Deciphering Building Information Modeling Evolution: A Comprehensive Scientometric Analysis across Lifecycle Stages

Abstract

Building Information Modeling (BIM) has catalyzed transformative shifts across various industries, which has sparked broader research interests in the BIM lifecycle. However, studies that specify the stated requirements for different technologies and methodologies from the perspective of the BIM lifecycle and analyze research hotspots and future research trends at each stage are scarce. Employing scientometric theories and methods, this study conducts an in-depth comparative analysis of BIM lifecycle stages. The analysis encompasses several aspects like annual research output and knowledge flows, in the aim of unveiling disparities in the technological requirements, defining research boundaries, and illuminating lifecycle research trends. Findings indicate an ongoing surge in research across all BIM lifecycle stages with technologies like digital twins and artificial intelligence becoming prevailing trends. The cooperative design of BIM components, virtual-real world coordination, interactions among buildings, individuals, and environments, as well as the in-depth integration of BIM with the multifaceted fields of urban management have emerged as focal points in the planning, construction, management, and maintenance of BIM, respectively. Future BIM lifecycle research will necessitate interdisciplinary collaboration, emphasizing technological integration, common data environment (CDE) information sharing, open-source BIM/historic building information modeling (HBIM) system, and impactful exploration in areas like urban construction and historical preservation.

1. Introduction

Building information modeling (BIM) is recognized as an innovative technology and management approach, as well as a multidimensional, dynamic architectural design tool, through which digital simulation platforms are provided for construction projects [1,2,3,4]. The BIM lifecycle involves utilizing BIM technology in each stage of a building project, which includes project conceptualization, design, construction, operation, maintenance, and final deconstruction or repurposing [5,6,7]. BIM lifecycle research aims to guarantee the availability of accurate and timely architectural information at each stage, thereby enhancing project efficiency, reducing waste, and ensuring the long-term operation and environmental sustainability of the building [8,9,10,11].

In recent decades, the academic community has undertaken comprehensive, meticulous studies of the BIM lifecycle, exploring every facet of it and consequently spawning numerous specialized research subdomains relevant to the BIM lifecycle, including implementation [5,12], management [4,13], research progress [6,14], and the semantic integration of BIM [7,15]. Therefore, some scholars have endeavored to conduct comprehensive reviews of the specific subdomains of BIM to assist researchers (especially those newly entering the field) in gaining an overarching understanding of the origins, current status, and achievements of these research areas [5,9,13]. Nevertheless, due to the multitude of BIM subdomains, along with disparities in scholars’ knowledge backgrounds and personal constraints, conducting a comprehensive and objective analysis and evaluation of BIM without quantitative methodologies is challenging. Additionally, in practical applications, the unclear role of BIM throughout the building lifecycle can lead to issues such as uneven resource allocation, chaotic information management, and limited efficiency in project collaboration [1,5,10]. All things considered, it is necessary to explore a quantitative analytical method that can accurately delineate the role of BIM at each stage in the building lifecycle to accurately discern the boundaries of each stage in the BIM lifecycle and thereby carry out a comprehensive and objective analysis and evaluation of the BIM lifecycle.

Fortunately, scientometrics provides a data-driven, quantitative approach to understanding the current state and potential future directions of a field, utilizing mathematical methods to evaluate scientific achievements and describe the structure and internal mechanisms of scientific systems [6,16]. This reveals the spatiotemporal characteristics of scientific development, identifies disciplinary boundaries, and seeks to discern the quantitative regularities of scientific activities within the context of human society [11,17,18,19]. Currently, scientometrics enables precise analyses and risk assessments of specific fields, authors, journals, and even specific articles [16,20]. For analyses of the scientific knowledge system, scientometrics provides not only real-time monitoring of the evolution of scientific knowledge but also a comprehensive grasp of the knowledge structure of the field. For field-risk assessment, scientometrics is capable of revealing emerging subfields and development trends [21,22]. More importantly, scientometrics is data-driven; that is, its analysis results are heavily dependent on data, which indicates the strong objectivity of the results.

In summary, employing descriptive data-driven analysis and network-based analysis within the framework of scientometrics, this study conducts comparative analyses of the characteristics of each stage of the BIM lifecycle. Through these comparisons, we try to identify the characteristics of BIM studies designed for each stage of the building lifecycle, unraveling its theoretical foundation and knowledge flow to decipher the evolution of BIM lifecycle research. This paper is structured as follows: Section 2 introduces data sources and collection methods; Section 3 describes the adopted models and methods; Section 4 elaborates on the results from the perspectives of scientometrical indicators and network associations; Section 5 discusses future research directions; and Section 6 concludes the paper.

2. Data

2.1. BIM Lifecycle

BIM can be applied to the entire lifecycle of buildings, from the initial planning and design to maintenance and operation [1,7,11,15,23]. Its applications in each stage across the building lifecycle stages are demonstrated in Figure 1.

As shown in Figure 1, at the planning and design stage, BIM enables functions such as needs analysis and the creation of accurate digital building models to optimize the design [17,24]. At the stage of manufacturing and supply, the academic community places greater emphasis on the digital modeling of building components for intelligent manufacturing and the optimization of the supply chain for related component products [17,24,25]. For construction, BIM benefits this stage from model conversion to construction quality control [26,27,28,29]. For facility management, the as-built BIM provides extensive digital asset data and the real-time monitoring of building systems, supporting an emergency management response [1,15,24,25]. In addition, the as-built BIM is updated with the latest operational data to inform maintenance planning, evaluate renovation or demolition options, and update models in real time for operation optimization [1,15,17,30]. Lastly, at the deconstruction stage, the core applications of BIM include the reuse of dismantled components, the safety management of demolition projects, and the waste disposal that meets environmental protection requirements [18,31].

Although BIM plays a significant role in the manufacturing and supply as well as the demolition stages of construction projects, given that this paper mainly focuses on the construction project itself as well as its management and maintenance, and coupled with the constraints of article length, the application of BIM in planning and design, construction, project management, and maintenance and operation is the primary emphasis. The four stages are crucial for the implementation of building projects and represent key areas where BIM displays its value:

• The stage of planning and design lays the foundation for project quality, outlining the essential vision and core technical goals that the project should achieve.
• The stage of construction translates designs into physical structures. By employing appropriate technical methods, the construction process is monitored in real time, with adjustments made as necessary to ensure the quality of the actual construction.
• During the stage of management, resources are coordinated and organized efficiently, leading to the effective management of human, financial, and material resources related to the building.
• The stage of maintenance and operation is closely tied to the entire lifecycle benefits of the project. Through digital technology, the daily operational and maintenance costs of the building are further reduced.

Given their significance, this paper presents an in-depth analysis of the above four stages, aiming to delve into the core applications and value of BIM. For the convenience of writing this article, BIM-planning denotes the application of BIM in the planning stage; accordingly, BIM-construction, BIM-management and BIM-maintenance correspond to the applications in the stages of construction, management and maintenance, respectively.

