digital transformation” and #6 “digital empowerment”, are also evident. These clusters contain keywords like “big data” [8], “automation” [20], and “internet of things” [21], suggesting a growing trend towards utilizing digital technologies to enhance the construction industry’s efficiency and capabilities [22]. Additionally, clusters like #7 “blockchain” [23] and #10 “machine learning” [24] point to the exploration of advanced technologies in construction. The weighted mean silhouette S of 0.9356 indicate a well-structured clustering result, with high internal cohesion within each cluster and clear distinctions between different clusters (Table 4).

2.2.2. Keyword Time Zone Analysis

The keyword time zone graph (Figure 3) provides a temporal perspective on the evolution of research topics in the construction industry. The horizontal axis represents the years from 2014 to 2024, and the vertical axis categorizes different keyword clusters.
Early-stage research, around 2014–2018, was dominated by fundamental concepts, such as “building information modeling” and “construction management”. As time progressed, new clusters emerged, reflecting the industry’s adaptation to technological advancements. For example, the cluster of “digital transformation” started to gain momentum around 2019–2020, with an increasing number of related keywords, such as “big data” and “information technology”, being added over the years. The appearance of keywords, like “blockchain” and “machine learning”, in more recent years (2022–2024) demonstrates the industry’s exploration of cutting-edge technologies. The connections between different time periods and clusters show the continuous development and cross-fertilization of ideas. For instance, the concepts of “building information modeling” have been continuously evolving and interacting with new digital and technological trends, as seen by the overlapping and connecting lines between different time zones within related clusters. This indicates that the construction industry research is a dynamic field, constantly integrating new knowledge and technologies over time.

3. Research Overview of Digital Intelligence Empowerment in BLM

According to the visual analysis in the second part, it can be seen that lifecycle assessment plays an important role in the digital intelligence empowerment of buildings. The whole lifecycle of the building includes the planning and design stage, the construction stage, the operation and maintenance stage, and the demolition stage. This paper focuses on how digital intelligence can improve the efficiency, sustainability, and safety of construction projects. In the planning and design, construction, and operation phases, the application of digital intelligence technology can play a significant role in achieving these goals. The frequency of demolition is low in actual projects, and many demolition projects have not systematically collected and sorted out relevant data. For buildings that are not demolished, it is almost impossible to obtain data on the demolition process. Therefore, this text only studies the digital intelligence empowerment of buildings in the planning and design stage, construction stage, and operation and maintenance stage.

3.1. Planning and Design Stage

3.1.1. Application of Big Data and Cloud Computing in Planning

In the stage of construction engineering planning, the integrated application of big data and cloud computing technology provides comprehensive and in-depth information support for planning decisions. Big data [25,26] technology can extract valuable information from massive and complex data, and the data can be available in the construction industry including project schedules and project drawings [27] to support decision making and improve the construction industry performance [28]. Cloud computing [17,29] provides a powerful computing power and an efficient platform for the storage, processing, and analysis of data mining data [30,31]. It can quickly process massive data and realize the real-time analysis and visual display of complex data [32]. In construction engineering planning, cloud computing can support the simulation analysis of building projects in different scenarios [33], such as energy consumption simulation under different climate conditions [34], etc. (Figure 4) Through the cloud computing platform, planners can complete a large number of data calculation and model operation in a short time [35], which greatly improves the efficiency of planning scheme formulation.

3.1.2. Application of Virtual Reality (VR) and Augmented Reality (AR) Technology in the Design Display

VR and AR technology bring a new experience to the architectural design display, enabling customers and design teams to feel the design effect in a more intuitive and immersive way [36]. (Figure 4) In the traditional design display, two-dimensional drawings, renderings, or three-dimensional animation are usually used. Although these methods can convey the basic information of the design, customers cannot really feel the architectural space and environment personally. VR technology creates a virtual three-dimensional architectural environment [37]. After users wear VR equipment, they feel like they are in a real architectural space, and they can freely walk, observe, and experience all details of the building, including construction planning, safety management, etc. [38]. This immersive experience allows customers to have a deeper understanding of the design intention, identify the problems in the design, and provide timely feedback [39].
AR technology superlays the virtual design information in the real scene, providing a more convenient and flexible way for the design display [40]. At the project site, designers can display the architectural design model in the actual site in real time through AR equipment [41], so that customers and relevant personnel can intuitively see the integration effect of the building and the surrounding environment [42], as well as the actual effect after the completion of the building. This helps to fully consider the coordination between the building and the surrounding environment in the design stage and avoid the design problems with the surrounding environment [43].

3.1.3. Integration of BIM and GIS

The integration of the Building Information Model (BIM) and Geographic Information System (GIS) is an important theoretical model and method of construction engineering planning stage. While BIM focuses on the detailed 3D modeling of building components (e.g., geometry, materials, and systems) [44], GIS provides geospatial context, analyzing terrain [45], infrastructure networks, and environmental factors [46]. (Figure 4)
In the planning stage, by integrating BIM, model, and GIS data, planners can intuitively see the relationship between building projects and the surrounding geographical environment [47], such as climate adaptation [48], the combination of buildings and terrain [45], the connection with the surrounding traffic facilities [49], site selection and space planning [50], and the impact on the surrounding landscape [51], etc. Using this fusion model, various simulation analyses [52] can also be conducted to evaluate the impact of the planning scheme on the surrounding environment and to optimize and adjust the scheme accordingly [53].
However, achieving seamless integration faces significant challenges, including data interoperability, coordinate system misalignment, and semantic inconsistencies, which hinder holistic applications. BIM data (typically in IFC format) and GIS data (e.g., CityGML or Shapefile) exhibit structural and semantic disparities. For instance, BIM emphasizes object-level granularity, while GIS prioritizes geospatial relationships. Studies highlight that direct integration without standardized protocols often leads to data loss or misrepresentation [47,53]. Successful integration requires advanced conversion tools to bridge format gaps, such as the IFC-CityGML conversion framework proposed by Şenol and Gökgöz [4], which introduces a semantic mapping algorithm to resolve interoperability challenges in BIM-GIS workflows [4]. To address this, recent frameworks propose middleware solutions and semantic mapping tools to bridge format gaps. For example, Diakite and Zlatanova developed an automated geo-referencing algorithm that aligns BIM models with GIS coordinates by embedding geospatial metadata into IFC files, reducing manual calibration errors by 40% [47]. Similarly, Forcael et al. emphasized the role of open-source platforms, like FME (Feature Manipulation Engine), in enabling bidirectional data conversion, though noting residual challenges in handling dynamic property updates [7].
Furthermore, these middleware solutions face significant practical challenges in global scalability due to region-specific adaptations and a lack of standardized protocols. In the EU, projects often align with INSPIRE directives for geospatial interoperability, while Chinese standards, like GB/T 35645-2017 [54], prioritize localized coordinate systems and data structures. Such disparities require middleware to undergo extensive reconfiguration for cross-regional applications, increasing development costs and delaying project timelines. A case study of the Trans-European Rail Corridor project revealed that reconciling German BIM standards (DIN SPEC 91391 [55]) with Polish GIS protocols (SIPG) required 30% additional effort to resolve semantic mismatches in infrastructure alignment [56]. To address these challenges, future research should prioritize the development of adaptive middleware architectures capable of accommodating regional standards without compromising global interoperability [57]. Initiatives such as the ISO 19650-3:2020 framework for geospatial–BIM alignment provide a promising foundation yet require broader adoption and localized implementation guidelines [58].
Future research should prioritize AI-driven semantic alignment and blockchain-based version control to enhance data consistency. The work of Santos et al. offers a promising Multi-Criteria Decision-Making (MCDM) framework that dynamically weighs BIM-GIS inputs, though its computational demands necessitate further optimization [46].

