Building Information Modeling and Big Data in Sustainable Building Management: Research Developments and Thematic Trends via Data Visualization Analysis

Abstract

At present, the construction industry has not yet fully optimized the integration of the potential of big data. Past studies signaled the potential benefits of integrating building information management (BIM) and big data in the field of sustainable building management (SBM). However, these studies have a monotonous perspective in identifying the development of BIM and big data applications in SBM. Therefore, this paper aims to explore BIM and big data from various perspectives in the field of SBM to identify the aspects where additional efforts are required and provide insights into future directions, and it adopts a mixed method of quantitative and qualitative analysis, including bibliometric analysis and knowledge mapping, providing a macro-overview of the research status and development trends of BIM and big data integration for SBM from multiple bibliometric perspectives. The results indicate the following: (1) the current studies on BIM and big data integration (BBi)-aided SBM mainly focused on data integration and interoperability for collaboration, development of information technologies and emerging technologies, data analysis and presentation, and green building and sustainability assessment; (2) the longitudinal analysis of three time-slice phases (2010–2014, 2015–2018, and 2019–2024) over the past 15 years indicates that the studies on BBi-aided SBM have been expanded from the application of BIM in construction projects to the integration and interoperability of BIM with information technology, the integration of virtual models with physical buildings, and sustainable management throughout the building life cycle stages; and (3) key research gaps and emerging directions include data integration and model interoperability across the building life cycle, model transferability in the application of technology, and a comprehensive sustainability assessment framework based on the whole building life cycle stages.

1. Introduction

In recent years, the full digitalization of the construction industry and sustainable building management (SBM), which refers to the ongoing optimization of building performance with respect to environmental, social, and economic sustainability goals, has received growing attention in both academia and the professional practice of the architectural, engineering, and construction (AEC) industries [1]. Among the key technologies driving this transformation is Building Information Modeling (BIM), which refers to a digital representation of a building’s physical and functional characteristics, serving as a shared knowledge resource for information about a facility throughout its life cycle [2]. The development of BIM is a core technology in this domain, which has brought opportunities for the upgrade of traditional manufacturing formats and collaboration within the AEC sector [3]. As BIM allows the management of multidisciplinary information and data generated throughout the building life cycle, this systematic approach provides an opportunity for building life cycle performance and sustainability enhancement toward cradle-to-cradle life cycle design, construction, and management [4,5,6].

In such a system, achieving sustainability outcomes is considerably complex, as construction project decisions are based on the use of massive data that have increased almost exponentially since BIM adoption in the construction industry [7,8,9]. Big data refers to extremely large and complex datasets that are generated at high speed and from a wide variety of sources and that require advanced methods for processing and analysis [10]. Nowadays, the characteristics of big data can be concluded to be volume, variety, velocity, and value [11]. In the field of construction, big data has caused extensive concerns, due to its strength on the capabilities of integrating, transmitting, and analyzing the large scale of multi-source, heterogeneous, and real-time data [7,8]. However, the lack of detailed and real-time life cycle data could ultimately hinder the construction process and productivity [10,12].

There are two main reasons for the insufficient use of big data in the construction industry. Firstly, the project collaboration process is becoming increasingly complex with the involvement of diverse professionals [13] and various information technologies related to big data (e.g., internet of things (IoT), artificial intelligence (AI), cloud computing, and edge computing) [14,15,16], which generate a large amount of multi-source heterogeneous data from the process of project design to project delivery [17]. Little research has been devoted to the integration of data and interoperability issues. Data integration and interoperability have gradually changed the way information can be created and exchanged between stakeholders in large and complex projects [18]. Secondly, most data mining and exchange processes only focus on a single stage of the building life cycle. The poor exchange and integration of disparate and heterogeneous data across the life cycle phase hinders the support for decision-making in construction projects [19].

Additionally, a large amount of data is produced during the building life cycle, since the construction contractors begin to widely use emerging information technology to manage the project. Advanced technologies, such as IoT, cloud computing, and AI, have been applied in the construction industry, which bring opportunities to support efficient design optimization, performance evaluation, and risk monitoring [20,21,22]. The implementation of these advanced technologies can improve productivity and lower operating costs [23]. However, the potential of emerging technologies in the construction sector is being exploited slowly due to the high degree of data fragmentation [24] and inefficient integration of data in BIM [25].

Although BIM and big data each offer significant benefits, the construction industry has not yet fully realized the potential of their integration [10]. A deep gap among the solutions of BIM and big data integration and sustainable building management still exists. In fact, the value of data in building information management has not been fully utilized. Current studies mainly focus on the application of big data in specific construction projects in promoting SBM [26,27]. And the contribution of BIM and big data in the field of SBM is presented separately. In addition, previous studies seem to adopt a limited perspective in identifying the development of BIM and big data applications in SBM.

Thus, the literature lacks a review of existing works that combines BIM and big data domains from various perspectives in the fields of SBM. A holistic review of previous research can identify the aspects where additional efforts are required and distinguish which future directions for BIM and big data integration (BBi) for SBM would be most helpful. This suggests a need for a comprehensive review that connects both fields to better support sustainable practices in construction. Therefore, this paper set out to critically analyze current studies on BBi-aided SBM from multiple bibliometric perspectives to shed light on the current status of BBi-aided SBM, the current knowledge structure and knowledge evolution path of BBi-aided SBM, and the potential avenues for further research.

This paper contains six concurrent sections. The first section outlines the research design of this review, followed by an examination of statistics, research topics, and knowledge development trends published on BBi-aided SBM. The subsequent two sections present a synthesis analysis focused on a detailed overview of existing research and results of bibliometric analysis, along with an overview of research gaps and potential future research directions. The final section draws the conclusions.

2. Methods

This paper adopts a mixed method of quantitative and qualitative analysis, including bibliometric analysis and knowledge mapping. Since different bibliometric software packages are based on distinct principles and different analysis methods, the analysis results obtained by a single software tool may not be accurate and completely indicate the development trends in the research field. He et al. [26] explored contemporary corporate eco-innovation through the co-word analysis using different bibliometric software to illustrate the development trends of research. Additionally, Yang et al. [27] conducted a comprehensive review associating the advantages of traditional bibliometric tools with the latest bibliometric software in a previous study. Therefore, following prior studies, this paper intends to gauge the latest research progress and research trends of BBi-aided SBM from multiple bibliometric perspectives that encompass changes in the number of publications, changes in journal citations, keyword evolution, and development in thematic maps.

To address the limitations inherent in relying on a single bibliometric tool, such as potential algorithmic bias or restricted visualization perspectives, and to gain a more comprehensive, multi-dimensional understanding of the research landscape on BBi-aided SBM, this study employs a suite of complementary bibliometric software tools. Each tool possesses distinct strengths in specific analytical functions. This integrated approach assists in visualizing knowledge structures and evolution paths from diverse perspectives. This multi-tool strategy constitutes a core methodological approach to enable a richer and more nuanced quantitative analysis than would be possible with any single tool.

As shown in Figure 1, the research methodology flow contains three stages: (1) an initial search of the research topic has been executed and followed by data inspection and screening; (2) various bibliometric software tools have been employed to build a holistic analytical network to identify the knowledge structure and knowledge evolution paths to the research domain; and (3) a synthesis analysis has been conducted based on keywords clustering.

2.1. Data Collection

This research retrieved published articles and accompanying metadata related to BBi-aided SBM from the Web of Science (WoS) database. The WoS database is chosen as it contains a wide range of high-impact journals and collections of articles, including the natural sciences, social sciences, arts, and humanities [28]. The search query contains only papers that focus on three main topics, namely, BIM, big data, and sustainable building. As shown in

Table 1. Topic search queries (generated by the authors).

Stages	String and Filter	No. of Articles
#1	Database: Web of Science (Core Collection) Topic = (“building information model*” OR “BIM” OR” building information management” OR “green building information model*” OR “green bim”) Timespan: all year; document types: article and review	11,680
#2	Database: Web of Science (Core Collection) Topic = (“sustainab* building*” OR “building life cycle” OR “green building*” OR “sustainab* building design” OR “building environment* sustainab*” OR “sustainab* building construction” OR “sustainab* building operation”) Timespan: all year; document types: article and review	6015
#3	Database: Web of Science (Core Collection) Topic = (“big data*” OR “bigdata*” OR “bigdata analytics” OR “digital twin” OR “cloud computing” OR “data security” OR “real-time computing” OR “data integration” OR “data model*” OR “data mining” OR “data visualization” OR “data sharing”) Timespan: all year; document types: article and review	178,242
(#2 OR #1) AND #3	Remove duplicates	1014

, these query records are organized as sets #1, #2, and #3, respectively. Subsequently, the search timespan has been set as “All year”, and only “Article” and “Review” have been selected for further analysis. An “OR” operator was applied between the keywords. A total of 296,320 articles were obtained from the WoS database, including a large number of irrelevant articles. Since “BIM” and “sustainable building” are associated research fields, these two keywords are connected by “OR”, combined with AND “Bigdata”, resulting in 1010 articles.