Figure 1. BIM applications across lifecycle stages.

Figure 1. BIM applications across lifecycle stages.

2.2. Database

Generally, mainstream citation databases include Web of Science (WoS), Scopus, Embase, etc. [19,32]. In this study, the WoS Core Collection database was selected as the data source for the standardized academic publication citation data. It can provide nearly a hundred years of WoS bibliographic data that cover approximately 12,000 mainstream academic journals worldwide. The coverage includes the extended Science Citation Index, Social Science Citation Index, and many other citation indexes [32,33]. In addition, WoS provides not only powerful web-based access to bibliographic and citation information but also free downloadable bibliographic and citation information [6,21,22]. This database has been widely used in studies such as bibliometrics, which traces the roots of knowledge.

2.3. Data Acquisition

The five members of the research team which have long been dedicated to BIM-related research identified the commonly used research topics across all stages of the BIM lifecycle and their corresponding keywords that were searched by reviewing many BIM-related papers. Those topics were established as the potential search keywords for this study. Subsequently, the potential keywords were input into the WoS database individually for data retrieval. Their search accuracy was validated, and any noise keywords yielding excessive irrelevant results were excluded. Ultimately, based on the retrieval paradigm of the WoS database, the search criteria for this study were established, as shown in

Table 1. Search criteria given in this paper.

Theme/Stage	Search Query	Timespan	Document	Citation Indexes	Language	Count
Planning and design	“building information modeling” OR “BIM” AND (plan * OR programme OR project OR design)	1999–2023	Article	SCI-E SSCI ESCI CCR-E IC	All	3439
Construction	“building information modeling” OR “BIM” AND (construction OR develop * OR establish * OR build)					5736
Management	“building information modeling” OR “BIM” AND (management OR administration OR control OR government OR conservancy)					3143
Maintenance and operation	“building information modeling” OR “BIM” AND (maintenance OR maintain * OR preserve OR conservation OR vindicate OR safeguard OR operation *)					910

. Notably, the symbol “*” signifies a wildcard search, and the search fields encompass the theme of the articles, including titles, abstracts, and keywords. The time span was limited to the past 25 years, from 1999 to 2023, in an attempt to obtain citation data for “article” type literature in all languages from the SCI-E database, as most recognized academic papers are published in article form. The database was accessed on 1 May 2023. Finally, a total of 39,762 articles were obtained.

For the five members specializing in the study of BIM, we split four of them into two pairs, entrusting them with the task of rigorously reviewing records, spanning article titles, author names, abstracts, keywords, institutional affiliations, and publication dates. In cases of doubt over excluding a record, the pair deliberated. If consensus was not achieved, the fifth researcher intervened, providing insights and, if necessary, making the final call. This meticulous process resulted in 13,228 remaining BIM-lifecycle-related articles. Among these articles, 92.74% (12,834) were written in English, and the rest were in other languages, including German (340), Spanish (69), and Italian (29).

3. Materials and Methods

3.1. Descriptive Data-Driven Analysis

Data-driven descriptive analysis is a foundational method in scientometrics. It primarily involves extracting certain knowledge elements from the literature—such as authors, organizations, and keywords—and subsequently statistically analyzing their distribution, occurrence frequency, and citations [6,19,21]. The impact factor (IF), derived from citation frequency, is a universally recognized metric for evaluating the quality of journals or papers [16,21]. Presently, this indicator reflects not only the utility and prominence of knowledge elements within a specified period but also their academic caliber and quality [6,34]. The mathematical expression of IF is as follows [6,16,21]:

Given that the impact factor of journal t is IF(t), then

$$IF(t)=\frac{{\sum }_{i=1}^n{Cited\left(t\right)}_{m-i}}{{\sum }_{i=1}^n{Num\left(t\right)}_{m-i}}$$

(1)

where ${Cited\left(t\right)}_{m-i}$ represents the total citations in year $m$ of papers published by journal $t$ in year $m-i$; ${Num\left(t\right)}_{m-i}$ denotes the total number of papers published by journal $t$ in year $m-i$.

According to formula (1), the calculation of IF heavily relies on the publication date, the number of published articles, and the accessibility of the academic journal, while its consideration of differences in disciplines and research fields is insufficient [35]. Therefore, when comparing different disciplines or research areas, IF may not be enough to reflect the quality of publications. Considering that, it is necessary to introduce other numerical indicators.

The total local citation score (TLCS) and total global citation score (TGCS) are indicators used in the bibliometric software HistCite Pro 2.0, both of which are calculated on annual basis. The TLCS indicates the total citations of a paper in the local dataset, characterizing the importance of this paper in its research field [16]. The TGCS represents the total citations of a paper in the entire database, reflecting its influence in the entire scientific field. Normally, the TLCS is lower than the TGCS. The two indicators can help researchers identify important research topics in their own field and in the scientific field as a whole.

3.2. Network-Based Analysis

Descriptive data-driven analysis provides an intuitive and easy-to-understand way to summarize and depict the main characteristics of objects in a particular field. However, for this method, it is difficult to unearth the complex relationships among domain objects. Numerous network-driven analysis methods have been proposed to describe the interrelationships among knowledge units, such as cooperation network analysis [36], co-word network analysis [37], and literature co-citation network analysis [38].

The co-occurrence network is used to depict the interrelationships among objects (such as researchers, institutions, countries) in a specific field. In a co-occurrence network, nodes represent objects, edges represent the relationships between these objects, and the weight of an edge represents the intensity of co-occurrence [39]. For example, if institutions I₁ and I₂ collaborate to publish a paper P1, then in the co-occurrence network $N_1$, I₁ and I₂ are connected by edge $E_1$, and the weight of $E_1$ represents the cooperation intensity between I₁ and I₂.

Co-word network analysis is mainly used to study the co-occurrence relationship between keywords to measure the affinity between them. Given keywords K₁ and K₂ and paper P₁, if K₁ and K₂ appear in P₁ simultaneously, then there is a co-word relationship between K₁ and K₂. The higher the number of co-occurrences of the keywords, the greater the co-word strength [40,41]. The results of co-word network analysis can be used to reveal the hidden structure and meaning of papers.

The co-citation network refers to the network formed by two or more papers simultaneously cited in an article [39]. Co-citation network analysis can reveal the implicit correlations of domain knowledge, helping readers determine the direction and characteristics of knowledge flow [20].

In summary, the elements (keywords, citations, etc.) in the co-occurrence network, the co-word network, or the co-citation network are represented by points, and the associations between elements are represented as lines. The association strength is directly proportional to the line width. The three most commonly used metrics for quantifying the importance of nodes in a network are degree centrality, closeness centrality, and betweenness centrality.