3.2. Construction Stage

3.2.1. The Internet of Things Realizes the Real-Time Monitoring of the Construction Site

In the construction stage of the construction engineering, the Internet of Things technology has become the key means to realize the real-time monitoring of the construction site [59] (Figure 5). By deploying various sensors on the construction equipment [60], materials, and personnel, such as temperature sensors, pressure sensors, position sensors, etc. [61], the physical entities of the construction site are transformed into perceptible and transmitted data nodes, so as to build a huge Internet of Things perception network in the construction site. These sensors can collect multi-dimensional data, such as the operation status of equipment [62], material inventory information [63], and the location and working status of personnel, and transmit the data to the data center through wireless transmission technology [64] (such as Wi-Fi, Bluetooth, ZigBee, NB-IoT, etc.) for centralized processing and analysis.
In terms of equipment management, the Internet of Things technology can monitor the operation parameters of the construction equipment in real time [65]. Through the analysis of these data, the potential faults of the equipment can be found in time, and the preventive maintenance of the equipment can be realized [66]. Empirical studies indicate that sensor networks reduce equipment downtime by 25% through predictive maintenance, though initial infrastructure costs average USD 15 k/km2(e.g., hardware deployment and network integration) [67]. This cost–benefit dynamic highlights the importance of ROI thresholds for IoT adoption, particularly in large-scale projects where long-term operational savings offset upfront investments. The Internet of Things technology can also track the operation track of the construction equipment in real time to ensure that the equipment operates in accordance with the predetermined construction plan and route and improve the construction efficiency and safety.
In terms of material management, the Internet of Things technology can monitor the inventory quantity, quality status, and usage of materials in real time [68]. By deploying sensors in the material storage area, the material inventory quantity change can be perceived in real time [67,69]. When the inventory quantity is below the set threshold, the system automatically issues a replenishment reminder to ensure the timely supply of materials. The Internet of Things technology can also monitor the quality status of the material in real time [70], such as the real-time detection of the strength and other indicators of the concrete [71], to ensure that the materials used meet the quality requirements.

3.2.2. Robotics and Automation in Intelligent Construction

Intelligent construction technology as an important symbol of the Construction 4.0 era, through the introduction of advanced automation, digitalization, and intelligent technology, to achieve the high precision, high efficiency, and high quality of building construction, for the construction of building engineering has brought revolutionary changes (Figure 5). The application of intelligent construction technology, such as 3D printing technology [11], robot construction technology, and automated construction equipment [19], cannot only improve the construction accuracy and efficiency but also effectively reduce labor costs, reduce human errors in the construction process, and improve the overall quality of construction projects.
Robotics technology is an important development direction of intelligent construction. Construction robots can complete a variety of repetitive and dangerous work tasks at the construction site [72], such as building walls [73], plastering [74,75], welding [76], painting [77], etc. These robots are usually equipped with advanced sensors, control systems, and actuators [70], which can automatically complete work tasks according to preset procedures and instructions and have high construction accuracy and stability. In a high-rise building construction project, a wall building robot is used for wall laying [78]. The robot can accurately place the bricks in place according to the design requirements and adjust the position and verticality of the bricks in real time during the laying process to ensure the quality of the wall laying. Robot construction technology can also be combined with other intelligent construction technologies, such as 3D printing technology [79], to achieve the automatic construction of building structures; it is combined with Internet of Things technology [36] to realize remote monitoring and management of robots. Automated construction equipment is also an important part of intelligent construction technology [18]. The application of automatic concrete mixing station [80] and other equipment has greatly improved the degree of automation in the construction process. The automatic concrete [36,81] mixing station can automatically complete the measurement, mixing, and transportation of raw materials according to the set mix ratio to ensure the stable quality of concrete.

3.3. Operation Stage

Intelligent security and emergency management systems are an important line of defense to ensure the safety of construction operation [82] (Figure 6). In the operation stage of construction engineering, with the help of advanced digital intelligence technology, the intelligent security system can realize all-round and multi-level security monitoring and early warning [83].
Image recognition technology is one of the core technologies of intelligent security system [84]. High-definition cameras are deployed in key positions and image recognition algorithms are used to analyze the video images collected by the cameras in real time [85]. Through face recognition technology, we can quickly and accurately identify the identity of personnel and judge whether the personnel has permission to enter a specific area [86]. In the access control system of office buildings, face recognition technology can automatically identify the identity of employees and realize fast passage, while warning strangers to enter.
Sensor technology also plays an important role in intelligent security systems [87]. Through the deployment of smoke sensors, fire sensors, gas leakage sensors, vibration sensors, and other sensors, the environmental changes and safety risks [88,89] in the building can be perceived in real time. The gas leakage sensor can monitor the concentration of harmful gases in the building in real time, such as carbon monoxide, natural gas, etc. [90]. Once the gas leakage is detected, the alarm will be issued in time, and measures such as ventilation and cutting off the gas source are taken to avoid explosion, poisoning, and other accidents.
In terms of emergency management, the intelligent security system and emergency management system are closely combined with emergency management, realizing the rapid and intelligent emergency response. When a security event occurs, the intelligent security system can quickly transmit the event information to the emergency management center, including the type of the event, occurrence location, severity, and other information. Using Geographic Information Systems (GIS) and positioning technology, the emergency management center can grasp the location information of rescue personnel and equipment in real time, reasonably dispatch resources, and improve the rescue efficiency. Through the coordinated work of intelligent security and emergency management systems, the safety of building operation can be effectively improved, and the safety of personnel life and property and the normal operation of the building can be guaranteed.

4. Case Study of Shanghai Tower in China

Shanghai Tower is an ideal case study for exploring digital intelligence in BLM due to its advanced application of digital technologies across its lifecycle stages. It located in Lujiazui, Shanghai, is one of the world’s tallest buildings at 632 m with 128 floors. Designed by Gensler Architects, it is a symbol of innovation and sustainability, featuring a double-skin façade, sky gardens, and renewable energy systems. The tower integrates commercial, hotel, retail, and cultural spaces, representing a vertical urban model. The tower’s innovative design and advanced technologies have contributed to its high level of sustainability, making it a prime example of green building practices.
This case study exemplified the integration of digital intelligence technologies, such as big data, AI, and the IoT. Moreover, this case highlighted the collaborative potential of digital intelligence technologies across lifecycle stages as illustrated in Figure 7. The integration of these technologies has significantly enhanced the building’s operational efficiency, sustainability, and safety, making it a prime example of how digital intelligence can drive holistic and integrated approaches to BLM [91]. This aligns with the research goals of identifying key technologies, analyzing integration mechanisms, and evaluating their impacts on project efficiency, sustainability, and safety.

4.1. Planning and Design Stage of Shanghai Tower

A GIS was used to analyze the geological conditions, traffic network, wind environment, and other data in Lujiazui area of Shanghai and to evaluate the impact of super tall buildings on the surrounding environment. For example, through wind tunnel simulations, it was found that the spiral molding of the Shanghai Center can reduce the wind load by 24% and reduce the amount of structural materials. In order to achieve the sustainability target setting, combined with LEED green building certification requirements, planning energy-saving technologies, such as double curtain wall ventilation, rainwater recycling system, and photovoltaic power generation, aiming to reduce building energy consumption by 30%.
In addition, a parametric algorithm was used to drive shape optimization. The 120° spiral twist form of the exterior curtain wall was generated by the Rhino + Grasshopper parametric tool to ensure the unique coordinate positioning of 80,000 curved glass units. However, the application of parametric tools was not without challenges. Grasshopper scripts required over 3000 manual adjustments to accommodate manufacturing and installation tolerances of the glass units [57], revealing the labor-intensive nature of parametric modeling in practice, particularly when handling complex geometries and high-precision requirements. In conflict detection, the Navisworks application was used to inspect electromechanical lines and structures for collision and resolved 5000+ potential conflicts ahead of time.
The performance simulation process for Shanghai Tower was meticulously coordinated across multiple engineering disciplines to ensure optimal design outcomes. For structural analysis, the Extended Three-Dimensional Analysis of Building Systems (ETABS) and Structural Analysis Program 2000 (SAP2000 v14.1) were employed to conduct advanced seismic simulations. These simulations were critical in optimizing the layout of the shear walls within the building’s core barrel, enhancing its stability and resilience against seismic forces. In terms of energy efficiency, EnergyPlus 3.1 software was utilized to simulate the ventilation performance of the building’s innovative double curtain wall system. This simulation demonstrated that the design effectively reduced the air conditioning load by 15%, contributing significantly to the building’s energy efficiency and sustainability goals. To facilitate global collaboration, the Autodesk Revit Server was implemented, enabling real-time model synchronization among more than 20 design units worldwide. This integration ensured seamless communication and coordination across the project’s lifecycle, from planning to construction, and played a vital role in achieving the tower’s ambitious design and performance objectives.