2.2. Data Analysis

Bibliometric analysis and knowledge mapping were adopted as data analysis techniques. Bibliometric analysis demonstrates the knowledge base and provides a general overview of the research area, while knowledge mapping reveals the dynamic changes in research by mining, sorting, and visualizing the knowledge structure [29].

To address limitations inherent in single-software approaches [27], an integrated multi-tool strategy was implemented as shown in Figure 2 and listed in

Table 2. Functions and integrated complementary effects of the used software toolkits for visual quantitative bibliometric analysis (devised by the authors).

Software	Core Function	Complementary Role
HistCite	Literature/citation time series analysis; High-impact journal identification	Provide a core literature benchmark for the field, laying the foundation for subsequent subject analysis (Bibliometrix/CiteSpace)
Bibexcel	Co-word matrix construction; Keyword frequency statistics	Generate structured data for SPSS clustering and CiteSpace evolution analysis
CiteSpace	Knowledge evolution path; Research frontier detection	Reveal interdisciplinary hubs and knowledge turning points, forming a spatial and temporal complementarity with the Bibliometrix subject map
SPSS Statistics	Hierarchical cluster analysis; Statistical validation	Convert Bibexcel’s matrix into a visual subject cluster to verify CiteSpace’s automatic clustering results
Bibliometrix	Topic map analysis; Collaboration network visualization	Quantify the development stage of the research subject, and form a “dynamic-static” dual perspective with CiteSpace’s evolution path

. This data analysis strategy leverages complementary tool capabilities to mitigate algorithmic bias, cross-validate findings, and generate a comprehensive and objective analysis. Figure 2 illustrates the structured analytical protocol that builds upon schemes developed and applied by prior studies.

The analytical process unfolded across two interconnected dimensions. The intellectual base was examined through descriptive bibliometrics. The descriptive statistics of the 1014 articles were analyzed via HistCite (version 2.1) and CiteSpace (version 6.4.1) software tools to reveal the current status and characteristics of BB-aided SBM, in which the publication metadata were collectively analyzed, including citation networks, the identification of journal articles, and the field of publication, establishing foundational insights into the development of the field. Subsequently, to explore the field of BBi-aided SBM more clearly and objectively, the research front was investigated through three synergistic approaches. Co-word analysis was conducted to accurately identify the core themes, in which the occurrence of keywords and the co-occurrence symmetry matrix of keywords were subsequently generated via Bibexcel (version 1.0) software for further hierarchical clustering analysis in SPSS (version 27) to reveal popular research topics. Finally, Bibliometrix (version 4.3.0) software tools were used to present the knowledge evolution path and development trends.

The specific software packages used, their primary functions, and how they complement each other are listed in Table 2. The integration of these complementary approaches provides an in-depth representation of popular themes and identifies merging trends. The visual bibliometric analysis, facilitated by this multi-tool strategy, provides a more objective and less subjective bias-prone overview compared to single-tool analysis.

3. Results of the Quantitative Analysis

3.1. Analysis of Intellectual Base

3.1.1. Descriptive Statistics

As shown in Figure 3, the studies on BBi-aided SBM from 1999 to 2009 were counted in only one relevant published article in each of the following five years: 1999, 2002, 2004, 2008, and 2009. The majority of studies (1010 out of 1014) were conducted during the period from 2010 to 2024. According to Figure 4, from 2010 to 2014, the number of publications increased slowly. Then, the research witnessed a period of gradual publication growth from 2015 to 2018. The number of publications shows a rapid upward trend between 2019 and 2024. As such, the timespan of the development of BBi-aided SBM from 2010 to 2024 can be divided into three time phases (2010 to 2014, 2015 to 2018, and 2019 to 2024) as time-slices based on the statistical results. The use of time-slicing to identify periods with distinct rates of research activity assists in revealing significant structural changes that occur at disparate stages and summarizes the direction of research development [30]. Wei [31] and Liu et al. [32] used the time-slice approach to analyze changes in the number of publications, changes in journal citations, and keyword evolution in the BBi-aided SBM research field, which helps to gain deeper insight into the migration change characteristics of the research. This division not only suggests leapfrog growth in the number of literatures but also corresponds to the key inflection point of technological evolution. The results of the published research statistics are shown in Figure 4, which indicates the following:

(1) In the first time-slice phase (2010–2014), the total number of published studies shows a fluctuating trend, comprising 2.9% of the total publications, which was also the embryonic period of related technologies. This indicates that the development of BBi-aided SBM is still in its infancy.

(2) In the second time-slice phase (2015–2018), the number of related studies has grown significantly with the increasing application of digital technology in construction. The total number of articles published in this period reached 112 (11% of total publications), at which time technologies such as IoT and AI were emerging, and this period was a period of growth for the emergence of related technologies. This shows that the development of BBi-aided SBM has entered a dispersal growth phase.

(3) During the third time-slice phase (2019–2024), the research trend rises sharply with 868 published articles (86% of total publications), in which the technology is approaching maturity, and this was an explosive period, when the BBi-aided SBM was applied to the actual system, indicating a rapid growth phase of BBi-aided SBM development. In the past 15 years (2010–2024), the number of published articles has increased by 50 times.

There is a similar trend in the frequency of citations of the articles, with no more than 150 citations per year during the first time-slice phase (2010–2014), whilst in the second time-slice phase (2015–2018), the frequency of citations of the articles shows a clear upward trend, reaching 700 citations in 2018. The citation frequency of the articles then increases rapidly during the third time-slice phase (2019–2024), reaching 7945 in 2024. In addition, the number of publications related to BBi-aided SBM has recently increased by 233% from 2019 to 2024. This suggests that the growing trend of research in BBi-aided SBM is set to continue.

3.1.2. Trends and Identification of Journal Articles

The HistCite software package is adopted to analyze the most important journals that published articles in the field of BBi-aided SBM from 2010 to 2024. The most important feature of HistCite is the ability to identify highly cited papers and authors based on Local Citation Score (LCS) and Global Citation Score (GCS) [33]. The LCS is the number of citations corresponding to the current publications on BBi-aided SBM, indicating the recognition of the article within the research field. The GCS is the number of citations corresponding to publications in all fields. If an article has a high GCS and a small LCS, it indicates that the article has been cited by researchers in various fields, and it is not possible to accurately determine the degree of recognition of the article within the field of study. In the WoS database, the abovementioned 1010 articles on BBi-aided sustainable buildings were published in 202 academic journals between 2010 and 2024, of which the top 13 journals (5%) with 594 articles, more than half of the articles, published in past years are shown in Figure 5, with two curves representing the LCS and GCS, respectively. The 594 articles cover diversified fields, such as environmental science, computer science, architecture, and urban construction, indicating that the research on BBi-aided SBM is interdisciplinary. The number of articles published in the journal AUTOMATION IN CONSTRUCTION (146 articles) is ahead of other journals, demonstrating that the relevant research articles published in the journal are receiving attention worldwide, of which most of the articles focus on information technology in design, engineering, construction technology, operations and maintenance, and project management. Additionally, journals such as BUILDINGS (105 articles), SUSTAINABILITY (67 articles), APPLIED SCI-ENCES-BASEL (58 articles), IEEE ACCESS (25 articles), ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (23 articles), and ADVANCED ENGINEERING INFORMATICS (38 articles) are active forums for BBi-aided SBM in the fields of environmental science, chemical and materials engineering, computer science, physical geography, and remote sensing.

Furthermore, the journals AUTOMATION IN CONSTRUCTION (LCS = 531 and GCS = 9215) and ADVANCED ENGINEERING INFORMATICS (LCS = 114 and GCS = 1540) lead in terms of the LCS and GCS indexes compared to others. These two journals are the most important journals for research on BBi-aided SBM. In terms of the LCS, the journals IEEE ACCESS (LCS = 125) and ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT (LCS = 46) have a noticeable influence on other studies in the field. In terms of the GCS, journals such as SUSTAINABILITY (GCS = 1487), BUILDINGS (GCS = 1425), and ENERGY AND BUILDINGS (GCS = 1220) are influential forums for research on BBi-aided SBM. These journals are interdisciplinary and concerned with computer-aided technologies and computer innovations applied to the fields of civil engineering, urban systems, and the natural environment. By and large, most of the current research focuses on the collaborative relationship between urban systems, built environments, and natural environments based on information visualization and computer analysis technology.