Degree centrality mainly reflects the out-degree and in-degree of the node; closeness centrality reflects the proximity between nodes in the network; and betweenness centrality is an indicator portraying the importance of a node based on the number of shortest paths passing through that node. According to scientometrics, the shortest paths passing through a node indicate that the more important the knowledge unit characterized by that node is in the domain research [16,32]. Given that, betweenness centrality was adopted in this paper as the importance metric of network nodes. The formula of this metric is as follows [6,16]:

$$C_{betweenness}\left(t\right)={\sum }_{i\ne t\ne j}\frac{{ds}_{ij}\left(t\right)}{{ds}_{ij}}$$

(2)

where $C_{betweenness}\left(t\right)$ denotes the betweenness centrality of node $t$; ${ds}_{ij}$ is the number of shortest paths from node $i$ to node $j$; and ${ds}_{ij}\left(t\right)$ represents the number of shortest paths passing through node t among the ${ds}_{ij}$ shortest paths from node $i$ to node $j$.

4. Results

4.1. Descriptive Data-Driven Analysis

4.1.1. Publication Outputs

The number of publications is an important indicator of research popularity. Utilizing this indicator as well as the TLCS and TGCS, this study quantitively evaluated the activity of studies related to the BIM lifecycle, as well as their field and global influence [6,16], as illustrated in Figure 2.

For BIM-planning (Figure 2a), its related research first appeared in 1999, and its TLCS and TLCS both peaked in 2013 and 2015. Bryde et al. (2013) [42] and Bosche et al. (2015) [43] are the articles with the highest TLCS and TGCS during the two periods. In 2017, Liu et al. (2017) [26] had the highest TLCS. In 2020, BIM research attracted the attention of the entire scientific community, with Alizadehsalehi et al. (2020) [44] having the highest TGCS.

For articles related to BIM-construction (Figure 2b), the first publication was in 2000, and there has been a gradual increase in the number of publications since 2008. The peaks of both the TLCS and TGCS of BIM-construction occurred in 2011, 2013, and 2015. During the three periods, the following articles had the highest citation scores: Singh et al. (2011) [45], Bryde et al. (2013) [42], and Bosche et al. (2015) [43]. Then, in 2018, the TLCS reached its highest peak during the study period, and Li et al. (2018) [46] became the most influential article of the year. The highest peak of the TGCS occurred one year later; in that year, the most influential interdisciplinary article was by Tan et al. (2019) [2].

In the field of BIM-management (Figure 2c), related articles have been published since 2001. Arayici et al. (2011) [47] and Singh et al. (2011) [45] made the most significant contributions to the first peaks of the TLCS and TGCS (2011). Their second peaks both occurred in 2013, with Bryde et al. (2013) [42] and Eadie et al. (2013) [48] having the highest scores. In 2015, the articles that contributed the most to the two single indicators were Kang et al. (2015) [49] and Wang et al. (2015) [50]. For the peak occurring in 2019, Tan et al. (2019) [2] made the largest contribution.

The first article related to BIM-maintenance (Figure 2d) appeared in 2003; ten years later, the first peaks of both the TLCS and TGCS in this field occurred, with Motawa et al. (2013) [51] and Xiong et al. (2013) [52] contributing the most. After a brief decline (2014–2016), the TLCS reached its highest peak in 2018, and Chen et al. (2018) [15] was the article with the highest TLCS of the year. Then, in 2019, the highest TGCS peak also appeared, with Khajavi et al. (2019) [53] obtaining the highest score.

Notably, although the TLCS and TGCS for the four stages showed a continuous downward trend after 2019, this does not mean that the academic community lost interest in related research. In contrast, in the years following 2019, the annual publication volume of articles remained relatively stable. One possible reason for this is that it takes time for papers to gain citations after publication. Generally, the number of citations of an article is directly proportional to its publication date. Within a certain time, the earlier the article is published, the more times it will be cited. An important reason for the overall low number of literature publications in 2013 is that the data for this study were downloaded in May 2023, and many of the articles may not have been published yet.

By comparing the four histograms, it can be observed that BIM-construction-related literature had not only the largest number of publications but also the highest TLCS and TGCS. A major reason for this is that BIM is currently being used on a large scale, which makes it a sunrise industry. Interestingly, the trends in the TGCS for the four stages from 2017 to 2020 were similar, indicating that research in the four stages has received widespread attention across the scientific community since 2017. Additionally, the overall trends of the TGCS and TLCS curves show that although they did not have an intersection point (except for 2023), there was a certain degree of dynamic pattern similarity with a slight delay. This similarity may result in overlapping articles in the two fields. Hence, a search for publications covering all four stages of BIM at the same time was also conducted.

A total of 333 overlapping papers were obtained. By reviewing these overlapping publications, it was found that there were eight papers after 2011 with a TGCS exceeding 100 (see [1,15,17,24,54,55,56,57,58]). Among BIM-related studies, BIM lifecycle research is an important research direction, while research on the four stages also has its own specific research hotspots.

Figure 2. Temporal distribution of the output of BIM lifecycle papers.

Figure 2. Temporal distribution of the output of BIM lifecycle papers.

4.1.2. Active Journals

Academic journals serve as important showcases for research achievements, disseminating domain knowledge, and organizing academic activities.

Table 2. The most active journals in the field of BIM-planning research.

Journal	Recs	TLCS	TGCS	Journal	Recs	TLCS	TGCS	Journal	Recs	TLCS	TGCS
Automation in Construction	423	5447	20,122	Automation in Construction	423	5447	20,122	Automation in Construction	423	5447	20,122
Sustainability	184	0	1571	Advanced Engineering Informatics	78	733	2814	Journal of Construction Engineering and Management	120	225	2895
Buildings	154	0	755	International Journal of Project Management	13	699	1655	Advanced Engineering Informatics	78	733	2814
Engineering Construction and Architectural Management	120	553	1734	Engineering Construction and Architectural Management	120	553	1734	Journal of Computing in Civil Engineering	71	166	2039
Journal of Construction Engineering and Management	120	225	2895	Journal of Civil Engineering and Management	44	350	856	Journal of Management in Engineering	62	100	1999
Applied Sciences-Basel	117	0	885	Journal of Cleaner Production	33	244	1513	Engineering Construction and Architectural Management	120	553	1734
Advanced Engineering Informatics	78	733	2814	Journal of Building Engineering	58	243	1392	International Journal of Project Management	13	699	1655
Journal of Information Technology in Construction	77	130	410	Journal of Construction Engineering and Management	120	225	2895	Sustainability	184	0	1571
Journal of Computing in Civil Engineering	71	166	2039	Journal of Computing in Civil Engineering	71	166	2039	Journal of Cleaner Production	33	244	1513
International Journal of Construction Management	67	151	938	Building Research and Information	16	161	389	Journal of Building Engineering	58	243	1392

Note: Recs denotes the number of articles published in the journal. TLCS represents the total local citation score, and TGCS represents the total global citation score. The columns are sorted in descending order: the left for Recs, the middle for TLCS, and the right for TGCS.

and

Table A1. The most active journals in in the field of BIM-construction research.