4.2. Construction Stage of Shanghai Tower

In the steel structure node, the processing drawings were generated by Tekla modeling, the factory prefabrication accuracy reached ±1 mm, and the on-site assembly efficiency increased by 40%. The size of each glass in the curtain wall unit was directly derived from BIM to the CNC machine tool cutting, with an error of less than 2 mm. At the same time, 4D construction simulation (BIM + progress) was used to bind the construction plan (Project file) to the BIM model to simulate key nodes, such as tower crane climbing and concrete pouring sequence. Through simulation, it was found that the construction rhythm of the core cylinder and the outer steel frame did not match, and the construction period was shortened by 2 months after adjustment.
On-site intelligent construction management utilized IoT device deployment, for example, the installation of tilt sensors and GPS, the real-time monitoring of tower crane displacement, the warning of overload or collision risk. The concrete pumping pressure at 600 m high was fed back to the control system in real time, and the pump speed was dynamically adjusted to avoid pipe blocking. In addition, there was 3D laser scanning and reverse engineering, scanning the built floors every week, generating point cloud model and BIM comparison, and cumulatively correcting more than 200 construction errors, with an accuracy of ±3 mm. The welding robot was used for joint welding of steel structure, and the weld pass rate was increased from 90% to 99.5%.
For resource and schedule optimization, the project utilized a cloud-based material management system combined with wireless radio frequency identification (RFID) tags to track the entire lifecycle of 100,000 tons of steel, from transportation and processing to installation. This technology-driven approach significantly enhanced operational efficiency, resulting in a 30% increase in inventory turnover rates. Additionally, by leveraging big data analytics to predict concrete demand, the project team was able to streamline material delivery schedules, reducing on-site waiting time by 50%. These innovations not only improved resource allocation and workflow coordination but also minimized delays and waste, contributing to a more sustainable and cost-effective construction process.

4.3. Operation Stage of Shanghai Tower

In structural health monitoring (SHM) [92], the sensor network deployment included 1200 strain gauges, accelerometers, inclinometers, and anemometers, with data collected every second at critical sites and every minute at common areas. For example, during Typhoon Lejima, a top wind induced vibration amplitude of 0.5 m was detected, and the amplitude was reduced by 60% through a tuned mass damper (TMD).
Intelligent energy management was a double-layer curtain wall with intelligent ventilation. According to the temperature and humidity sensor data automatically opening and closing the vent, summer could reduce the indoor temperature by 3 °C. As well as photovoltaic power generation optimization, 270 photovoltaic inverters monitored power generation efficiency in real time, and AI predicted light intensity to adjust energy storage strategies.
In addition, the digital twin operation and maintenance platform was used for equipment management, space leasing, and emergency drills. Device management mapped the running status of devices, such as elevators and air conditioners in real time, reducing the response time for fault diagnosis to 10 min. Space leasing used BIM models to integrate tenant information and visually analyze floor vacancy rates and rental income. The emergency drill included fire evacuation simulation combined with a real-time human flow thermal map to optimize escape path planning. Finally, user interaction and value-added services were realized. For example, visitors can quickly find the target floor or shop through mobile AR location. Tenants adjusted the temperature, humidity, and lighting of the office area through the app, and the system automatically optimized after learning user habits.

4.4. Challenges and Generalizability of Digital Intelligence Implementation in Shanghai Tower

While the Shanghai Tower exemplifies the transformative potential of digital intelligence in BLM, its implementation faced significant technological and managerial challenges that warrant critical analysis.

4.4.1. Technological Challenges

The integration of BIM, GIS, and IoT systems required extensive middleware development to resolve semantic and structural discrepancies between platforms (e.g., IFC and CityGML formats) [48,53]. For instance, aligning parametric models from Rhino/Grasshopper with GIS wind simulations demanded 15% additional development time and specialized plugins, increasing initial software costs by 20%. Such interoperability barriers are common in large-scale projects, as noted by Diakite and Zlatanova, who reported similar challenges in European smart city initiatives [48]. Such systemic incompatibilities highlight the need for standardized open-source frameworks to reduce reliance on proprietary tools.
Deploying 1200 sensors for structural health monitoring introduced data latency and bandwidth bottlenecks during peak operational periods. While edge computing mitigated these issues, the infrastructure required an additional USD 2.3 million investment, raising questions about cost-effectiveness for smaller projects. This aligns with Kazmi and Sodang, who identified IoT scalability as a critical barrier in off-site construction [59]. This underscores a paradox; while edge computing resolves latency, its upfront costs risk excluding resource-constrained projects from adopting IoT-driven monitoring, perpetuating reliance on manual methods.

4.4.2. Managerial Challenges

The shift to automated construction, including welding robots and 3D-printed components, disrupted traditional workflows, requiring 40% of the workforce to undergo 120 h of upskilling programs. However, training focused solely on operational skills overlooked the need for adaptive problem solving in dynamic robotic environments, leading to a 3-month delay as workers struggled with unanticipated errors. Resistance to AR/VR tools further compounded delays; subcontractors perceived immersive design validation as redundant, preferring 2D blueprints. Gurgun et al. attribute such resistance to “technology inertia”, where entrenched practices in Turkish megaprojects led to a 22% adoption lag despite proven efficiency gains [18].
The cloud-based material management system faced cybersecurity breaches during RFID tracking, prompting a 30% budget reallocation to blockchain-based encryption. Similar vulnerabilities were reported by Ahmed et al. in IoT-driven logistics systems [65]. For the Shanghai Tower, this incident highlighted the need for proactive cyber-risk assessments in digital procurement strategies, rather than reactive fixes.

4.4.3. Generalizability of Solutions

The applicability of the Shanghai Tower’s BLM framework to other projects is constrained by contextual factors that may not be universally replicable. First, the project’s USD 2.4 billion budget enabled the adoption of cutting-edge technologies, such as digital twins, which are likely prohibitive for cost-sensitive regions with limited financial resources. Second, regulatory support played a pivotal role, as Shanghai’s smart city policies expedited permits for IoT deployments and data-sharing agreements, a level of bureaucratic flexibility that might be absent in jurisdictions with stricter regulatory frameworks. Third, the project benefited from a collaborative ecosystem involving partnerships with 20 global design firms and technology vendors, which collectively provided specialized expertise in fields, such as parametric modeling and sensor integration. However, this model may not be feasible in fragmented markets where stakeholders lack incentives for collaboration or where proprietary data-sharing barriers persist. Additionally, while the project’s success demonstrated the potential of integrating BIM, GIS, and IoT, the middleware development required to bridge semantic and structural discrepancies between platforms (such as IFC and CityGML formats) may represent a significant barrier for projects without comparable capital or technical capacity. These contextual limitations highlight that while the Shanghai Tower exemplifies innovative BLM practices, its solutions may require contextual adaptation to align with the financial, regulatory, and collaborative realities of other projects.

5. Discussions and Conclusions

5.1. Research Contributions

This study deeply analyzes the theory and practice of digital intelligence empowerment in the whole lifecycle of Construction projects under the background of Construction 4.0.
Through systematic theoretical research and the typical case analysis, a series of valuable research results have been obtained. In the aspect of theoretical research, the key technologies and application models of digital intelligence empowerment in each stage of the whole lifecycle of construction engineering are comprehensively sorted out. In the planning stage, big data and cloud computing provide comprehensive information support and powerful computing capabilities for planning decisions, artificial intelligence assists planning decisions to realize the generation and optimization of multiple schemes, and the combination of BIM and GIS provides a comprehensive visualization and simulation analysis means for planning. In the design stage, BIM technology-led design collaboration breaks the information barrier under the traditional design mode and realizes multi-professional and efficient collaboration. In the construction stage, the Internet of Things realizes the real-time monitoring of the construction site, big data analysis optimizes the construction management process, and intelligent construction technology improves the construction accuracy and efficiency and provides a strong guarantee for the intelligent and fine management of the construction process. In the operation stage, the intelligent management of construction equipment realizes the intelligent operation and fault prediction of equipment and achieves the goal of energy conservation and emission reduction. The intelligent security and emergency management system ensures the safety of building operations and improves the overall operation level of the building.
Through the in-depth analysis of the case of Shanghai Tower in China the practical application effect and great value of digital intelligence empowerment in the whole lifecycle of construction projects are verified. Shanghai Tower makes full use of digital intelligence technology in the whole lifecycle and has achieved remarkable results in terms of construction period, cost, and quality. In terms of construction period, the application of digital intelligence technology shortens the construction period of the project and completes the delivery in advance. In terms of cost, through accurate planning, collaborative design, efficient construction, and intelligent operation management, the cost is effectively reduced. In terms of quality, intelligent construction technology and real-time monitoring ensure the high-quality construction and stable operation of the building.
These theoretical studies and case studies provide comprehensive theoretical support and practical experience for the transformation of digital intelligence in the construction industry and help construction enterprises and relevant practitioners to better understand and apply digital intelligence technology and promote the development of the construction industry in the direction of digitalization, intelligence, and sustainability.