3.1.3. Research Subject Category

The CiteSpace software has been used to extract subject category information from the WoS database to visualize the main disciplinary categories of the BBi-aided SBM. The top 10 research subject categories on the BBi-aided SBM and their frequency and centrality values are shown in Figure 6. The co-occurrences of subject category reveals that ENGINEERING CIVIL (frequency = 497), CONSTRUCTION and BUILDING TECHNOLOGY (frequency = 417), and ENGINEERING MULTIDISCIPLINARY (frequency = 126) are the three most popular research categories, followed by ENVIRONMENTAL SCIENCES (frequency = 118) and GREEN and SUSTAINABLE SCIENCE and TECHNOLOGY (frequency = 107). Among the top 10 subject categories, COMPUTER SCIENCE INTERDISCIPLINARY APPLICATIONS (centrality = 0.34) has the highest centrality and plays a significant role in research on the BBi-aided SBM, followed by ENGINEERING CIVIL (centrality = 0.32) and ENGINEERING ELECTRICAL and ELECTRONIC (centrality = 0.15). Hence, the past research on the BBi-aided SBM involves multidisciplinary subject categories, from computer science to engineering and urban planning.

3.2. Analysis of the Research Front

3.2.1. Co-Word Analysis and Clustering

Keywords in an article generally summarize and express the main content [34], and co-word analyses have been conducted for research exploration in many different fields [35]. Statistical analysis of the occurrence and co-occurrence of keywords reveals the connection between research topics as well as the hotspots and knowledge structure of the research [34]. Therefore, the Bibexcel software package was used to perform co-word analysis through the keyword co-occurrence matrix generated by high-frequency terms and to visualize the knowledge structure. As above mentioned, the 1010 articles during 2010 and 2024 were imported into Bibexcel to obtain 1001 keywords and their frequency of occurrence. Keywords with a frequency of occurrence greater than 11 were selected to determine the co-occurrence between every two keywords. Additionally, different forms of keywords (e.g., synonyms, abbreviations, case words, singular, and plural words) were standardized as part of this process of identifying co-occurrence. As shown in

Table 3. Frequent keywords with more than 11 occurrences in articles on the BBi-aided SBM from 2010 to 2024 generated via Bibexcel software tool (devised by the authors).

No.	Keyword	Freq.	No.	Keyword	Freq.
1	BIM	546	25	Data model	21
2	Digital Twin	302	26	Virtual reality	21
3	Construction industry	132	27	Thermal comfort	20
4	Internet of things	98	28	Energy efficiency	19
5	Big data	71	29	Linked data	19
6	Facility management	67	30	Smart city	19
7	Internet	63	31	Life cycle assessment	19
8	Integration	59	32	Structural health monitoring	18
9	Industry foundation classes	54	33	Circular economy	17
10	Artificial intelligence	46	34	Fault detection	16
11	GIS	45	35	Infrastructure	16
12	Augmented reality	39	36	Ontology	16
13	Simulation	35	37	Sustainability	16
14	Cloud computing	34	38	Cyber–physical system	14
15	Green building	34	39	Neural network	14
16	Machine learning	34	40	Algorithm	13
17	Data mining	32	41	Construction management	13
18	Industry 4	32	42	Energy performance	13
19	Life cycle management	31	43	Decision-making	12
20	Visualization	31	44	Asset management	11
21	Semantic web	24	45	Built environment	11
22	Smart construction	24	46	Collaboration	11
23	Data integration	23	47	Information technology	11
24	Deep learning	22

, a total of 47 frequent keywords with more than 11 occurrences were obtained. The frequency of the 47 keywords is 2238 (about 46% of the total). Thus, these 47 keywords have the potential to cover the main research topics of the BBi-aided SBM, which are the dataset of the subsequent cluster analysis and social network analysis.

In addition, a symmetric keyword co-occurrence matrix was carried out to reflect the co-occurrence between pairs of keywords based on the result of keyword frequency by employing Bibexcel software, of which the most frequent and relevant data for the keyword co-occurrences on big data-driven BIM in SBM is shown in

Table 4. Symmetric keyword co-occurrence matrix of the BBi-aided SBM via Bibexcel software tool (devised by the authors).

	BIM	Digital Twin	IoT	IFC	AI	Facility Management	Construction Industry	Machine Learning	Sustainability
BIM
Digital Twin	133
Internet of Things	50	40
IFC	42	6	3
Artificial Intelligence	15	17	13	1
Facility management	15	11	6	0	0
Construction Industry	14	12	0	1	0	0
Machine Learning	11	9	4	0	11	2	2
Sustainability	10	11	2	5	6	0	2	0

. Since the frequency of keyword co-occurrences located on both sides of the diagonal in Table 4 is the same, i.e., the left and right matrices are symmetric, the data of the left diagonal and right matrix were removed from Table 4 to avoid misleading anyone.

As indicated in the second column of Table 4, BIM and digital twins are the most commonly discussed topics, with a frequency of 133 occurrences. This indicates that the research focus is concentrated on the integration and application of these two technologies, particularly within the AEC industry. Traditionally, BIM is utilized for design and construction documentation, while digital twins are employed for real-time monitoring, simulation, and life cycle management. Their frequent pairing underscores the emphasis on their synergistic potential. This pairing highlights a growing interest in the holistic and integrated use of these technologies, bridging the design, construction, and operational phases of projects, thereby facilitating end-to-end project life cycle integration [36]. Additionally, this co-occurrence reflects the growing interest in using BIM and digital twins to drive innovation, improve asset management, and support the digital evolution of the built environment. However, the application of digital twins in construction is still in its early stages [37].

As shown in Table 4, BIM and IoT co-occurred 50 times, while digital twins and IoT co-occurred 40 times, from a symmetric keyword co-occurrence matrix perspective, indicating that several studies focus on the collaboration between BIM and IoT. This provides better semantic interoperability of data, meeting the real-time data update requirements of digital twins for smart buildings [38]. The co-occurrence of BIM and industry foundation classes (IFC) was observed 42 times. The IFC data modeling format is an effective means of information exchange between project stakeholders [36]. The standardization and interoperability of BIM have garnered significant attention, particularly in enhancing information exchange between different software platforms and stakeholders within the built environment. AI contributed 15 times to BIM, 17 times to digital twins, and 11 times to machine learning. The research focuses on the integration of AI technologies with these advanced digital modeling and simulation tools to drive optimization and innovation across various stages of construction, as well as to enhance efficiency [39]. Furthermore, facility management and BIM co-occurred 15 times, indicating that several studies focus on the integration of BIM technology with facility management practices to promote the digitalization of the relevant field [40].

The co-word matrix is systematically and hierarchically clustered to further reveal the intrinsic relationship between keywords and the structure of research trends of the BBi-aided SBM. The matrix of high-frequency keyword co-occurrence derived from Bibexcel software has been used as the original dataset, which is used for the measurement of “intergroup” connection and “cosine distance” in Statistical Product Service Solutions (SPSS) Statistics software, version 27, for systematic clustering. As such, a hierarchical clustering dendrogram of 42 co-words in the BBi-aided SBM has been obtained, as shown in Figure 7, which can be further grouped into four cluster groups: cluster group 1 is focused on sustainable smart building and facility management through the integration of the BBi-aided SBM; cluster group 2 is concentrated on the development of advanced technologies of the BBi-aided SBM; cluster group 3 is associated with interoperability and data integration for the BBi-aided SBM; and cluster group 4 is related to data mining and green building. These clusters are further discussed in Section 4.

3.2.2. Knowledge Evolution Path

The knowledge structure and research themes of the BBi-aided SBM have been explored in Section 3.2.1. Additionally, keyword evolution demonstrates the trend of the research field in a macro way and summarizes the direction of knowledge development [30]. Hence, this section presents the keyword evolution path to further uncover the development trend of research focus by using CiteSpace software, where the keyword co-word analysis has been performed to evaluate the three time-slice phases (2010–2014, 2015–2018, and 2019–2024) of the BBi-aided SBM. The number of research nodes in the three time-slice phases gradually increases, and the cross-fertilization of the nodes is significantly enhanced. The top 15 keywords ranked in terms of frequency and centrality from 2010 to 2021 are presented in

Table 5. Top 15 keywords on the BBi-aided SBM in the three time-slice phases, i.e., 2010 to 2014, 2015 to 2018, and 2019 to 2024, via Citespace software tool (devised by the authors).