Journal	Recs	TLCS	TGCS	Journal	Recs	TLCS	TGCS	Journal	Recs	TLCS	TGCS
Automation in Construction	705	11,843	31,813	Automation in Construction	705	11,843	31,813	Automation in Construction	705	11,843	31,813
Sustainability	292	36	2593	Advanced Engineering Informatics	135	1727	4630	Advanced Engineering Informatics	135	1727	4630
Buildings	235	0	1250	Engineering Construction and Architectural Management	154	912	2095	Journal of Construction Engineering and Management	161	334	4170
Applied Sciences-Basel	182	0	1641	International Journal of Project Management	13	871	1651	Journal of Computing in Civil Engineering	121	378	2993
Journal of Construction Engineering and Management	161	334	4170	Energy and Buildings	53	595	1652	Journal of Cleaner Production	68	558	2867
Engineering Construction and Architectural Management	154	912	2095	Journal of Civil Engineering and Management	64	562	1070	Sustainability	292	36	2593
Advanced Engineering Informatics	135	1727	4630	Journal of Cleaner Production	68	558	2867	Journal of Management in Engineering	77	110	2246
Journal of Computing in Civil Engineering	121	378	2993	Building and Environment	51	465	1799	Engineering Construction and Architectural Management	154	912	2095
Journal of Information Technology in Construction	108	276	582	Journal of Computing in Civil Engineering	121	378	2993	Journal of Building Engineering	99	360	1867
Journal of Building Engineering	99	360	1867	Journal of Building Engineering	99	360	1867	Building and Environment	51	465	1799

,

Table A2. The most active journals in in the field of BIM-management research.

Journal	Recs	TLCS	TGCS	Journal	Recs	TLCS	TGCS	Journal	Recs	TLCS	TGCS
Automation in Construction	351	4334	17,042	Automation in Construction	351	4334	17,042	Automation in Construction	351	4334	17,042
Sustainability	168	0	1525	Engineering Construction and Architectural Management	118	591	1748	Journal of Construction Engineering and Management	104	180	3075
Buildings	151	0	763	International Journal of Project Management	10	528	1446	Advanced Engineering Informatics	57	458	1980
Engineering Construction and Architectural Management	118	591	1748	Advanced Engineering Informatics	57	458	1980	Engineering Construction and Architectural Management	118	591	1748
Applied Sciences-Basel	108	0	879	Journal of Civil Engineering and Management	45	365	875	Journal of Management in Engineering	53	37	1649
Journal of Construction Engineering and Management	104	180	3075	Journal of Cleaner Production	32	226	1631	Journal of Cleaner Production	32	226	1631
Journal of Information Technology in Construction	64	101	371	Safety Science	14	186	820	Journal of Computing in Civil Engineering	60	109	1589
Journal of Computing in Civil Engineering	60	109	1589	Journal of Construction Engineering and Management	104	180	3075	Sustainability	168	0	1525
Advanced Engineering Informatics	57	458	1980	Computers in Industry	20	142	1087	International Journal of Project Management	10	528	1446
Advances in Civil Engineering	53	0	485	Building Research and Information	14	139	389	Computers in Industry	20	142	1087

and

Table A3. The most active journals in in the field of BIM-maintain research.

Journal	Recs	TLCS	TGCS	Journal	Recs	TLCS	TGCS	Journal	Recs	TLCS	TGCS
Automation in Construction	94	447	4257	Automation in Construction	94	447	4257	Automation in Construction	94	447	4257
Sustainability	64	0	623	Advanced Engineering Informatics	12	46	496	Sustainability	64	0	623
Applied Sciences-Basel	40	0	307	Advances in Engineering Software	4	46	205	Advanced Engineering Informatics	12	46	496
Buildings	31	0	244	Facilities	17	27	139	Journal of Management in Engineering	12	0	373
Journal of Information Technology in Construction	20	13	140	International Journal of Architectural Heritage	9	25	180	Applied Sciences-Basel	40	0	307
Bautechnik	18	13	66	Journal of Cultural Heritage	5	25	128	Journal of Cleaner Production	8	10	265
Facilities	17	27	139	Building and Environment	7	23	183	Buildings	31	0	244
Journal of Building Engineering	16	8	216	Computers in Industry	5	21	149	Energy and Buildings	11	12	232
Journal of Computing in Civil Engineering	16	2	141	International Journal of Building Pathology and Adaptation	12	17	150	Journal of Performance of Constructed Facilities	13	2	225
Advances in Civil Engineering	14	0	159	Applied Geomatics	6	17	150	Journal of Building Engineering	16	8	216

depict the key journals that publish BIM lifecycle research. In the four tables, there are seven overlapping journals. On the other hand, sixteen journals only published articles related to one stage. Automation in Construction is the journal with not only the highest activity in the field of BIM lifecycle research but also the highest TLCS and TGCS, which suggests that this journal has been recognized not only by its field but also by the entire scientific community. Although Sustainability and Buildings have published many studies on the BIM lifecycle, their influence across the industry needs to be improved.

In the field of BIM-planning, in addition to Automation in Construction, there were four other journals ranking in the top ten in all three metrics: the number of published articles, TLCS, and TGCS (Table 2). The four journals are Advanced Engineering Informatics, Engineering Construction and Architectural Management, Journal of Computing in Civil Engineering, and Journal of Construction Engineering and Management. Another noteworthy journal is Building Research and Information. Although it only published 16 articles related to BIM-planning, it obtained a TLCS of 161, indicating high journal awareness in its field. Similarly, journals such as International Journal of Project Management and Journal of Cleaner Production also ranked high in both TLCS (third, sixth) and TGCS (seventh, ninth), with quite a small number of published articles (13, 33), which suggests the high recognition they receive from not only their fields but also the entire scientific community.

In the field of BIM-construction research, in addition to Automation in Construction, there are four other journals—Advanced Engineering Informatics, Engineering Construction and Architectural Management, Journal of Building Engineering, and Journal of Computing in Civil Engineering—that ranked in the top ten in terms of the number of articles published, TLCS, and TGCS (Table A1). This indicates their wide recognition in the field of BIM-management research both within their field and in the wider scientific community. The International Journal of Project Management, with fewer than 15 articles published, ranked fourth in TLCS, suggesting that it is also an important journal in the field of BIM-management research.

Among journals concerning BIM-management, Advanced Engineering Informatics, Engineering Construction and Architectural Management, and Journal of Construction Engineering and Management all rank in the top ten in all three aforementioned indicators, like Automation in Construction, and the high ranks showcase their importance in the field of BIM-management research (Table A2). Additionally, apart from the four journals, International Journal of Project Management, Advanced Engineering Informatics, Safety Science, and Building Research and Information were also considered journals with the most significant influence in the field. Among them, International Journal of Project Management ranked 4th in TLCS and 10th in TGCS, with only 10 published articles.