5.2. Managerial Insights

From the perspective of management, digital intelligence empowerment has a profound impact on the whole lifecycle management of construction projects and promotes the reform of management concepts and models. Traditional construction project management mode often relies on manual experience and paper documents, information transmission lags behind, collaborative efficiency is low, and it is difficult to cope with complex and changeable project needs. In the Construction 4.0 era, the wide application of digital intelligence technology has brought new ideas and methods for construction project management. To implement lifecycle management in Construction 4.0, research must align with EU CEN TC 350 standards [93], enabling systematic and comparable assessments across project phases through the following five steps: First, define goals and scope using EN 15978:2011 [94] to establish system boundaries, functional units, and environmental indicators (e.g., carbon footprint, energy use), while leveraging BIM-GIS integration for data granularity [4]. Second, automate inventory analysis with IoT sensors for material tracking [63], equipment monitoring [65], and blockchain for supply chain transparency [23], using cloud computing for real-time data aggregation [30]. Third, assess impacts using AI tools, like machine learning models [24], benchmarked against EN 15804:2012 [95], and employ digital twins for dynamic simulations. Fourth, optimize decisions with Multi-Criteria Decision-Making (MCDM) frameworks [46] to balance cost, sustainability, and safety. Finally, establish reporting and benchmarking standards using open-source databases for industry comparability. The findings of the research Construction 4.0’s need for real-time collaboration by embedding interoperability standards such as IFC-CityGML conversion [4] and automating compliance checks. Examples include the Shanghai Tower’s cloud-based material management system (Step 2) and its digital twin for energy optimization (Step 3).
Digital intelligence also promotes the refinement and intelligence of construction project management. Through big data analysis and AI technology [74], key indicators such as cost, quality, and safety of the project can be monitored and analyzed in real time to achieve accurate management. In terms of cost management, using big data analysis technology to compare and analyze the historical project cost data and the current project cost data, we can find the risk points of cost overruns in time and take corresponding measures to control them [96]. Through the analysis of material price fluctuations, labor cost changes, and other factors, the change trend of project cost is predicted, and the decision basis for cost control is provided [97]. In terms of quality and safety management, the use of the Internet of Things and AI technology for the real-time monitoring of the construction site can detect quality problems and safety hazards in time and automatically issue early warnings [98]. In a high-rise building construction project, through the deployment of intelligent cameras and sensors on the construction site, the use of AI image recognition technology monitors the construction personnel’s helmet wearing situation and the setting of safety protection facilities in real time; once violations are found, an alarm is immediately issued to notify the relevant personnel to rectify, effectively improving the safety management level of the construction site [99].
Digital intelligence empowerment also promotes the scientific decision making of construction project management. To operationalize these insights, we propose the Digital Intelligence Readiness Assessment Framework (DIRAF) for BLM, designed to help managers systematically assess their organization’s preparedness and prioritize actionable steps. DIRAF encompasses six critical dimensions (Figure 8), each supported by evaluation criteria and a scoring mechanism (1–5 scale: 1 = minimal readiness, 5 = advanced readiness). While DIRAF provides a foundational snapshot of readiness, its practical utility hinges on dynamic adaptability to evolving project conditions. To address this, the framework has been augmented with the following three iterative mechanisms: (1) Real-time data integration: DIRAF now interfaces with project management tools, such as BIM platforms and IoT, to ingest real-time data streams, sensor outputs, and workforce training logs. For example, IoT-generated equipment utilization rates dynamically update the technology infrastructure score, while AI-driven skills gap analyses adjust the workforce competency metric. (2) Adaptive weighting algorithms: DIRAF employs machine learning models to recalibrate dimension weights based on project phase and external factors. During the planning stage, interoperability standards may carry higher weight (30%) due to BIM-GIS integration demands; whereas, construction phase priorities shift toward cybersecurity (35%) to mitigate IoT breach risks. The algorithm uses historical project data and market trends to optimize weight distributions. (3) Feedback-driven reassessment loops: DIRAF mandates quarterly reassessments, triggered automatically by milestone completions or risk thresholds.

5.3. Future Research and Limitations

Looking forward to the future, with the continuous exploration of Construction 5.0 and the continuous progress of science and technology and the growing demand for digitalization and intelligence in the construction industry, the digital intelligence empowerment of construction engineering will show a more diversified and in-depth development trend.
While this study highlights the transformative potential of digital intelligence in BLM, several research gaps emerged from the systematic analysis of 206 articles. Based on the reviewed literature and the empirical insights from the Shanghai Tower case study, the following three areas of digital intelligence application require urgent empirical testing or longitudinal studies to bridge theory and practice.

5.3.1. Integration of Blockchain for Transparent and Secure Data Sharing

Blockchain technology was frequently cited for its potential to enhance trust and transparency in multi-stakeholder collaborations (e.g., supply chain traceability, contract management) [100]. However, only 12 articles (Cluster #7, Table 3) provided empirical evidence of its implementation in BLM, primarily focusing on pilot-scale projects (e.g., material provenance tracking in single buildings) rather than large-scale, cross-project applications. This scarcity of empirical validation underscores critical barriers to broader adoption like technology immaturity, interdisciplinary complexity, regulatory and standardization gaps, and stakeholder resistance [101,102]. While blockchain offers cross-organizational trust, high-security needs and cloud-based ledgers [103] excel in single-organization, high-frequency operations (Table 5). To bridge these gaps, a five-phase research roadmap is proposed [5] (Figure 9). Future frameworks should hybridize these technologies, like using blockchain for critical legal/financial records and cloud systems for high-frequency operational data to optimize performance and trust.

5.3.2. Human–Robot Collaboration (HRC) in Construction Automation

While robotics and automation were widely discussed (e.g., 3D printing, welding robots), only 9% of articles addressed HRC challenges, such as safety protocols and workforce adaptation (Cluster #10, Table 3). It requires field experiments to assess HRC workflows in dynamic environments (e.g., high-rise construction). Similarly, longitudinal studies on workforce reskilling and productivity impacts are also required to measure error rates and worker acceptance in similar contexts.

5.3.3. Lifecycle Sustainability Assessment via Digital Twins

Digital twins were identified as critical for real-time monitoring and predictive maintenance (Cluster #0, Table 3). However, only 15% of studies quantitatively linked digital twins to lifecycle sustainability outcomes (e.g., carbon reduction, energy optimization). Shanghai Tower’s digital twin focused on operational efficiency but lacked longitudinal carbon tracking. Future work could integrate lifecycle assessment (LCA) tools to quantify environmental ROI.

5.3.4. Human–Robot Collaboration Frameworks for Construction 5.0

The emergence of Construction 5.0 underscores the necessity of symbiotic human–robot collaboration (HRC) frameworks to optimize both productivity and workforce well-being. Marinelli highlights that future research should prioritize developing adaptive systems where human expertise and robotic precision complement each other, particularly in dynamic construction environments [12]. Empirical studies are needed to assess the socio-technical integration of these frameworks, including worker acceptance, safety protocols, and skill development programs to facilitate seamless human–robot interactions. For instance, longitudinal studies could explore how HRC workflows mitigate ergonomic risks, while enhancing precision in tasks like robotic welding or 3D printing. Such investigations will be critical to addressing the workforce implications of digital intelligence adoption, ensuring equitable transitions in labor practices, and aligning with Construction 5.0′s vision of human-centric technological advancement.

5.4. Policy Recommendations

To translate theoretical advancements into actionable governance frameworks, policymakers should prioritize institutionalizing digital intelligence practices in public infrastructure. A critical step is for municipalities to mandate Digital Twin handovers [5] as part of project delivery requirements. This policy would enforce BLM data continuity across planning, construction, and operational phases, ensuring interoperability between stakeholders (e.g., contractors, operators, regulators) and safeguarding asset lifecycle transparency. Such mandates could be further reinforced by blockchain-based audit trails [6] to prevent data fragmentation in public–private partnerships. By embedding digital twin governance into procurement standards, cities can accelerate the transition toward data-driven urban resilience under Construction 4.0 paradigms.