Years 2010 to 2014			Years 2015 to 2018			Years 2019 to 2024
Keyword	Freq.	Centrality	Keyword	Freq.	Centrality	Keyword	Freq.	Centrality
BIM	12	0.21	BIM	39	0.13	BIM	463	0.01
Design	6	0.22	Model	24	0.33	Digital twin	302	0.01
System	5	0.3	System	19	0.18	System	132	0.03
Model	4	0.19	Design	18	0.28	Management	125	0.02
IFC	3	0.09	Management	17	0.16	Framework	117	0.05
Management	3	0.07	Framework	14	0.19	Construction industry	113	0.03
Tracking	2	0.01	Construction	14	0.14	Model	105	0.03
AEC	2	0.0	IFC	12	0.15	Design	97	0.02
Framework	2	0.05	Performance	10	0.12	IoT	96	0.01
Construction	2	0.01	Information technology	9	0.05	Technology	65	0.03
GIS	2	0.0	Technology	9	0.12	Big data	63	0.02
Commerce	1	0.0	Architecture	7	0.08	Internet	63	0.02
Bridge	1	0.0	Consumption	7	0.17	Facility management	62	0.02
Industry	1	0.0	GIS	5	0.05	Performance	59	0.03
Augmented reality	1	0.0	Environment	4	0.01	Integration	59	0.04

. Keywords such as BIM, design, system, model, and management ranked at the top in terms of frequency. The concepts of design, system, and management in BIM are in the infancy in terms of basic research on informationization in the construction industry. Between 2019 and 2024, keywords such as digital twin, technology, IoT, and big data appear more frequently. Business collaboration and the integration of various software models in the construction industry have become critical research topics due to the large amount of data, leading to the complexity of different models.

A thematic map has been created to further consolidate the trends of existing research priorities by using the Bibliometrix software that can be applied to bibliometric analysis, co-word analysis, and cluster analysis and integrates the visualization capabilities of various scientific mapping tools [41]. The thematic map consists of four quadrants [42], of which the first quadrant (top right) belongs to Motor themes, keywords in the field area representing important and well-developed themes; the second quadrant (top left) belongs to Niche themes, which represent well-developed but isolated research topics; the third quadrant (bottom left) belongs to Emerging or Declining themes, which represent less developed foci that are important to the fringe of research trends and may have just emerged or are about to disappear; and the fourth quadrant (bottom right) belongs to basic themes, which represent weakly developed research areas that are important to the field but have not been well developed and generally refer to foundational concepts. As such, as shown in Figure 8, a thematic map for revealing the trends of the BBi-aided SBM-related research has been obtained during the three time-slice phases via Bibliometrix software.

In the first time-slice phase (2010 to 2014), the research focus is on the application of BIM in construction projects, which mainly discusses the function of BIM in the design phase as well as project management. Within the second time-slice phase (2015 to 2018), the research mainly looks into big data analysis, algorithm research, and semantic integration in sustainable building projects. During the third time-slice phase (2019 to 2024), the research concentrated on the integration and interoperability of BIM and information technology, cyber–physical systems brought about by the integration of virtual models and physical buildings, and the assessment of the whole life cycle performance of buildings.

Furthermore, as shown in Figure 8, four thematic features have been identified: (1) the Motor themes show that big data analysis, the application of algorithms, artificial intelligence, and cyber–physical systems in construction management and optimization of buildings are well-developed topics. In the second time-slice phase (2015 to 2018), the research focuses more on big data analytics, algorithm research, and web semantic integration; the studies in the third time-slice phase (2019 to 2024) shift to systems research for the physical world and information fusion, including artificial intelligence, algorithm-related research, and cyber–physical systems; (2) the Emerging or Declining themes indicate that data-driven 3D modeling, data models, and analysis have developed rapidly since 2015, where the application of neural networks meets the requirements for prediction and optimization in building energy performance. Artificial intelligence and data-driven approaches play a key role in improving building energy efficiency and meeting climate goals [43]. This fully reflects the research trend of data-driven research to enhance the efficiency and effectiveness of energy modeling in architecture and engineering; (3) the Niche themes demonstrate that numerous studies have paid attention to the aspect of building life cycle assessment and management since 2015. The integration of various information technologies involves research aims to manage complex projects, reduce waste, and increase productivity, while achieving harmony between buildings and the environment, and (4) the basic themes indicate that the integration of BIM models, and physical simulation with virtual simulation with emerging technologies, namely, big data, IoT, and augmented reality, has become a recurrent research area associated with the BBi-aided SBM. However, these basic themes are currently less developed research motivations due to being short and newly emerged, but they are very important for the development of big data-driven BIM in SBM. Since 2019, the research and applications using various emerging technologies and digital models have increased greatly. It is foreseen that the future development of research could be characterized by a high degree of informatization. The above features of the trends will be further discussed in Section 4.

4. Synthesis of Research Themes

In Section 3, the quantitative analysis of the articles provides insights into the identification of the BBi-aided SBM research topics, knowledge structure, and knowledge evolution path. As such, this section complements these findings by integrating the literature content into a comprehensive review of research themes to clarify the technical connotations, applications, and challenges of the themes. The identified four groups of clusters of current status of the BBi-aided SBM research (Figure 7) are devised into four themes based on the clusters delineated by the tree diagrams in Figure 8, which are smart technologies for sustainable building systems, advanced technologies, interoperability and data integration, and data mining and green building, as shown in

Table 6. Four thematic categories of the BBi-aided SBM (devised by the authors).

Thematic Category	Cluster	Keyword
Smart technologies for sustainable building systems	1	Structural Health Monitoring, Digital Transformation, Operation and Maintenance, Smart City, Predictive Maintenance, Information Management, Facility management, Industry 4.0, Construction Industry, Energy Efficiency, IoT, Sustainability, Machine Learning, Asset Management, Computer Vision, Sustainable Construction, Circular Economy, Life Cycle, BIM, Construction Management, Sustainable Development, Ontology
Advanced technologies	2	Artificial Intelligence, Augmented Reality, Virtual Reality, Deep Learning
Interoperability and data integration	3	Interoperability, Citygml, Data Integration, Linked Data, Semantic Web, Ifc, Gis, Big Data, Blockchain, Cloud Computing, Integration, Visualization, Digital Twin, Smart Building
Data mining and green building	4	Data Mining, Green Building

. This qualitative analysis offers a more nuanced understanding of how these technological directions align with and extend the bibliometric trends revealed earlier.

4.1. Smart Technologies for Sustainable Building Systems in Building Information Management (BIM) and Big Data Integration (BBi)-Aided Sustainable Building Management (SBM)

As shown in Table 6, keywords for the topic of smart technologies for sustainable building systems in the BBi-aided SBM include “Structural Health Monitoring”, “Digital Transformation”, “Operation and Maintenance”, “Smart City”, “Predictive Maintenance”, “Information Management”, “Facility management”, “Industry 4.0”, “Construction Industry”, “Energy Efficiency”, “IoT”, “Sustainability”, “Machine Learning”, “Asset Management”, “Computer Vision”, “Sustainable Construction”, “Circular Economy”, “Life Cycle”, “BIM”, “Construction Management”, “Sustainable Development”, and “Ontology”. Thus, smart technologies for sustainable building systems in BBi-aided SBM, technologies and tools for smart building systems, operational and performance optimization, sustainability and resource efficiency, and smart city and Industry 4.0 are further investigated in the sections below.

4.1.1. Technologies and Tools for Smart Building Systems

The development of smart building systems relies on the integration of several key technologies that enable data-driven decision-making, optimize resource usage, and promote sustainability. At the core of this ecosystem is BIM, which acts as the digital representation of a building’s physical and functional aspects. BIM serves as a central platform for all stakeholders involved in a building’s life cycle, from design to construction to operation. This centralized model integrates data from multiple sources, ensuring that information is readily available and up to date for all parties involved. However, the true potential of BIM is unlocked when it is combined with advanced technologies such as IoT, machine learning, and computer vision.

IoT is integral to the real-time monitoring of building systems. For instance, by embedding sensors into systems for energy management, construction monitoring, health and safety management, and building management, IoT allows continuous data collection, feeding this information into BIM for analysis and decision-making [15]. This data-driven feedback loop enables buildings to adjust in real time, optimizing their operations while minimizing waste. When integrated with machine learning, IoT data can be analyzed to predict future performance and identify areas for improvement [44]. Machine learning algorithms analyze historical and real-time data to predict when systems might need maintenance or how to optimize energy usage based on occupancy patterns, thus preventing inefficiencies before they occur [45]. On top of this, by using cameras and sensors to monitor the physical environment, computer vision can help with tracking building performance, identifying anomalies, and even enhancing safety. For example, computer vision can be employed on construction sites to automatically detect safety hazards or monitor construction progress, verifying that the building structure conforms to the BIM model [46].