For research on BIM-maintenance, the numbers of published papers were generally low compared with those in the other three stages. According to Table A3, seven of the top ten journals in TLCS published fewer than ten related articles. Judging from TLCS, journals such as Advances in Engineering Software, Journal of Cultural Heritage, and Computers in Industry have high domain influences and good industry reputations in the research field of BIM-maintenance. Another thing worth noting in Table A3 is that there are five journals with less than 10 published papers ranking in the top 10 in TGCS, which hints that the BIM-maintenance papers published by the five journals have a higher probability of attracting attention from the entire scientific community.

4.1.3. Active Institutions

In the research field of BIM-planning, Hong Kong Polytechnic University led with 98 papers (

Table 3. The most active institutions in the field of BIM-planning research.

Institution	Recs	TLCS	TGCS	Institution	Recs	TLCS	TGCS	Institution	Recs	TLCS	TGCS
Hong Kong Polytechnic University	98	742	3518	Hong Kong Polytechnic University	98	742	3518	Hong Kong Polytechnic University	98	742	3518
University of Hong Kong	62	419	2112	Georgia Institute of Technology	46	699	2735	Georgia Institute of Technology	46	699	2735
Curtin University	58	466	2313	Curtin University	58	466	2313	Curtin University	58	466	2313
Tongji University	54	343	1477	University of Hong Kong	62	419	2112	University of Hong Kong	62	419	2112
Polytechnic University of Milan	47	54	440	University of Salford	22	393	1004	Tsinghua University	40	300	1868
Deakin University	46	388	1697	Deakin University	46	388	1697	Deakin University	46	388	1697
Georgia Institute of Technology	46	699	2735	Huazhong Univeristy of Science and Technology (HUST)	41	377	1559	HUST	41	377	1559
National University of Singapore	46	282	1500	Kyung Hee University	27	373	1419	National University of Singapore	46	282	1500
Hanyang University	42	257	1098	University Newcastle	6	372	931	Tongji University	54	343	1477
HUST	41	377	1559	Technion Israel Institute of Technology	16	352	1230	Kyung Hee University	27	373	1419

Note: Recs represents the number of articles published by the institution, TLCS indicates the total local citation score, and TGCS indicates the total global citation score. The columns are sorted in descending order: the left for Recs, the middle for TLCS, and the right for TGCS.

) and the highest TLCS (742) and TGCS (3518), which displays its dominant influence in this research field. The Georgia Institute of Technology and Curtin University followed Hong Kong Polytechnic University in both the TLCS and TGCS, demonstrating their significance in the field as well as in the scientific community. Additionally, although the University of Newcastle published only six relevant papers, it obtained a rather high TLCS (372), which indicates the high domain influence of the papers it has published.

For research on BIM-construction, the Hong Kong Polytechnic University, the University of Hong Kong, and Curtin University were the top three institutions in terms of the number of publications (

Table A4. The most active institutions in the field of BIM-construction research.

Institution	Recs	TLCS	TGCS	Institution	Recs	TLCS	TGCS	Institution	Recs	TLCS	TGCS
Hong Kong Polytech Univ	142	1080	4290	Georgia Inst Technol	72	1564	4269	Hong Kong Polytech Univ	142	1080	4290
Univ Hong Kong	94	741	2835	Hong Kong Polytech Univ	142	1080	4290	Georgia Inst Technol	72	1564	4269
Curtin Univ	93	1024	3763	Curtin Univ	93	1024	3763	Curtin Univ	93	1024	3763
Tongji Univ	89	556	2013	Univ Newcastle	8	921	1763	Univ Hong Kong	94	741	2835
Natl Univ Singapore	78	481	2108	Technion Israel Inst Technol	38	860	2126	Tsinghua Univ	73	744	2626
Tsinghua Univ	73	744	2626	Kyung Hee Univ	49	832	2377	Kyung Hee Univ	49	832	2377
Georgia Inst Technol	72	1564	4269	Tsinghua Univ	73	744	2626	Technion Israel Inst Technol	38	860	2126
Southeast Univ	70	90	816	Univ Hong Kong	94	741	2835	Natl Univ Singapore	78	481	2108
Politecn Milan	67	229	881	Univ Salford	33	722	1544	HKUST	63	612	2074
HKUST	66	644	1965	HKUST	66	644	1965	Tongji Univ	89	556	2013

). Judging from the TLCS and TGCS, Hong Kong Polytechnic University and the Georgia Institute of Technology held the top positions in all, which suggests their high academic reputation. Interestingly, the activity of the University of Newcastle in BIM-construction research was quite similar to that in BIM-planning research. This institution ranked fourth in the TLCS with only eight relevant articles, demonstrating that it also gained high recognition in the field.

In the field of BIM-management research, Hong Kong Polytechnic University, Curtin University, and University of Hong Kong ranked highest in

Table A5. The most active institutions in the field of BIM-management research.

Institution	Recs	TLCS	TGCS	Institution	Recs	TLCS	TGCS	Institution	Recs	TLCS	TGCS
Hong Kong Polytech Univ	88	541	3163	Curtin Univ	68	606	2739	Hong Kong Polytech Univ	88	541	3163
Curtin Univ	68	606	2739	Hong Kong Polytech Univ	88	541	3163	Curtin Univ	68	606	2739
Univ Hong Kong	59	469	2275	Univ Hong Kong	59	469	2275	Univ Hong Kong	59	469	2275
Tongji Univ	55	203	1297	Huazhong Univ Sci & Technol	45	424	1686	Georgia Inst Technol	34	314	2122
Politecn Milan	53	63	547	Univ Salford	24	396	1148	Huazhong Univ Sci & Technol	45	424	1686
Southeast Univ	47	57	709	Kyung Hee Univ	28	352	1658	Kyung Hee Univ	28	352	1658
Huazhong Univ Sci & Technol	45	424	1686	Georgia Inst Technol	34	314	2122	Deakin Univ	43	200	1395
Univ Melbourne	44	192	1070	Tsinghua Univ	36	306	1068	Hong Kong Univ Sci & Technol	30	293	1300
Deakin Univ	43	200	1395	Chung Ang Univ	20	305	1153	Tongji Univ	55	203	1297
Natl Univ Singapore	43	232	1146	Hong Kong Univ Sci & Technol	30	293	1300	Shenzhen Univ	38	233	1174

in all three indicators, displaying their enormous influences and leading positions. Notably, with less than 20 articles published, the University of Salford and Chung Ang University successfully ranked in the top ten in TLCS, suggesting the widespread attention they have attracted among scholars in this field. According to the TGCS ranking, the articles published by Georgia Institute of Technology, Hong Kong University of Science and Technology (HKUST), and Shenzhen University have been highly recognized by the entire scientific community, which corroborates the broad influence of these universities.