Author Contributions

Conceptualization, J.L. and H.-Y.C.; methodology, J.L.; software, R.W.; validation, R.W.; formal analysis, J.L.; investigation, J.L.; resources, J.L.; data curation, J.L.; writing—original draft preparation, R.W.; writing—review and editing, H.-Y.C.; supervision, H.-Y.C.; project administration, H.-Y.C.; writing—review and editing, X.L.; supervision, X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Jiangsu Provincial Department of Education Fund of Philosophy and Social Science (grant no. 2023SJYB0338), Jiangsu Provincial of Education Science Planning Project Funding (C/2023/01/30).

Data Availability Statement

No new data were created or analyzed in this study.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Berger, R. Roland Berger Digitization in the Construction Industry: Building Europe’s Road to “Construction 4.0”; Roland Berger GMBH: Munich, Germany, 2016. [Google Scholar]
  2. Corallo, A.; Lazoi, M.; Lezzi, M.; Luperto, A. Cybersecurity awareness in the context of the Industrial Internet of Things: A systematic literature review. Comput. Ind. 2022, 137, 103614. [Google Scholar] [CrossRef]
  3. Kaewunruen, S.; O’Nell, C.; Sengsri, P. Digital twin-driven strategic demolition plan for circular asset management of bridge infrastructures. Sci. Rep. 2025, 15, 10554. [Google Scholar]
  4. Şenol, H.İ.; Gökgöz, T. Integration of Building Information Modeling (BIM) and Geographic Information System (GIS): A new approach for IFC to CityJSON conversion. Earth Sci. Inform. 2024, 17, 3437–3454. [Google Scholar] [CrossRef]
  5. Adu-Amankwa, N.A.N.; Rahimian, F.P.; Dawood, N.; Park, C. Digital Twins and Blockchain technologies for building lifecycle management. Autom. Constr. 2023, 155, 105064. [Google Scholar] [CrossRef]
  6. Alhadi, A.; Tom, B.D.; Yacine, R. Enhancing asset management: Integrating digital twins for continuous permitting and compliance-A systematic literature review. J. Build. Eng. 2025, 99, 111515. [Google Scholar] [CrossRef]
  7. Forcael, E.; Ferrari, I.; Opazo-Vega, A.; Pulido-Arcas, J.A. Construction 4.0: A literature review. Sustainability 2020, 12, 9755. [Google Scholar] [CrossRef]
  8. Chen, Y.L.; Huang, D.; Liu, Z.; Osmani, M.; Demian, P. Construction 4.0, Industry 4.0, and Building Information Modeling (BIM) for Sustainable Building Development within the Smart City. Sustainability 2022, 14, 10028. [Google Scholar] [CrossRef]
  9. Jrade, A.; Jalaei, F.; Zhang, J.J.; Eirdmousa, S.J.; Jalaei, F. Potential Integration of Bridge Information Modeling and Life Cycle Assessment/Life Cycle Costing Tools for Infrastructure Projects within Construction 4.0: A Review. Sustainability 2023, 15, 15049. [Google Scholar] [CrossRef]
  10. Cerdá-Suárez, L.M.; Espinosa-Cristia, J.F.; Núñez-Valdés, K.; Núñez-Valdés, G. Detecting Circular Economy Strategies in the Fourth Sector: Overview of the Chilean Construction Sector as Evidence of a Sustainable Business Model. Sustainability 2023, 15, 8559. [Google Scholar] [CrossRef]
  11. Mesa, J.A.; Fúquene-Retamoso, C.; Maury-Ramírez, A. Life Cycle Assessment on Construction and Demolition Waste: A Systematic Literature Review. Sustainability 2021, 13, 7676. [Google Scholar] [CrossRef]
  12. Marinelli, M. From Industry 4.0 to Construction 5.0: Exploring the Path towards Human-Robot Collaboration in Construction. Systems 2023, 11, 152. [Google Scholar] [CrossRef]
  13. Dallasega, P.; Rauch, E.; Linder, C. Industry 4.0 as an enabler of proximity for construction supply chains: A systematic literature review. Comput. Ind. 2018, 99, 205–225. [Google Scholar] [CrossRef]
  14. Papadonikolaki, E.; van Oel, C.; Kagioglou, M. Organising and Managing boundaries: A structurational view of collaboration with Building Information Modelling (BIM). Int. J. Proj. Manag. 2019, 37, 378–394. [Google Scholar] [CrossRef]
  15. Li, Z.; He, Y.K.; Lu, X.Y.; Zhao, H.Y.; Zhou, Z.; Cao, Y.Y. Construction of Smart City Street Landscape Big Data-Driven Intelligent System Based on Industry 4.0. Comput. Intell. Neurosci. 2021, 2021, 1716396. [Google Scholar] [CrossRef]
  16. Malomane, R.; Musonda, I.; Okoro, C.S. The Opportunities and Challenges Associated with the Implementation of Fourth Industrial Revolution Technologies to Manage Health and Safety. Int. J. Environ. Res. Public Health 2022, 19, 846. [Google Scholar] [CrossRef]
  17. Lekan, A.; Clinton, A.; Owolabi, J. The Disruptive Adaptations of Construction 4.0 and Industry 4.0 as a Pathway to a Sustainable Innovation and Inclusive Industrial Technological Development. Buildings 2021, 11, 79. [Google Scholar] [CrossRef]
  18. Gurgun, A.P.; Koc, K.; Kunkcu, H. Exploring the adoption of technology against delays in construction projects. Eng. Constr. Archit. Manag. 2024, 31, 1222–1253. [Google Scholar] [CrossRef]
  19. Muñoz-La Rivera, F.; Mora-Serrano, J.; Valero, I.; Oñate, E. Methodological-Technological Framework for Construction 4.0. Arch. Comput. Methods Eng. 2021, 28, 689–711. [Google Scholar] [CrossRef]
  20. Musarat, M.A.; Alaloul, W.S.; Irfan, M.; Sreenivasan, P.; Rabbani, M.B.A. Health and Safety Improvement through Industrial Revolution 4.0: Malaysian Construction Industry Case. Sustainability 2023, 15, 201. [Google Scholar] [CrossRef]
  21. Allam, A.S.; Nik-Bakht, M. Supporting circularity in construction with performance-based deconstruction. Sustain. Prod. Consum. 2024, 45, 1–14. [Google Scholar] [CrossRef]
  22. Wang, K.Y.; Guo, F.Y.; Zhang, C.; Schaefer, D. From Industry 4.0 to Construction 4.0: Barriers to the digital transformation of engineering and construction sectors. Eng. Constr. Archit. Manag. 2024, 31, 136–158. [Google Scholar] [CrossRef]
  23. Weerapperuma, U.S.; Rathnasinghe, A.P.; Jayasena, H.S.; Wijewickrama, C.S.; Thurairajah, N. A knowledge framework for blockchain-enabled smart contract adoption in the construction industry. Eng. Constr. Archit. Manag. 2025, 32, 374–408. [Google Scholar] [CrossRef]
  24. Pourrahimian, E.; Eltahan, A.; Salhab, D.; Crawford, J.; Abourizk, S.; Hamzeh, F. Integrating expert insights and data analytics for enhanced construction productivity monitoring and control: A machine learning approach. Eng. Constr. Archit. Manag. 2024; ahead-of-print. [Google Scholar]
  25. Ji, S.; Lee, B.; Yi, M.Y. Building life-span prediction for life cycle assessment and life cycle cost using machine learning: A big data approach. Build. Environ. 2021, 205, 108267. [Google Scholar] [CrossRef]
  26. Ngo, J.; Hwang, B.G.; Zhang, C.Y. Factor-based big data and predictive analytics capability assessment tool for the construction industry. Autom. Constr. 2020, 110, 103042. [Google Scholar] [CrossRef]
  27. Yu, T.; Liang, X.; Wang, Y.W. Factors Affecting the Utilization of Big Data in Construction Projects. J. Constr. Eng. Manag. 2020, 146, 04020032. [Google Scholar] [CrossRef]
  28. Lu, Y.S.; Zhang, J.T. Bibliometric analysis and critical review of the research on big data in the construction industry. Eng. Constr. Archit. Manag. 2022, 29, 3574–3592. [Google Scholar] [CrossRef]
  29. You, Z.J.; Feng, L.J. Integration of Industry 4.0 Related Technologies in Construction Industry: A Framework of Cyber-Physical System. IEEE Access 2020, 8, 122908–122922. [Google Scholar] [CrossRef]
  30. Bello, S.A.; Oyedele, L.O.; Akinade, O.O.; Bilal, M.; Delgado, J.M.D.; Akanbi, L.A.; Ajayi, A.O.; Owolabi, H.A. Cloud computing in construction industry: Use cases, benefits and challenges. Autom. Constr. 2021, 122, 103441. [Google Scholar] [CrossRef]