In addition, to make all of these diverse systems work together seamlessly, ontology provides a framework that allows data to flow smoothly between different systems, ensuring that information from various sources can be integrated into a cohesive and actionable model [47]. As a result, building managers and operators can make more informed decisions, improve operational efficiency, and optimize performance.

Furthermore, the culmination of these technologies forms a digital transformation in SBM, where physical spaces are closely monitored and managed through a virtual, data-driven interface. This transformation enhances decision-making, facilitates better collaboration among stakeholders, and drives sustainability by optimizing resource usage and reducing waste. By relying on these interconnected technologies, buildings can evolve into adaptive, responsive systems that not only meet current demands but also anticipate future needs, all while supporting broader goals of SBM.

4.1.2. Operational and Performance Optimization

• Information Management (Asset Management and Facility Management)

Current research trends indicate that academics are increasingly interested in applying BIM to information management aspects [48]. For example, an integrated Geo-BIM model has been developed in a digital building environment for a community in Milan, where data are collected, processed, and analyzed by multiple software to provide relevant information for asset management decisions [49]. In addition, solutions using blockchain and digital twin have been developed. A consensus process model has been provided for multi-party collaboration, where the lossless transmission of building information and traceability of responsibility in asset and facility management (FM) are conducted through the convergence of BIM and blockchain technology [50]. Additionally, an anomaly detection system has been built for asset monitoring based on digital twins by extending the IFC standard, which facilitates efficient and automated data integration in daily operation and maintenance management [48]. Furthermore, Alshammari et al. [51] implemented an access management ontology that enables interoperability of physical built environment assets, sensing and actuation devices, and current built environment services with existing BIM datasets. However, data exchange and interoperability between asset management (AM) and FM are still major challenges in SBM due to factors such as the quantity, type, and quality of data, as well as the lack of industry standards for data integration [49].

• Predictive Maintenance

Predictive maintenance enables the proactive identification and resolution of potential equipment failures, reducing downtime and extending the life cycle of assets. This approach combines IoT sensors, real-time monitoring, and predictive analytics to assess the condition of building systems and predict maintenance needs. For instance, an IoT-enabled platform has been proposed to monitor HVAC systems, using machine learning models to predict component failures and optimize energy consumption [52]. Interestingly, studies have shown that the integration of BIM and digital twins has practical advantages in achieving preventive maintenance and improving the efficiency of infrastructure management operations [53], which directly improves equipment availability, a core operation and maintenance key business performance indicator (KPI).

Additionally, predictive maintenance can be integrated into the digital twin environment for simulating future maintenance scenarios of the SBM and scheduling tasks based on system performance metrics to improve tenant comfort as well as reduce overall building management and operating costs [54]. Despite these advances, predictive maintenance still suffers from standardization and synchronization of data integration [44], and integration with sustainability metrics, such as energy efficiency and carbon footprint reduction, is still understudied.

• Structural Health Monitoring

Structural health monitoring (SHM) plays a critical role in ensuring the safety and efficiency of buildings and infrastructure by leveraging real-time data collection and analysis. SHM systems utilize advanced sensor technologies and data processing algorithms to monitor structural performance, detect potential failures, and improve decision-making in maintenance activities. For example, a real-time SHM system has been developed using BIM as a computational environment for SHM and an integrated digital representation platform for structural performance assessment and risk management of large-span bridges [55]. Moreover, augmented reality (AR) and virtual reality (VR) technologies can be applied to record, systematically interpret, and visualize SHM data in a three-dimensional (3D) environment for a wide range of infrastructure monitoring [56]. However, challenges remain in integrating SHM with broader operational systems into a unified BIM for seamless data-driven monitoring and maintenance.

4.1.3. Sustainability and Resource Efficiency

The primary focus of sustainability and resource efficiency in SBM is to promote energy efficiency, optimize resource utilization, and facilitate sustainable construction practices through technologies such as BIM, IoT, and circular economy frameworks.

The global construction industry consumes about 40% of the total energy each year [57]. As such, energy efficiency plays a central role in achieving sustainability goals in building management. In addition, sustainable construction emphasizes reducing environmental impact while enhancing resource efficiency throughout the building life cycle. Importantly, BIM has emerged as a key enabler by allowing detailed material analysis and life cycle assessments. For example, the integration of BIM and IoT into a building energy management system is used to optimize energy efficiency and conservation in buildings [58], which significantly improves the energy cost ratio. BIM improves the accuracy of quantifying waste and can help practitioners make decisions about waste management [59]. However, further advancements in aligning sustainable construction practices with industry standards and policies are essential.

At present, the demand for sustainable buildings and sustainability assessment criteria is increasing [60]. A number of various green building rating systems are applied to assess and verify the sustainability performance of buildings, such as LEED, BREEAM, Green Star, and CASBEE. BIM is often used to optimize BREEAM credits for building emissions reduction and improvement of building energy efficiency [61]. Similarly, a modeling approach that integrates BIM, LCA tools, and sustainable material databases has been proposed to automatically calculate LEED certification scores [62], from which projects that apply sustainable materials can obtain higher LEED scores. However, no single rating scheme can assess all sustainability aspects of a project [63].

Life cycle management aims to ensure that sustainability principles are upheld across all stages of a building’s life cycle, from design to deconstruction. BIM plays a pivotal role in enabling life cycle thinking by integrating data from design, construction, operation, and maintenance phases. For example, BIM can be utilized for life cycle assessment to help with construction waste management [64]. Additionally, machine learning technologies have been incorporated into life cycle management systems for cost management, automated construction process monitoring, defect detection, and construction waste management [65]. These technologies have enhanced life cycle management practices, which are case-specific and not easily reproducible on other projects [64].

4.1.4. Industry 4.0 and Smart City

Through the integration of IoT, real-time data analytics, and automation, smart cities have been used to respond to the needs of people’s livelihoods, environmental protection, and economic activities [66], which helps the building industry by creating more sustainable environments. Industry 4.0 refers to the ongoing transformation of industrial processes through the integration of automation, IoT, big data, and machine learning [67]. The integration of Industry 4.0 principles in construction management processes allows the construction sector to innovate with smart technologies to increase productivity [68].

Furthermore, construction 4.0 [69] encompasses three key concepts such as digital data, automation, and connectivity. IoT plays an important role in SBM by connecting sensors of physical components to BIM [70,71], where IoT has been used in conjunction with the cloud to improve the efficiency of real-time data transmission [72]. In addition, IoT facilitates smart city planning to optimize public services by managing large amounts of data in real time [73]. The current trend in construction 4.0 is characterized by automated construction [74], where the automated systems improve construction efficiency, such as the automatic planning of routes for material transportation and the automatic planning of construction site tasks [75,76]. Thus, construction 4.0 is a new approach to innovating and improving productivity in SBM.

4.2. Advanced Technologies of BBi-Aided SBM

Keywords in advanced technologies of BBi-aided SBM include “AI”, “AR”, “VR”, and “Deep learning”. Emerging advanced technologies, e.g., AI, AR, VR, and deep learning, in the construction industry are experiencing significant changes in processes and work methods, which have been instigated across all stages of the building life cycle. Advanced technologies development for BBi-aided SBM, physical simulation to virtual simulation, and leveraging artificial intelligence are further investigated in the sections below.

4.2.1. Physical Simulation to Virtual Simulation

In terms of physical simulation to virtual simulation, the application of VR technology in the construction field facilitates participants to comprehend and communicate engineering issues [69]. Applications help with identifying hazardous areas in advance, especially for construction training [77] in the very early project phase. In the briefing and design phase, VR can be used for risk evaluation and scheme design development and to assist with construction plan evaluation and construction process monitoring in the construction stage [78,79,80]. AR technology is used to overlay digital information into the view of users [81] and aid construction workers in activities such as assembly, construction, and maintenance [82]. However, VR technology also faces challenges, where information must be superimposed on the construction object with a high degree of accuracy to accurately guide the user [83]. In addition, the most critical issue currently hindering progress in the application of virtual and augmented reality technology in the construction industry remains the conversion of project data into a standard form and the efficiency of real-time data synchronization available in virtual displays [84], while enabling collaboration between inspectors [56].

4.2.2. Leveraging Artificial Intelligence

In the past decade, the application of AI in SBM has rapidly developed, employing a number of techniques, such as machine learning, data mining and big data analysis, and deep learning [14]. The main purpose of AI in the field of SBM is prediction and optimization.