There are fewer institutions involved in BIM-maintenance research than in the other three stages (

Table A6. The most active institutions in the field of BIM-maintain research.

Institution	Recs	TLCS	TGCS	Institution	Recs	TLCS	TGCS	Institution	Recs	TLCS	TGCS
Politecn Milan	22	62	328	Concordia Univ	10	67	407	Carnegie Mellon Univ	7	51	534
Univ Hong Kong	16	24	305	Politecn Milan	22	62	328	Concordia Univ	10	67	407
Tsinghua Univ	14	50	311	Natl Univ Singapore	6	55	395	Natl Univ Singapore	6	55	395
Univ Seville	14	19	212	HKUST	12	54	359	Univ Castilla La Mancha	3	29	387
Cairo Univ	13	26	257	Carnegie Mellon Univ	7	51	534	HKUST	12	54	359
Hong Kong Polytech Univ	13	2	209	Tsinghua Univ	14	50	311	Univ Helsinki	2	13	353
HKUST	12	54	359	Heriot Watt Univ	1	48	187	Univ Cambridge	8	40	351
Tongji Univ	12	12	181	Univ Cambridge	8	40	351	Politecn Milan	22	62	328
Northumbria Univ	11	7	86	Birmingham City Univ	6	29	165	Tsinghua Univ	14	50	311
Virginia Tech	11	23	181	Univ British Columbia	8	29	175	Univ Hong Kong	16	24	305

). Among them, Hong Kong University of Science and Technology, Polytechnic University of Milan, and Tsinghua University were the most active institutions, ranking in the top ten in all three indicators. Although Heriot Watt University has published only one article, it ranks sixth in TLCS. Similarly, the University of Helsinki ranked fifth in TGCS, with only two published articles. Despite the small numbers of publications, the two universities still received widespread attention and recognition from both their fields and the entire academic community.

According to the Recs, there were four institutions playing significant roles in research at all stages of BIM: the Hong Kong Polytechnic University, Polytechnic University of Milan, Tongji University, and the University of Hong Kong. Among them, the TGCS of University of Hong Kong ranked in the top 10 for all four stages. The high activities and leading positions of the four institutions made them the core forces for BIM lifecycle research.

4.2. Network-Based Analysis

4.2.1. Co-Word Network Analysis

Considering previous studies, data characteristics, and the network load of this study, we especially focused on keywords appearing over 10 times. Utilizing VOSviewer, a co-word network diagram was drawn as a reflection of the BIM lifecycle (Figure 3 and Figure A1, Figure A2 and Figure A3). In Figure 3 and Figure A1, Figure A2 and Figure A3, each node represents a keyword, the size of the node is proportional to the frequency of the keyword’s appearance in papers, and the strength of the connections between nodes represents their co-occurrence frequency in the papers. The color of each node identifies the knowledge community or cluster to which it belongs.

In the field of BIM-planning research, we identified 388 keywords appearing over 10 times during the research period and then outlined four primary thematic clusters (Figure 3). Among them, the green cluster, where “BIM” is located, is the largest, with 123 keywords, demonstrating the dominant position of BIM in the field. The blue, yellow, and red clusters, covering 97, 94 and 74 keywords, respectively, constitute the secondary theme levels of BIM-planning research.

For BIM-construction research, there were a total of 617 keywords with over ten occurrences (Figure A1). Those keywords form five primary clusters, among which the red, blue and green ones are larger with 151, 147, and 140 keywords, respectively, indicating the vibrancy and prosperity of their corresponding research fields. Another point worth noting is that the smallest cluster, the purple cluster, which contains only 44 keywords, suggesting some imbalance in the development of this research area.

In the research field of BIM-management, 375 keywords appeared more than 10 times and constituted six thematic clusters (Figure A2). Among them, the largest were the red and blue ones with 115 and 100 keywords, respectively, showcasing their dominant positions in BIM-management research. In contrast, the two smallest clusters, purple and yellow, both involve less than 40 keywords. This reflects significant differences in research intensity distribution.

For BIM-maintenance research, 305 high-frequency keywords constituted five primary clusters, among which the red cluster was the only one with over 100 keywords (Figure A3). However, there was also a cluster containing only 15 keywords, which hints at the immense potential and room for expansion in BIM-maintenance research.

In summary, BIM-construction research has exhibited not only its prosperity but also the plurality of its research topics with abundant keywords and clusters. These diverse topics extensively encompass areas such as cloud computing, big data, and AI, highlighting the depth, breadth, and innovation of BIM-construction research. By comparison, the keywords and clusters of BIM-maintenance research were less abundant, primarily due to its relatively late start in the construction maintenance stage and the challenges it faces, such as integrating BIM and facility management systems and handling complex data. Nonetheless, with advancements in technology and increases in domain requirements, it is foreseen that BIM-maintenance research will gradually deepen and become valuable for the long-term development and efficiency improvement in the field.

Figure 3. Co-word network clustering map of BIM-planning research.

Figure 3. Co-word network clustering map of BIM-planning research.

4.2.2. Co-Citation Network Analysis

In this study, hot topic clusters of research in each stage in the BIM lifecycle were mapped with CiteSpace software 6.2.R1 (Figure 4 and Figure A4, Figure A5 and Figure A6). In Figure 4 and Figure A4, Figure A5 and Figure A6, two network metrics were adopted to evaluate the co-citation network, namely modularity (Q) and silhouette (S). The former measures the strength of the network within a cluster, and the latter measures the cluster consistency. Generally, if the Q value of a cluster is greater than 0.3, the structure of this cluster is considered significant, and if it has an S value over 0.5, its division is considered reasonable. In addition, the closer S is to one, the higher the consistency of the cluster members [59,60]. The color of the connections between the nodes within the cluster corresponds to the time color in the legend. With the evaluation, not only can the main clusters of research be revealed, but the main research topics and fields involved in each stage in the BIM lifecycle can also be identified. The modularity and silhouette of the four diagrams in Figure 4 and Figure A4, Figure A5 and Figure A6 are above 0.8 and 0.9, respectively, which indicates that all four diagrams present strong network structures and cluster quality. Overall, both BIM-planning and BIM-management research included dozens of clusters, more than half of which were formed between 2019 and 2023, and the earliest clusters were formed between 2009 and 2013. For BIM-maintenance research, there were only 8 clusters, which were formed during the periods of 2009–2013 and 2014–2018, while no significant clusters were formed during 2019–2023.

For BIM-planning research, the earliest and largest cluster, namely cluster #14, emerged between 2009 and 2013, with its main cited articles centering around the “construction industry”. Within this cluster, the “BIM handbook” was frequently cited, thereby demonstrating its pivotal importance (Figure 4). Within this cluster, the BIM handbook is particularly noteworthy due to its frequent citations. Four primary thematic clusters (#4, #9, #12, #13) subsequently emerged from 2014 to 2018. Then, between 2019 and 2023, eight distinguishable research clusters (#1, #2, #3, #5, #6, #7, #8, #10) arose. Among these, cluster #1 formed during this period represents the largest research group, with major themes relating to “interorganizational collaboration” and “application programming interface”. Its highly cited literature includes works such as Barlish et al. (2012) [61], Ghaffarianhoseini et al. (2017) [62], and Liu et al. (2017) [26]. The most recently formed cluster was #7, primarily focusing on “virtual reality” and other emerging digital technologies.