  31. Morawiec, P.; Soltysik-Piorunkiewicz, A. Cloud Computing, Big Data, and Blockchain Technology Adoption in ERP Implementation Methodology. Sustainability 2022, 14, 3714. [Google Scholar] [CrossRef]
  32. Eirinaki, M.; Dhar, S.; Mathur, S.; Kaley, A.; Patel, A.; Joshi, A.; Shah, D. A building permit system for smart cities: A cloud-based framework. Comput. Environ. Urban Syst. 2018, 70, 175–188. [Google Scholar] [CrossRef]
  33. Sadeq, H.; Sayed, T. Automated Roundabout Safety Analysis: Diagnosis and Remedy of Safety Problems. J. Transp. Eng. 2016, 142, 04016062. [Google Scholar] [CrossRef]
  34. Wang, Y.; Zhu, C.A. Thoughts on the construction of public health informatization for community health archives grass roots management system. Front. Public Health 2023, 11, 1160478. [Google Scholar] [CrossRef]
  35. Micieta, B.; Staszewska, J.; Kovalsky, M.; Krajcovic, M.; Binasova, V.; Papanek, L.; Antoniuk, I. Innovative System for Scheduling Production Using a Combination of Parametric Simulation Models. Sustainability 2021, 13, 9518. [Google Scholar] [CrossRef]
  36. Tecchio, P.; McAlister, C.; Mathieux, F.; Ardente, F. In search of standards to support circularity in product policies: A systematic approach. J. Clean. Prod. 2017, 168, 1533–1546. [Google Scholar] [CrossRef] [PubMed]
  37. Noghabaei, M.; Han, K. Object manipulation in immersive virtual environments: Hand Motion tracking technology and snap-to-fit function. Autom. Constr. 2021, 124, 103594. [Google Scholar] [CrossRef]
  38. Getuli, V.; Capone, P.; Bruttini, A.; Isaac, S. BIM-based immersive Virtual Reality for construction workspace planning: A safety-oriented approach. Autom. Constr. 2020, 114, 103160. [Google Scholar] [CrossRef]
  39. Gunduz, M.; Laitinen, H. A 10-step safety management framework for construction small and medium-sized enterprises. Int. J. Occup. Saf. Ergon. 2017, 23, 353–359. [Google Scholar] [CrossRef]
  40. Zhao, G.; Liu, S.; Zhu, W.J.; Qi, Y.H. A Lightweight Mobile Outdoor Augmented Reality Method Using Deep Learning and Knowledge Modeling for Scene Perception to Improve Learning Experience. Int. J. Hum.-Comput. Interact. 2021, 37, 884–901. [Google Scholar] [CrossRef]
  41. Guo, Y.J.; Li, S.; Zhang, X.D.; Fu, Y.Y.; Yuan, Y.M.; Liu, Y.Q. Embracing the Metaverse: A Survey of Virtual Reality and Augmented Reality Practices at the United States’ Top One Hundred University Libraries. Coll. Res. Libr. 2024, 85, 1006. [Google Scholar] [CrossRef]
  42. Hincapie, M.; Diaz, C.; Valencia, A.; Contero, M.; Güemes-Castorena, D. Educational applications of augmented reality: A bibliometric study. Comput. Electr. Eng. 2021, 93, 107289. [Google Scholar] [CrossRef]
  43. Yeh, H.C.; Tseng, S.S. Enhancing multimodal literacy using augmented reality. Lang. Learn. Technol. 2020, 24, 27–37. [Google Scholar]
  44. Mzyece, D.; Ndekugri, I.E.; Ankrah, N.A. Building information modelling (BIM) and the CDM regulations interoperability framework. Eng. Constr. Archit. Manag. 2019, 26, 2682–2704. [Google Scholar] [CrossRef]
  45. Wang, Z.Y. Spatial Differentiation Characteristics of Rural Areas Based on Machine Learning and GIS Statistical Analysis-A Case Study of Yongtai County, Fuzhou City. Sustainability 2023, 15, 4367. [Google Scholar] [CrossRef]
  46. Rall, E.; Hansen, R.; Pauleit, S. The added value of public participation GIS (PPGIS) for urban green infrastructure planning. Urban For. Urban Green. 2019, 40, 264–274. [Google Scholar] [CrossRef]
  47. Santos, P.A.S.; Cortez, B.; Carvalho, M.T.M. Integrating GIS and BIM with MCDM for infrastructure planning: A comprehensive framework. Eng. Constr. Archit. Manag. 2024; ahead-of-print. [Google Scholar]
  48. Diakite, A.A.; Zlatanova, S. Automatic geo-referencing of BIM in GIS environments using building footprints. Comput. Environ. Urban Syst. 2020, 80, 101453. [Google Scholar] [CrossRef]
  49. Campbell, A.; Both, A.; Sun, Q. Detecting and mapping traffic signs from Google Street View images using deep learning and GIS. Computers, Environ. Urban Syst. 2019, 77, 101350. [Google Scholar] [CrossRef]
  50. Antwi-Afari, M.F.; Li, H.; Pärn, E.A.; Ewards, D.J. Critical success factors for implementing building information modelling (BIM): A longitudinal review. Autom. Constr. 2018, 91, 100–110. [Google Scholar] [CrossRef]
  51. Gbanie, S.P.; Griffin, A.L.; Thornton, A. Impacts on the Urban Environment: Land Cover Change Trajectories and Landscape Fragmentation in Post-War Western Area, Sierra Leone. Remote Sens. 2018, 10, 129. [Google Scholar] [CrossRef]
  52. Lou, K.; Hu, Z.Q.; Zhang, H.W.; Li, Q.Y.; Ji, X.F. Information Storage Based on Stimuli-Responsive Fluorescent 3D Code Materials. Adv. Funct. Mater. 2022, 32, 2113274. [Google Scholar] [CrossRef]
  53. Olanrewaju, O.I.; Kineber, A.F.; Chileshe, N.; Edwards, D.J. Modelling the relationship between Building Information Modelling (BIM) implementation barriers, usage and awareness on building project lifecycle. Build. Environ. 2022, 207, 108556. [Google Scholar] [CrossRef]
  54. GB/T 35645-2017; Navigation Electronic Map Framework Data Exchange Format. Standardization Administration of the People’s Republic of China: Beijing, China, 2017.
  55. DIN SPEC 91391; Requirements for Safety-Related Mechatronic Systems in Machinery. Deutsches Institut für Normung e.V.: Berlin, Germany, 2018.
  56. Cepa, J.J.; Alberti, M.G.; Pavón, R.M.; Calvo, J.A. Integrating BIM and GIS for an Existing Infrastructure. Appl. Sci. 2024, 14, 10962. [Google Scholar] [CrossRef]
  57. Santos, T.A.; Fonseca, M.A.; Martins, P.; Soares, C.G. Integrating short sea shipping with trans-european transport networks. J. Mar. Sci. Eng. 2022, 10, 218. [Google Scholar] [CrossRef]
  58. Abanda, F.H.; Balu, B.; Adukpo, S.E.; Akintola, A. Decoding ISO 19650 Through Process Modelling for Information Management and Stakeholder Communication in BIM. Buildings 2025, 15, 431. [Google Scholar] [CrossRef]
  59. Kazmi, Z.A.; Sodangi, M. Modeling the Constraints to the Utilization of the Internet of Things in Managing Supply Chains of Off-Site Construction: An Approach toward Sustainable Construction. Buildings 2022, 12, 388. [Google Scholar] [CrossRef]
  60. Tu, M.R. An exploratory study of Internet of Things (IoT) adoption intention in logistics and supply chain management: A mixed research approach. Int. J. Logist. Manag. 2018, 29, 131–151. [Google Scholar] [CrossRef]
  61. Khurshid, K.; Danish, A.; Salim, M.U.; Bayram, M.; Ozbakkaloglu, T.; Mosaberpanah, M.A. An In-Depth Survey Demystifying the Internet of Things (IoT) in the Construction Industry: Unfolding New Dimensions. Sustainability 2023, 15, 1275. [Google Scholar] [CrossRef]
  62. Chen, Y.T.; Sun, E.W.; Chang, M.F.; Lin, Y.B. Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0. Int. J. Prod. Econ. 2021, 238, 108157. [Google Scholar] [CrossRef]
  63. McCartney, M. The China-Pakistan Economic Corridor (CPEC): Infrastructure, Social Savings, Spillovers, and Economic Growth in Pakistan. Eurasian Geogr. Econ. 2022, 63, 180–211. [Google Scholar] [CrossRef]
  64. Scuotto, V.; Ferraris, A.; Bresciani, S. Internet of Things Applications and challenges in smart cities: A case study of IBM smart city projects. Bus. Process Manag. J. 2016, 22, 357–367. [Google Scholar] [CrossRef]
  65. Ahmed, E.; Yaqoob, I.; Hashem, I.A.T.; Khan, I.; Ahmed, A.I.A.; Imran, M.; Vasilakos, A.V. The role of big data analytics in Internet of Things. Comput. Netw. 2017, 129, 459–471. [Google Scholar] [CrossRef]
  66. Zhang, H.; Yan, X.Z.; Li, H.; Jin, R.; Fu, H.F. Real-Time Alarming, Monitoring, and Locating for Non-Hard-Hat Use in Construction. J. Constr. Eng. Manag. 2019, 145, 04019006. [Google Scholar] [CrossRef]