AI in SBM has been used to enable predictions and decisions about subsequent solutions by summarizing knowledge from past experiences [85,86]. In the briefing and design phase, AI is facilitated to assist in the analysis and design of prefabricated components, such as configuration, size segmentation, and scheme optimization of prefabricated components [87]. In the procurement phase, AI is implemented to analyze complex data and estimate the cost of transportation [88], while being able to optimize the management of the procurement supply chain to reduce costs [80]. During the construction phase, AI has been integrated to predict a variety of potential risks based on historical and real-time data [89]. Indeed, most of the construction projects exceed the expected schedule due to poor planning and inadequate information during the project design phase [65,90], which requires knowledge gathered from previous projects to achieve more accurate estimates. Hence, a multiple regression analysis has been used to predict project costs at the beginning of construction [91]. Similarly, radial basis functions and artificial neural network models have been proposed for preliminary construction cost estimation, which greatly saves costs and time [90]. The use of AI models provides a more robust solution for managing complex variables in modern projects that is transformative and significantly outperforms traditional methods in terms of accuracy and efficiency [92], which also greatly optimizes the project cost deviation rate KPI.

In addition, during the operation and maintenance phase, AI manages data and server resources to form a unified command platform that provides technical support for diagnosis, prediction, and decision-making [93]. Currently, the challenge of AI predictive techniques in SBM lies in the collection, cleaning, and structural processing of data [94]. However, the application of AI technology lacks versatility, and the training models in some cases are unsustainable for other construction projects [65].

Optimization via AI in the field of SBM covers cost optimization [14], site construction optimization [68], semantic optimization [95], and layout optimization [96]. In addition, AI optimization algorithms address the problems that may arise in on-site construction projects [68]. For example, AI is useful for identifying certain elements of a construction site and monitoring the performance of construction workers in real time [97]. AI has been used to rapidly build prefabricated and assembled steel structures in a hospital emergency engineering project [98]. Furthermore, in semantic optimization research, AI has been proposed to normalize and automate semantic processing [95]. For instance, a deep learning-based approach has been introduced to create semantics for specific applications to build and maintain the underlying data in virtual 3D models or BIM [99]. Meanwhile, in layout optimization research, virtual environments are created by integrating technologies such as AI to enable stakeholders to comprehensively monitor designs and facilitate collaboration and decision-making [96].

However, how to achieve cost-effective AI solutions for the construction industry, address data security and privacy issues, and develop industry-wide standards and protocols needs to be further investigated [100].

4.3. Interoperability and Data Integration for BBi-Aided SBM

Keywords for the topic of interoperability and data integration in BBi-aided SBM have been shown in the Table 6, including “Interoperability”, “CityGML”, “Data Integration”, “Linked Data”, “Semantic Web”, “IFC”, “GIS”, “Big Data”, “Blockchain”, “Cloud Computing”, “Integration”, “Visualization”, “Digital Twin”, and “Smart Building”. In the context of big data, the design, construction, and operation processes of construction projects are advanced by various information technologies, in which the amount of available data has greatly increased. However, as an information storage and exchange platform, BIM contains a large amount of non-geometric information, such as procurement information, material characteristics, and component cost [44]. Therefore, a holistic and scalable data management system is needed to address the problems of data heterogeneity and data sharing in the construction industry [101].

4.3.1. Common Collaboration Standard

Currently, IFC and CityGML are two of the most important semantic models that are used to represent architectural objects and geospatial models [102]. As the importance of the full digitalization of the construction industry has been recognized, the conversion between IFC and CityGML is seen as a necessary step for sharing and exchanging information between architectural objects and geospatial objects [102]. Hence, extended models for IFC and CityGML facilitate the modeling and visualization tasks required for construction projects [103,104]. Several studies have attempted to develop intermediate models for information exchange between IFC and CityGML, where core models contain common elements for model transformation, reducing the information overload problems [102,105,106], which also directly optimizes the project coordination efficiency KPI.

In addition, recent studies explored the need for the integration of BIM and GIS in smart cities as well as digital twin cities [107]. Furthermore, Zhu et al. [108] suggested the integration of BIM and GIS by proposing the shapefile format, which is beneficial to the research of digital twins and smart cities. Similarly, Pan et al. [107] proposed an innovative scheme based on hierarchical data formats to further improve the responsiveness of services in digital twin applications. Meanwhile, in terms of interoperability, automatic conversion of data conforming to different specifications is the key to interoperability between heterogeneous systems [109]. However, the current research still does not fully address the issue of interoperability for all applications in the construction industry [44], and fewer data conversion methods are only applicable to a few specific cases [110].

4.3.2. Technologies for Managing Semantics

• Semantic Web

The interoperability between building models and data based on the semantic web is a current research trend [111]. From 2010 to 2024, the relevant research of the semantic web has made significant progress in building sustainability assessment [112], building supply chain [100], and construction cost estimation [113]. Representation of BIM models through open standards (e.g., IFC, semantic web, and digital twin) has been popular in facilitating data conversion when exchanging building data with other software [114]. Niknam et al. [113] suggested a semantic-based estimation application that integrates information from BIM and material suppliers to perform cost estimation of material resources. In addition, the use of the semantic web in smart cities has also received more attention, with important progress in the integration of the semantic web and IoT [112,115]. Interestingly, Kuster et al. [112] developed an enhanced semantic data model for sensor network data to support real-time urban sustainability assessment. Similarly, Howell et al. [115] established a semantic managed service and domain ontology, integrated with the IoT, to provide value-added services to consumers and contractors by associating the water system of a city with a large data model such as the waste network. In addition, semantic web technologies and visual programming languages can be used for automated product data processing and integration in BIM software [116], as well as for integrating domain knowledge, BIM information, and IoT sensing data to form digital twin data models, and can also support efficient information acquisition, extraction, validation, and updating [117].

• Linked Data

Building data represented by linked data can be integrated with other data from related cross-disciplinary areas [118]. Recent studies have investigated the potential of linked data to improve construction project procurement [119], construction project management [120], and building performance management [121]. For example, Curry et al. [25] proposed using linked data as the basis for integration and exchange of building data services to create a complete project-related info-graphic for managing buildings. In addition, He et al. [119] developed an e-platform for construction procurement based on linked data, using BIM and linked data to connect various data sources to achieve e-procurement and overcome the problem of data integration. Furthermore, Zhang et al. [121] integrated KPI formulas with building data from a semantic perspective and proposed an automatic ontology-based calculation method to support building energy assessment. The linked data has been deemed as an important part of the research on the development of BIM, facilitating digital twin [122]. However, there is no unified standard for the structure of the data model for diverse integration needs [123]. The currently developed systems have some limitations that may be addressed by deep learning-based methods [124].

4.3.3. Cloud Computing

The application of cloud computing overcomes the problem of the high volume of BIM data and enhances the synergy of BIM projects to sustainability [78]. Bello et al. [125] summarized the application of cloud computing in the SBM and found that cloud computing is the basis for the implementation of other emerging technologies, such as BIM, virtual reality, augmented reality, and big data analytics. Cloud computing assists in the integration of BIM with real-time data from other information technologies, addressing the problem of process opacity caused by discrete information [78], and improving construction and operational efficiency [126]. A great deal of research have focused on the integration of cloud computing with BIM, of which a “Cloud + BIM” platform based on IFC has been proposed [127], and BIM and business process models have been integrated with a unified modeling language based on cloud computing to facilitate team collaboration and data management issues during the building life cycle [128]. Similarly, BIM and cloud computing have been integrated to upgrade automated, rapid-response building fire protection systems, saving firefighters and rescuers a lot of time [129]. Furthermore, recent research has focused on connecting cloud computing with emerging technologies and BIM models with facility management software and improving construction industry efficiency by enhancing the understanding of cloud implementation drivers and their impact on construction activities [130,131].

4.3.4. Data Integration and Visualization

BIM datasets contain a large number of different pieces of information that are generated and used by various experts throughout the life cycle of the project [132]. Irizarry et al. [133] suggested that data and model integration have two levels: the fundamental level and the application level, of which the foundation level focuses more on the exchange standards of heterogeneous data and the interoperability of models, while the application level concerns the application of software models and the potential for visualization. Rajabifard et al. [134] further proposed a three-level framework for the data and model integration, such as application level, process level, and data level, and the two levels of integration are built on the basis of the data-level integration that is the core part, and the integrated output can be used for data analysis and visualization.