In the field of BIM-construction research, spanning from 2019 to 2023, eight primary thematic clusters emerged (#1, #2, #3, #4, #7, #9, #10, #11), as depicted in Figure A4. Among them, clusters #1 and #2 placed the largest emphasis on “carbon emission” and “digital twin”, respectively. Cluster #10 was the latest formed cluster. This cluster primarily focuses on research topics such as “augmented reality” and “innovation strategy”. The earliest cluster (#5) mainly concerns methods, theories, and techniques of model building and was formed between 2009 and 2013. During the same period, four other clusters (#6, #8, #12, #13) also came into being. The largest cluster (#13) focuses on “construction engineering” and involves works such as the BIM handbook and those from Azhar and Gu et al. Among them, the articles by Chong et al. in 2017 and Lu et al. in 2017 were highly cited in WoS. All those works point out that BIM brings a new research paradigm to the fields of architecture, engineering, and construction (AEC).

A total of thirteen primary clusters were formed in the field of BIM-management research (Figure A5). The earliest cluster (#5) was formed during the period between 2009 and 2013; it focuses on construction engineering and is represented by works such as Eastman C (2008) and Eastman et al. (2011) [63]. Then, in the next five years, four clusters (#3, #11, #12, and #13) came into being; among them, the largest cluster was #13. In this cluster, great attention is given to stakeholder expectations, and Bryde et al. (2013), which discusses the benefits that BIM can bring to construction projects, is the most cited work [42]. Most clusters were formed between 2019 and 2023: clusters #1, #2, #4, #6, #7, #8, #9, and #10. Among them, cluster #1 was the largest cluster formed during the study period. The cluster focuses on digital twins; Tang et al. (2019) contributed the most cited paper, which provides a detailed study of the integration of BIM and the Internet of Things (IoT), and the paper from Dave and Li et al. received the second most citations [10,46,64]. As the latest emerging clusters, clusters #9 and #10 mainly focused on organizational capabilities and augmented reality, respectively.

There were eight primary thematic clusters of BIM-maintenance research. Cluster #3 was the earliest formed cluster (2009–2013) and focuses on “construction process simulation”. Other clusters were formed between 2014 and 2018. The most recently formed cluster, cluster #5, mainly concerns “augmented reality”, “digital twins”, and “urban infrastructure”. The largest cluster was cluster #4, and it primarily explores “historic building information modeling (HBIM)” [65,66]. In this cluster, the most cited article was from Parn et al. (2017), which reviews factors affecting the building usage and operational stage in BIM-maintenance [67]. In addition, the research by Gao et al. (2019) was an important part of the cluster #4 [1]. Smaller than the cluster #4 in size, cluster #1 was mainly about corrective maintenance research, such as studies on office building and equipment maintenance. Representative works include those by Chen et al. (2018) and Shalabi et al. (2017) [15,68].

4.2.3. Hybrid Network Analysis

Figure 5 (Figure A7, Figure A8 and Figure A9) depicts a hybrid network of BIM’s application lifecycle research. In terms of the number of published articles, the institutions and journals ranking in the top 30 are represented by the squares on the left and right, respectively. The 30 squares in the center of Figure 5 (Figure A7, Figure A8 and Figure A9) represent the 30 most frequently occurring keywords.

In the field of integrated research on BIM-planning (Figure 5) and BIM-management (Figure A8), Hong Kong Polytechnic University, the University of Hong Kong, and Curtin University have published the most articles, and those articles cover the top 30 keywords, displaying the breadth and depth of the three institutions in the research field. In addition, a large portion of institutions with over 10 related articles were associated with the most frequently occurring keywords, indicating the popular research topics of a certain period. This suggests that the choice of many institutions to conduct research on BIM-planning and management is likely driven by the need to follow research hotspots.

For integrated research on BIM-construction (Figure A7) and maintenance (Figure A9), none of the top 30 institutions were associated with all the top 30 keywords for two reasons. First, BIM-construction is a broad field of research, and its various contents and research directions make the coverage of all high-frequency keywords a daunting challenge for any single institution. Second, there is an extremely rapid rate of technological updates in BIM-maintenance. Such updating means the rapid iteration of keywords and research directions in the field, which brings great difficulties in covering all keywords in a short period.

Additionally, some keywords only appear in the BIM-maintenance field, including “HBIM”, “digital twin”, “cultural heritage”, “conservation”, “optimization”, “photogrammetry”, and “reconstruction”. This is partly an indication that BIM-maintenance research involves the application of many new technologies and efforts in model reconstruction, and it also suggested that the role of BIM technology in areas such as cultural heritage preservation and historical heritage research will gradually become more prominent [25,69,70]. Similarly, two journals, namely the Journal of Performance of Constructed Facilities and International Journal of Architectural Heritage, entered the top 30 in terms of the number of published articles only in the research field of BIM-maintenance. This results from their focuses, building performance, and architectural heritage preservation. The two topics are more compatible with BIM-maintenance and suggest that the journals pay special attention to case studies and empirical research on BIM-maintenance, especially project management and problem-solving solutions. Hence, the two journals can be considered quality submission journals for BIM-maintenance research.

Overall, in the research field of the BIM lifecycle, the three journals with the largest number of publications are Automation in Construction, Sustainability, and Buildings. In addition to the search keyword “BIM”, these journals also included words such as “System”, “model”, and “framework”, hinting that current mainstream journals place high importance on systematic research methods, the practical application of models, and the construction of theoretical frameworks. This not only reflects the depths of relevant research but also highlights their active explorations into practical problem-solving solutions.

5. Discussion

This study works on the objectives of revealing the associations and disparities in technological requirements among various stages of the BIM lifecycle, and pinpointing the research boundaries of each stage, exploring the research trends of each stage. The descriptive data-driven analysis method was employed to achieve a quantitative description of research outputs, research trends, and thematic evolutions. Additionally, the network-based analysis method was adopted to realize a quantitative visualization of the relationships among the research themes throughout the various stages. In the subsequent text, all stages of the BIM lifecycle are discussed to unearth concealed connections among them, to identify their research boundaries, and to elucidate the practical utility of BIM in fields like construction and information management.