  67. Edirisinghe, R. Digital skin of the construction site Smart sensor technologies towards the future smart construction site. Eng. Constr. Archit. Manag. 2019, 26, 184–223. [Google Scholar] [CrossRef]
  68. Arashpour, M.; Kamat, V.; Bai, Y.; Wakefield, R.; Abbasi, B. Optimization modeling of multi-skilled resources in prefabrication: Theorizing cost analysis of process integration in off-site construction. Autom. Constr. 2018, 95, 1–9. [Google Scholar] [CrossRef]
  69. Xu, G.Y.; Huang, G.Q.; Fang, J.; Chen, J. Cloud-based smart asset management for urban flood control. Enterp. Inf. Syst. 2017, 11, 719–737. [Google Scholar] [CrossRef]
  70. Li, C.Z.; Hong, J.; Xue, F.; Shen, G.Q.; Xu, X.X.; Luo, L.Z. SWOT analysis and Internet of Things-enabled platform for prefabrication housing production in Hong Kong. Habitat Int. 2016, 57, 74–87. [Google Scholar] [CrossRef]
  71. Luo, L.Z.; Hu, S.Y.; Chen, K.; Liu, Y.L.; Li, C.Z. Exploring Safety Vulnerability in Prefabricated Construction and Mitigation Effects of Internet of Things. IEEE Trans. Eng. Manag. 2024, 71, 8531–8547. [Google Scholar] [CrossRef]
  72. Dindorf, R.; Wos, P. Challenges of Robotic Technology in Sustainable Construction Practice. Sustainability 2024, 16, 5500. [Google Scholar] [CrossRef]
  73. You, S.; Kim, J.H.; Lee, S.; Kamat, V.; Robert, L.P. Enhancing perceived safety in human-robot collaborative construction using immersive virtual environments. Autom. Constr. 2018, 96, 161–170. [Google Scholar] [CrossRef]
  74. Khosravani, M.R.; Haghighi, A. Large-Scale Automated Additive Construction: Overview, Robotic Solutions, Sustainability, and Future Prospect. Sustainability 2022, 14, 9782. [Google Scholar] [CrossRef]
  75. Mitterberger, D.; Ercan Jenny, S.; Vasey, L.; Lloret-Fritschi, E.; Aejmelaeus-Lindström, P.; Gramazio, F.; Kohler, M. Interactive robotic plastering: Augmented interactive design and fabrication for on-site robotic plastering. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 30 April–5 May 2020; pp. 1–18. [Google Scholar]
  76. Delgado, J.M.D.; Oyedele, L.; Ajayi, A.; Akanbi, L.; Akinade, O.; Bilal, M.; Owolabi, H. Robotics and automated systems in construction: Understanding industry-specific challenges for adoption. J. Build. Eng. 2019, 26, 100868. [Google Scholar] [CrossRef]
  77. Yeom, D.J.; Na, E.J.; Lee, M.Y.; Kim, Y.J.; Kim, Y.S.; Cho, C.S. Performance Evaluation and Life Cycle Cost Analysis Model of a Gondola-Type Exterior Wall Painting Robot. Sustainability 2017, 9, 1809. [Google Scholar] [CrossRef]
  78. Ding, L.; Jiang, W.; Zhou, Y.; Zhou, C.; Liu, S. BIM-based task-level planning for robotic brick assembly through image-based 3D modeling. Adv. Eng. Inform. 2020, 43, 100993. [Google Scholar] [CrossRef]
  79. Brissi, S.G.; Chong, O.W.; Debs, L.; Zhang, J.S. A review on the interactions of robotic systems and lean principles in offsite construction. Eng. Constr. Archit. Manag. 2022, 29, 383–406. [Google Scholar] [CrossRef]
  80. Zayed, T.M.; Halpin, D.W.; Basha, I.M. Productivity and delays assessment for concrete batch plant-truck mixer operations. Constr. Manag. Econ. 2005, 23, 839–850. [Google Scholar] [CrossRef]
  81. Cho, K.; Ahn, S.; Park, K.; Kim, T.W. Schedule delay leading indicators in precast concrete construction projects: Qualitative comparative analysis of Korean cases. J. Manag. Eng. 2021, 37, 04021024. [Google Scholar] [CrossRef]
  82. Hao, Z.B.; Wang, Y.Z.; Yang, X.G. Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles. Sustainability 2024, 16, 2917. [Google Scholar] [CrossRef]
  83. Gomes Rossi, A.H.; Marcondes, G.B.; Pontes, J.; Leitao, P.; Treinta, F.T.; Martins De Resende, L.M.; Mosconi, E.; Yoshino, R.T. Lean Tools in the Context of Industry 4.0: Literature Review, Implementation and Trends. Sustainability 2022, 14, 12295. [Google Scholar] [CrossRef]
  84. Khan, S.; Panuwatwanich, K.; Usanavasin, S. Integrating building information modeling with augmented reality: Application and empirical assessment in building facility management. Eng. Constr. Archit. Manag. 2024, 31, 2809–2828. [Google Scholar] [CrossRef]
  85. Guo, H.L.; Yu, Y.T.; Ding, Q.H.; Skitmore, M. Image-and-Skeleton-Based Parameterized Approach to Real-Time Identification of Construction Workers’ Unsafe Behaviors. J. Constr. Eng. Manag. 2018, 144, 04018042. [Google Scholar] [CrossRef]
  86. Lee, Y.; Choi, S.I. Training Set Enlargement Using Binary Weighted Interpolation Maps for the Single Sample per Person Problem in Face Recognition. Appl. Sci. 2020, 10, 6659. [Google Scholar] [CrossRef]
  87. Lee, W.; Lin, K.Y.; Seto, E.; Migliaccio, G.C. Wearable sensors for monitoring on-duty and off-duty worker physiological status and activities in construction. Autom. Constr. 2017, 83, 341–353. [Google Scholar] [CrossRef]
  88. Wu, C.K.; Chen, C.J.; Jiang, R.; Wu, P.; Xu, B.; Wang, J. Understanding laborers’ behavioral diversities in multinational construction projects using integrated simulation approach. Eng. Constr. Archit. Manag. 2019, 26, 2120–2146. [Google Scholar] [CrossRef]
  89. Sorensen, G.; McLellan, D.L.; Sabbath, E.L.; Dennerlein, J.T.; Nagler, E.M.; Hurtado, D.A.; Pronk, N.P.; Wagner, G.R. Integrating worksite health protection and health promotion: A conceptual model for intervention and research. Prev. Med. 2016, 91, 188–196. [Google Scholar] [CrossRef]
  90. Pishgar, M.; Issa, S.F.; Sietsema, M.; Pratap, P.; Darabi, H. REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health. Int. J. Environ. Res. Public Health 2021, 18, 6705. [Google Scholar] [CrossRef] [PubMed]
  91. Hao, J.L.; Cheng, B.; Lu, W.; Xu, J.; Wang, J.; Bu, W.; Guo, Z. Carbon emission reduction in prefabrication construction during materialization stage: A BIM-based life-cycle assessment approach. Sci. Total Environ. 2020, 723, 137870. [Google Scholar] [CrossRef] [PubMed]
  92. Xiong, H.B.; Cao, J.X.; Zhang, F.L.; Ou, X.; Chen, J. Investigation of the SHM-oriented model and dynamic characteristics of a super-tall building. Smart Struct. Syst. 2019, 23, 295–306. [Google Scholar]
  93. CEN Technical Committee 350 (CEN TC 350); Sustainable Construction—Environmental Product Declarations and Product Category Rules. European Committee for Standardization (CEN): Brussels, Belgium, 2005.
  94. EN 15978:2011; Sustainability of Construction Works—Assessment of Environmental Performance of Buildings—Calculation Method. European Committee for Standardization (CEN): Brussels, Belgium, 2011.
  95. EN 15804:2012; Sustainability of Construction Works—Environmental Product Declarations—Core Rules for Product Category of Construction Products. European Committee for Standardization (CEN): Brussels, Belgium, 2012.
  96. Tijani, B.; Jin, X.H.; Osei-Kyei, R. Effect of project organization elements on the mental health of project management practitioner in AEC projects. Eng. Constr. Archit. Manag. 2024, 31, 73–114. [Google Scholar] [CrossRef]
  97. Madureira, R.C.; Silva, C.S.; Amorim, M.; Dias, M.F.; Lins, B.; Mello, G. Think Twice to Achieve a Sustainable Project Management: From Ecological Sustainability towards the Sustainable Project Management Cube Model. Sustainability 2022, 14, 3436. [Google Scholar] [CrossRef]
  98. Taboada, G.L.; Seruca, I.; Sousa, C.; Pereira, A. Exploratory Data Analysis and Data Envelopment Analysis of Construction and Demolition Waste Management in the European Economic Area. Sustainability 2020, 12, 4995. [Google Scholar] [CrossRef]