Moreover, the integration of GIS and BIM has attracted a lot of attention [135]. For example, the spatial geographic information and data hierarchical processing capabilities of GIS can make up for the shortcomings of BIM in urban applications and engineering fields [136], and the integration of GIS and BIM throughout the project life cycle has a number of advanced features such as reducing costs and providing accurate digital design of projects [135]. However, there is no data standard specifically for the integration of BIM and GIS [136].

Furthermore, data visualization presents the analysis results of big data vividly and helps users to make in-depth insights and decisions [78]. By and large, current data visualization practice in the field of BBi-aided SBM mainly includes graphical visualization, intelligent visualization, and technology auxiliary data visualization. The design relationships between model components in BIM are complex, and graphical visualization of their dimensions and the relationships between structures can better assist participants in comprehending project details [137,138]. In addition, since stakeholders using BIM datasets include project contractors, construction planners, and schematic designers, who have different requirements for the datasets in BIM, intelligent visualization can automatically coordinate the different task requirements and goals [139], displaying data information regarding various interested users [140]. Data conversion using data structures from different fields, such as IFC, CityGML, and Web Ontology Framework, can further enhance the interoperability and visualization of BIM data [141].

4.3.5. Data Management Optimization

The most notable feature of data management is the application of digital technologies, such as blockchain, digital twin, and big data, which have great potential for collaboration, data sharing, efficiency, and improved sustainability in the construction industry [77].

Digital twin and BIM are two complementary concepts that, integrating data from the BIM model into the digital twin, can provide cost-effective decision-making solutions for projects [142]. Digital twin provides users with the ability to test the actual project solution before implementation [143]. What sets digital twins apart from other cyber–physical systems is bidirectional data exchange and real-time self-management [144]. Additionally, digital twin takes advantage of the synchronization of bidirectional data flows, which represents the complex components more comprehensively than the standardized semantics of components available in BIM [68]. Recent studies have demonstrated that digital twin has tremendous value in managing the entire building life cycle [36,145,146,147]. Furthermore, digital twin allows stakeholders to interact with real-time perception and virtual-to-real interaction in 3D models [148], increasing the transparency of project information and enabling more efficient visual management [149]. In addition, the comprehensive research field of applications of digital twins in the operation and maintenance phase of buildings is not yet mature enough in terms of implementation, operational practices, and related technical tools [150].

On the other hand, the integration of blockchain and digital twin to better manage building life cycle data to support the digital transformation of the built environment has recently received a lot of attention from researchers in the field [151]. In addition, blockchain can enhance data integrity, security, and transparency in projects, which ensures trust issues among stakeholders [75,152]. For example, decentralized databases and decentralized applications that leverage blockchain will avoid a single source of trust, providing a secure model for life cycle information exchange in an ecosystem where stakeholders interact with digital twins [153].

4.4. Data Mining and Green Building

Sustainability in the construction industry encompasses every stage of the building life cycle, from project specifications to schematic design, excavation of raw materials and procurement, processing and transportation, construction, operation and maintenance, and finally demolition. However, it is important to utilize data mining techniques to identify valid patterns in order to predict and identify factors that affect safe green building construction [154]. The current published research works on data mining and green building in BBi-aided SBM, data mining and management, smart, green building, and sustainability are explored in the sections below.

4.4.1. Data Mining and Management

In construction projects, unstructured and structured data need to be integrated, extracting knowledge and information from unstructured data such as text messages [155]. Some studies have approached structured data by preserving the model or structure of the data, while for unstructured data, it is necessary to compare the structures of different data sources and extract the important information from them [156]. Data mining performs automatic or semi-automatic quantitative analysis of unstructured data to uncover its hidden relationships [10]. For example, Lu et al. [157] applied data mining to analyze the potential relationships of unstructured data, such as construction waste information. Data management in the construction field requires an integration of knowledge from various disciplines to propose specialized solutions for different situations and projects [155]. Similarly, Motaghifard et al. leveraged data mining algorithms to more effectively predict and identify relevant data and factors affecting building safety and performance [154]. However, the current process of merging structured and unstructured data in construction projects has the problem of conflicting data fusion requirements with data privacy requirements [158].

4.4.2. Smart, Green Building and Sustainability

Construction projects are responsible for a large amount of energy consumption and carbon emissions [159]. The integration of BIM and big data helps to determine the balance between green building assessment criteria and customer needs [160]. Currently, smart buildings are considered to be a new trend in the development of green building technology, which requires the use of renewable energy as the main power source and an effective control system to achieve a high level of comfort and energy efficiency in buildings [161]. Additionally, smart buildings assist in minimizing environmental impact and make buildings more integrated, flexible, energy-efficient, intelligent, and sustainable [162]. The integration of data from various sources makes systems of smart and green buildings more extensive [163]. Furthermore, building thermal simulation models and multi-objective optimization algorithms have been employed to predict the optimal operating conditions of building HVAC systems [164], from which the project outcome minimizes energy consumption without sacrificing the thermal comfort of personnel.

At present, the demand for sustainable buildings and sustainability assessment criteria is increasing [60], and a number of various green building rating systems are applied to assess and verify the sustainability performance of buildings. BIM technology, expert review, and mathematical modeling have been used in integration to propose new methods for evaluating the greenness of buildings [165].

5. Discussion

5.1. Research Hotspots and Development Trends of the Big Data-Driven BIM in SBM

Research on big data-driven BIM in SBM has significantly increased in the last 15 years (2010 to 2024). In essence, the development trend is a process from data collection to information integration and to sustainable management. These various perspectives on trend analysis echo the trend of academic interest in BBi-aided SBM, avoiding the errors caused by applying a single bibliometric software for analysis. However, research on BBi-aided SBM is still at a rapidly evolving stage.

The current BBi-aided SBM research can be divided into four sub-themes based on the analysis of research front through the use of co-word analysis, cluster analysis, social network analysis, and knowledge evolution path, which are smart technologies for sustainable building systems, advanced technologies, interoperability and data integration, and data mining and green building. The research on BBi-aided SBM has been further classified into three hot topics, namely, business collaboration in big data-driven BIM, technology application in big data-driven BIM, and SBM and life cycle assessment. The issue of complex business collaboration is brought up in SBM with the information development of BIM. Additionally, big data-driven BIM leads to the diversity and complexity of models. Thus, information exchange and model interoperability are fundamental to enhancing business collaboration. The results of keyword co-occurrences in Section 3.2.1 and the thematic map in Section 3.2.2 indicate that semantic models and data integration have been important and well-developed themes in relation to the development of BBi-aided SBM. In the time-slice phase from 2015 to 2018, research focused on semantic extensions. During the time-slice phase year from 2019 to 2024, research hotspots shifted to data integration in the physical world and information fusion. Future research on semantic models and data integration tends to be richer in connectivity and automated processing [36,38,122]. In terms of technology application in big data-driven BIM, the integration with various information technologies (e.g., AI, digital twin, IoTs, and cloud computing) has being increased since 2019, which is supported by the results of keyword co-occurrences (Section 3.2.1) and the frequent occurrence of relevant keywords from 2019 to 2024. Moreover, current research is developing rapidly, where the focus is on the extension of information technologies in building life cycle management, such as AI, digital twin, IoT, and cloud computing [36,37,39,125]. The application of these information technologies greatly enriched the value of “big data” [163]. Another major research hotspot is BIM-based data analysis and visualization, through which the development of BBi-aided SBM would be featured with high informatization. Furthermore, in terms of SBM and life cycle assessment, the research on big data-driven BIM for SBM and life cycle assessment is gradually gaining attention in the development of sustainability themes. The results from the keyword occurrence samples (Section 3.2.1), keyword evolution (Section 3.2.2), and thematic maps (Section 3.2.2) indicate that numerous studies have concentrated on the building life cycle assessment and management aspects since 2015. However, most of the studies are in the stage of development of the theoretical framework, and the assessment of sustainable buildings is mainly focused on individual cases.

5.2. Stakeholder Value of the Big Data-Driven BIM in SBM

The results of the qualitative analysis in Section 4 suggest that the integration of BIM and big data mainly focuses on building energy efficiency optimization [44,52,58,61,113,121,130,131,142] and construction schedule prediction [46,75,76,78,79,80,154] and improving the greenness of the building [59,65,166], whereas little research has been conducted on the mechanism of stakeholder engagement. However, as the public’s attention to the application of intelligent technology increases, the requirements of stakeholders, including owners, users, designers, contractors, and workers, for intelligent building design and management have gradually increased.