  99. Ma, X.Z.; Xiong, F.; Olawumi, T.O.; Dong, N.; Chan, A.P.C. Conceptual Framework and Roadmap Approach for Integrating BIM into Lifecycle Project Management. J. Manag. Eng. 2018, 34, 05018011. [Google Scholar] [CrossRef]
  100. Morteza, A.; Chou, R.A. Distributed Matrix Multiplication: Download Rate, Randomness and Privacy Trade-Offs. In Proceedings of the 2024 60th Annual Allerton Conference on Communication, Control, and Computing, Urbana, IL, USA, 24–27 September 2024; IEEE: Piscataway, NJ, USA, 2024. [Google Scholar]
  101. Oyewobi, L.; Okanlawon, T.T.; Ibrahim, K.; Jimoh, R.A. Influence of blockchain adoption barriers and drivers on potential application areas in the construction lifecycle: Partial least squares structural equation modelling (PLS-SEM) approach. Eng. Constr. Archit. Manag. 2025, 32, 1798–1815. [Google Scholar] [CrossRef]
  102. Singh, A.K.; Kumar, V.P.; Dehdasht, G.; Mohandes, S.R.; Manu, P.; Rahimian, F.P. Investigating the barriers to the adoption of blockchain technology in sustainable construction projects. J. Clean. Prod. 2023, 403, 136840. [Google Scholar] [CrossRef]
  103. Liu, C. HPCLS-BC: A novel blockchain framework using heterogeneous peer-node and cloud-based ledger storage for Internet of Things applications. Future Gener. Comput. Syst. 2024, 150, 364–379. [Google Scholar] [CrossRef]
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Figure 1. Stepwise selection process.
Figure 1. Stepwise selection process.
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Figure 2. Keyword cluster map of digital intelligence empowerment in construction.
Figure 2. Keyword cluster map of digital intelligence empowerment in construction.
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Figure 3. Keyword time zone graph on the evolution of research topics in the construction.
Figure 3. Keyword time zone graph on the evolution of research topics in the construction.
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Figure 4. Structure of digital intelligence empowerment in the planning and design stage.
Figure 4. Structure of digital intelligence empowerment in the planning and design stage.
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Figure 5. Structure of digital intelligence empowerment in the construction stage.
Figure 5. Structure of digital intelligence empowerment in the construction stage.
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Figure 6. Structure of digital intelligence empowerment in the operation stage.
Figure 6. Structure of digital intelligence empowerment in the operation stage.
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Figure 7. Lifecycle analyses in BLM.
Figure 7. Lifecycle analyses in BLM.
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Figure 8. Digital Intelligence Readiness Assessment Framework (DIRAF).
Figure 8. Digital Intelligence Readiness Assessment Framework (DIRAF).
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Figure 9. Blockchain Implementation Roadmap for BLM.
Figure 9. Blockchain Implementation Roadmap for BLM.
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Table 1. Inclusion and exclusion criteria.
Table 1. Inclusion and exclusion criteria.
CategoryCriterion
Inclusion Criteria(1) Focusing on Construction 4.0 technologies;
(2) Addressing Building Lifecycle Management (BLM);
(3) Including empirical or case study data.
Exclusion CriteriaFocusing solely on manufacturing digitization
OR lack empirical BLM applications.
Table 2. Search results.
Table 2. Search results.
Number of Articles Select
Construction 4.059173
digital + empowerment + construction494
digital + enablement + construction10
digital + enabling + construction30554
digital + empowerment + building1252
digital + enablement + building40
digital + enabling + building99226
intelligence + empowerment + construction30
intelligence + enablement + construction00
intelligence + enabling + construction9317
intelligence + empowerment + building301
intelligence + enablement + building00
intelligence + enabling + building40536
Total2598213
Table 3. Number of papers distributed by journal.
Table 3. Number of papers distributed by journal.
Journal TitleNumber
Engineering Construction and Architectural Management51
Sustainability32
Automation in Construction26
Journal of Building Engineering5
Journal of Construction Engineering and Management4
Buildings3
IEEE Access3
IEEE Transactions on Engineering Management3
Computational Intelligence and Neuroscience2
Environmental Science and Pollution Research2
International Journal of Environmental Research and Public Health2
Table 4. Top 10 keyword cluster.
Table 4. Top 10 keyword cluster.
ClusterIDSizeSilhouette MeanYearLabel (LSI)
#0 life cycle assessment310.9342021building information; digital twin; digital transformation
#1 sem230.9252020project management; building information modeling
#2 building information modeling220.9312018building information modeling; unmanned aerial vehicle
#3 project delivery models200.9052018digital maturity; project delivery models; infrastructure megaprojects
#4 safety performance180.9312017autism intervention; knowledge exchange; knowledge elicitation
#5 digital transformation160.9542021digital transformation; business models; dynamic capabilities
#6 digital empowerment140.9312021digital technology; rural sustainable development; digital village
#7 blockchain120.9562021construction industry; systematic literature evaluation; critical success
#8 technological trajectory121.0002022construction industry; search path node pair; technological trajectory
#10 machine learning90.8752020digital twin; artificial intelligence; machine learning; big data cybernetics
Table 5. Comparison between Blockchain and Cloud-Based Ledger Systems.
Table 5. Comparison between Blockchain and Cloud-Based Ledger Systems.
DimensionBlockchainCloud-Based Ledger Systems
DecentralizationDistributed across nodes; no single point of failureCentralized; outage risks
ImmutabilityCryptographic hashing and consensus ensure tamper-proofingRelies on provider’s access controls; internal tampering possible
Trust MechanismSmart contracts enforce automation; no third-party trustDepends on provider’s credibility; manual audits needed
ScalabilitySharding/sidechains improve TPS but add complexityElastic scaling by provider; TPS limited by infrastructure
Storage CostHigh on-chain costs; requires off-chain storageLower cost but data sovereignty remains with provider
PrivacyFederated chains use channels for role-based access controlEncryption provided but data is monitored by provider
Typical BLM Use CasesCross-organizational collaboration (design-construction-operation data sharing), asset traceabilitySingle-organization workflows
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Lai, J.; Wan, R.; Chong, H.-Y.; Liao, X. Digital Intelligence in Building Lifecycle Management: A Mixed-Methods Approach. Sustainability 2025, 17, 5121. https://doi.org/10.3390/su17115121

AMA Style

Lai J, Wan R, Chong H-Y, Liao X. Digital Intelligence in Building Lifecycle Management: A Mixed-Methods Approach. Sustainability. 2025; 17(11):5121. https://doi.org/10.3390/su17115121

Chicago/Turabian Style

Lai, Jianying, Runnan Wan, Heap-Yih Chong, and Xiaofeng Liao. 2025. "Digital Intelligence in Building Lifecycle Management: A Mixed-Methods Approach" Sustainability 17, no. 11: 5121. https://doi.org/10.3390/su17115121

APA Style

Lai, J., Wan, R., Chong, H.-Y., & Liao, X. (2025). Digital Intelligence in Building Lifecycle Management: A Mixed-Methods Approach. Sustainability, 17(11), 5121. https://doi.org/10.3390/su17115121

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