Big data-driven BIM in SBM provides a common single data source for stakeholders [167], realizes information sharing among multiple roles and processes [140,141,142,156], improves the efficiency of SBM [47,48,117], and enhances the experience of users and owners [73]. For example, AI is used to collect information in project processes such as design, procurement, and construction, assist stakeholders in cost and time control, and provide sustainable technical support to stakeholders in the subsequent operation and maintenance phases (Section 4.2.2). Intelligent data visualization has been deployed to intuitively present the results of big data analysis, help stakeholders to make further insights and decisions, and provide different stakeholders with the data information they need from various perspectives (Section 4.3.4). Digital twins and blockchain technologies have been employed to ensure data security, integrity, and transparency, thereby enhancing trust between stakeholders (Section 4.3.5) and safeguarding the interests and needs of different stakeholders (Section 4.1.2). In addition, related technologies can be integrated to help assess and maintain the greenness of a building, maintaining a balance between green standards and customer needs (Section 4.4.2). Therefore, if big data-driven BIM is to serve the stakeholders, a holistic and scalable data management system is needed to address the issue of data sharing in the construction industry, and the technical training of the practitioners should be enhanced.

The value of big data-driven BIM in SBM is increasingly suggested via its impact on KPIs. For stakeholders, this integration enhances equipment availability through predictive maintenance, improves the energy cost ratio via BIM–IoT energy management, and optimizes cost deviation through AI-based budgeting (Section 4.1.2 and Section 4.2.2). Moreover, it enhances coordination efficiency through model integration and enables automatic calculation of KPI from semantic building data to support energy performance assessment (Section 4.3.1 and Section 4.3.2). These demonstrate how big data not only supports technical functions but also drives measurable business outcomes across the building life cycle.

5.3. Potential Directions and Opportunities of the BBi-Aided SBM

Potential future research directions can be identified from the results of the cluster analysis (Figure 7 and Table 6) for the BBi-aided SBM, as shown in Figure 9, which include the following:

(1) Differences in data integration and model interoperability across the building life cycle in cluster 1, smart technologies for sustainable building systems in BBi-aided SBM, cluster 2, advanced technologies of BBi-aided SBM, and cluster 3, interoperability and data integration for BBi-aided SBM.

Semantic models and data integration in BIM are well-developed topics based on the results of the quantitative and qualitative analysis. However, most of the research is focused on the semantics and data integration across the phases of the building life cycle. In addition, research is limited for various file formats and data standard types [168], and whole life cycle-based data standards and interoperability models have not been fully developed. Furthermore, the transmission and management of data types need more standards in the near future.

(2) Lack of model transferability in the application of technology to the BBi-aided SBM from cluster 1, smart technologies for sustainable building systems in BBi-aided SBM, and cluster 3, interoperability and data integration for BBi-aided SBM.

The integration of BIM with various emerging technologies is insufficient due to its newly emerged status. Most of the studies on the application of technology are implemented to a specific type of project, which could not be applied to other projects [65]. Various modeling approaches have been developed and validated on different datasets, which makes it difficult to compare.

(3) Lack of a holistic sustainability performance framework to support sustainability assessment throughout the building life cycle via cluster 1, smart technologies for sustainable building systems in BBi-aided SBM, and cluster 4, data mining and green building.

Although numerous studies have paid attention to the aspects of building life cycle assessment and management, the current research on BBi-aided SBM is immature. There are two major problems with the current SBM and whole life cycle sustainability assessment. First, the process of green building assessment projects usually involves multiple stakeholders, which may lead to a cumbersome assessment process [168,169] and loss of important information [167]. Second, the research seems to lack a comprehensive sustainability assessment framework based on the whole life cycle.

5.4. Comparative Analysis with Past Review Studies

Although past review studies have extensively explored components such as BIM [63,65], big data and digital technologies [65,80,135], and sustainable building management [58,162,168] individually, this paper presents three key distinctions that contribute to the state of knowledge in this domain:

Past reviews predominantly focused on the impact of single technologies on building management, such as BIM’s role in each stage of building management [59,61], or on a single aspect of building management [64], whilst this paper highlights the integrated use of multiple technologies including BIM, big data, digital twins, and blockchain and how this convergence generates synergistic value across multiple life cycle phases, from design through construction to facility operations.

Moreover, traditional literature reviews in this domain have typically depended on manual, qualitative thematic syntheses or used a single bibliometric visualization software, which are vulnerable to subjectivity and limited scalability [69,80]. In contrast, this study advances methodological rigor by employing a mixed-methods approach that integrates multiple bibliometric visualization software tools, to objectively quantify knowledge clusters (Figure 7) and trace their evolution paths over time (Figure 8), along with the rest of the other visualizations. The complementary use of diverse visualization tools allows for cross-validation of patterns and enhances the depth and reliability of insights extracted from large multidisciplinary datasets.

Furthermore, this comparative perspective underscores the unique contribution of this paper by moving beyond siloed, technology-specific analyses toward a holistic and integrative understanding of cross-technology convergence opportunities in sustainable building management. This comprehensive data-driven approach not only synthesizes prior fragmented knowledge but also charts a clear trajectory for future research and practice in the smart built environment.

6. Conclusions

The integration of BIM and big data has shown great potential for collaboration, data sharing, and sustainable building management in the design and construction industry. However, it is hard to identify research gaps due to different disciplinary perspectives and various methods in the field of BBi-aided SBM. Therefore, this paper adopted a mixed quantitative and qualitative method to analyze the development of the BBi-aided SBM. Firstly, a descriptive analysis of the chronological division, journal impact, and subject area of the relevant past studies has been performed by collecting data from the WoS database. Additionally, the knowledge structure has been mapped using co-word analysis, which is followed by a knowledge evolution path analysis on BBi-aided SBM. Subsequently, the clusters and findings derived from prior studies are summarized and categorized, outlining the research, with gaps in the past 15 years (from 2010 to 2024). The main findings are as follows:

(1) Since 2010, research has increased significantly on a yearly basis, reaching a 50-fold increase in 2024. The research areas cover a wide range of disciplines such as computer science, engineering, urban planning, and construction. Emerging information technology-associated keywords have high centrality, such as “BIM”, “digital twin”, “IoT”, “IFC”, “data mining”, “data model”, “cloud computing”, and “AI”.

(2) The knowledge evolution path has revealed how the BBi-aided SBM research has evolved over time, and the research has undergone significant changes from the application of BIM in construction projects to big data analysis, algorithm research, and semantic integration. It has also expanded to include the integration and interoperability of BIM with information technology, the integration of virtual models with physical buildings, and sustainable management throughout the whole building life cycle.

(3) Four existing research themes have been identified for the BBi-aided SBM, namely, smart technologies for sustainable building systems, advanced technologies, interoperability and data integration, and data mining and green building.

(4) There are still barriers to information transmission, model transferability, and a comprehensive sustainability assessment framework during the whole life cycle, which need to clarify stakeholder engagement mechanisms, integrate a scalable data management system for data sharing, and enhance practitioner training to enable big data-driven BIM to serve stakeholders effectively.

The main contributions of this paper include the following: (1) a comprehensive method has been proposed to serve as a reference for the future study, which facilitates and reveals the knowledge structure and evolution trends, research directions, and future trends in the BBi-aided SBM; (2) instead of relying on a single bibliometric tool, this paper employs a mixed method of quantitative and qualitative analysis to explore reliable research trends on the BBi-aided SBM from multiple bibliometric perspectives, which improves the analysis methods and enables the extraction of valuable information from a large amount of data by integrating multiple bibliometric analysis tools; and (3) this paper explores the application and challenges of the BBi-aided SBM throughout the entire project life cycle, where the integration of BIM and big data offers a multidimensional upgrade and breakthrough path in the field of SBM.

However, this paper has limitations. The analysis was conducted using only the WoS database, which offers comprehensive coverage of high-impact journals and may not fully obtain all relevant research in the rapidly evolving field of BBi-aided SBM. Future research could broaden the data sources to enhance the comprehensiveness and timeliness of the analysis. Although relevant keywords were included in the search, there may be different terms representing the same meaning, potentially affecting the retrieval results. Further refinement of the search strategy using synonym expansion and expert validation could improve coverage. The current analysis provides limited clarification regarding the nature of the reviewed studies, specifically, whether they primarily represent theoretical advancements, conceptual models, or real-world implementations. A deeper classification of the literature based on its empirical grounding, case-based application, or purely theoretical contribution could offer more nuanced insights for practitioners and scholars. The geographical distribution of the research is not fully explored. A regional geographical breakdown could help identify global trends, leaders, and gaps in BBi-aided SBM adoption. Furthermore, leveraging research in circular economy and building life cycle assessment, a more comprehensive analysis of the potential of big data-driven BIM in sustainable building management could be conducted, focusing on promoting economic benefits and environmental protection.