5.1. BIM-Planning

BIM-planning research involves a wide range of specific technical issues about construction, management, framework design, and visualization methods (Figure 3). The involvement has generated 14 research topic clusters, which are mainly related to themes such as “construction industry”, “interorganizational collaboration”, “application programming interface”, and “virtual reality” (Figure 4). This suggests that researchers have conducted comprehensive and mature studies on BIM’s application at the planning stage, which is an interdisciplinary field covering technical, design, construction, and managerial aspects that align with the integrative role of BIM in planning [17,24]. Given the considerable overlap between BIM-planning and subsequent stages, quite a few studies on the interactions between them have been carried out [6,17,24]. Despite the diversity of those study topics, they are closely related in the BIM-planning context, implying an integrated research focus.

According to cluster #14 (Figure 4) concentrating on the construction industry, BIM has already transformed the way buildings are planned and designed. Additionally, future research will focus on expanding BIM’s capabilities in planning beyond individual buildings and toward interorganizational collaboration as well as the integration of emerging and digital technologies [4,17]. Cluster #1 indicates an important research focus, namely interorganizational collaboration. More efforts will be put into exploring better applications of BIM to facilitate collaboration between different stakeholders at the planning stage, including architects, engineers, contractors, and building owners. This will involve the development of new standards for data sharing and interoperability, as well as new tools and technologies for collaboration support [26,62,71]. The application programming interface (API) will play an increasingly important role in BIM-planning. APIs enable different software systems to communicate with each other, allowing for the integration of data from multiple sources [7,58]. The development of open source BIM technologies will also be an important enabler for greater interoperability and data integration through open APIs and standards [72]. Cluster #7 shows that future research will pay attention to new APIs for the better integration of BIM with other planning tools and systems. Additionally, the web-based cloud-native architectural design collaboration platform can offer the real-time sharing of solution and task collaboration environments for professionals involved in the planning and design stages. This, in turn, enhances the efficiency of communication regarding design inspiration and data sharing among groups during the engineering design stage. Virtual reality (VR) will become an essential tool in BIM-planning. VR technology enables users to experience a building or space in a virtual environment before it is constructed [3,58,73]. Future research will focus on developing new VR tools and interfaces to enable the better visualization and communication of design ideas. Furthermore, the integration of 4D and 5D simulation technologies into BIM is recognized as highly promising. With this technology, not only can the construction progress, costs, safety, sustainability, and performance be accurately simulated, but design solutions can also be assessed and optimized across multiple dimensions. Consequently, not only can design quality be significantly enhanced, but the time, budget, and efficiency of projects can also be effectively controlled, leading to a substantial improvement and advancement in the intelligence level of architectural design and planning.

In addition, the integration of emerging and digital technologies will also shape future research on BIM-planning. This includes technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) [10,17,46]. Researchers will explore how these technologies can be integrated with BIM to enable better decision making and more efficient planning processes [17,24]. Overall, the use of BIM in urban planning will continue to expand. Researchers will investigate how BIM can be used to model entire cities and urban systems, allowing for the more effective planning and management of urban infrastructure [17,25]. This includes exploring the potential of BIM-based digital twins for urban planning and management.

5.2. BIM-Construction

Researchers have studied various aspects of applying BIM at the stage of construction, including design collaboration, project management, data models, and web-based solutions. The formation of clusters around keywords such as “design”, “management”, “models” and “Internet” in the field of BIM-construction research indicates that the construction stage poses unique challenges that require design optimization, the management of information-rich models, and Internet-enabled solutions for coordination and integration (Figure A1) [17,23,46]. Moreover, BIM-construction research focuses not only on the technology itself but also on how BIM intersects with design processes, management functions, data modeling approaches, and Internet-based solutions during construction [6,13,74].

With mature BIM technology, research related to BIM-construction is moving beyond simply exploring its use and focusing more on how BIM can address specific issues related to design, management, modeling, and connectivity in the construction context [5,29,75]. Future research on BIM-construction is expected to focus on several key areas, including “construction models”, “construction engineering”, “digital twins”, “augmented reality (AR)”, “innovation strategies”, and “carbon emission reduction” (Figure A4).

Reducing “carbon emissions” will be a primary focus of future research in BIM-construction, which is supposed to generate explorations into applying BIM to optimized building design and construction to minimize the carbon footprint, such as developing new tools and methodologies to simulate and analyze carbon emissions throughout the building lifecycle [11,58]. The use of “digital twins” will continue to expand in BIM-construction. The application of digital twin technology can not only enable the entire construction process to be virtually simulated and dynamically modeled but also realize real-time construction quality monitoring, supply chain coordination, and assembly construction guidance. By integrating various types of construction data and deeply merging the virtual world with actual engineering, this technology is instrumental in achieving the precision, visualization, and collaborative management of architectural projects, thereby significantly enhancing the construction efficiency [4,13,53,75]. Future research will focus on refining digital twin technologies to enable better integration with BIM, as well as exploring new applications for digital twins in building design and construction [13,24,53,75].

“Construction models” will continue to evolve and drive future research on BIM-construction [25,43,76]. Researchers will explore utilizing BIM to improve construction modeling, such as 4D and 5D modeling, for better visualization and analysis of construction processes [23,29,75,77]. Another potentially significant area of future research is “construction engineering”. More investigations on how BIM can benefit the engineering process by identifying potential issues and streamlining workflows are expected to be conducted [17,58,77].

The integration of “AR” will also shape future research in BIM-construction. Using AR, virtual BIM models can be projected onto actual construction environments, allowing for the positioning, monitoring, and information display of components. This will support detecting and providing early warnings for quality and safety issues and offer guidance and optimization for construction activities. This technology is set to become an essential tool for BIM on-site construction management. When deeply integrated with BIM, AR technology is believed to greatly facilitate the intelligent innovation of building construction [3,4,46,64,73]. The role of “innovation strategies” in BIM-construction will become more important. The development of new business models and value chains will be studied to drive innovation in the AEC industry [9,44,77].

5.3. BIM-Management

The clusters formed around keywords such as “sustainability”, “Internet of Things”, “point clouds”, and “safety” in addition to the core keywords “BIM” and “management” in the field of BIM-management research (Figure A2) indicate that scholars have explored diverse applications of BIM beyond traditional project objectives, such as achieving sustainability goals, integrating emerging technologies, utilizing point cloud data, and enhancing safety during facility usage [14,64,78]. While management remains a focus, scholars have also investigated issue-based research questions involving sustainability, digital connectivity, data processing, and risk mitigation utilizing BIM. This suggests that, as BIM is a mature technology, management research is shifting from a general focus to more specialized domains such as sustainability initiatives, data analytics, and safety assurance [53,58].

Several areas are expected to be the focus of future BIM-management research (Figure A5). One of them is “construction engineering”, which involves the application of engineering principles to construction projects [30,75]. Another important area is stakeholders’ expectations (Figure A5). Along with more numerous and wider adoptions of BIM in the construction industry, stakeholders’ expectations for its use and effectiveness are likely to be higher [79]. BIM investment and return on investment (ROI) expectations are also expected to be key areas of research. Additionally, as companies invest more in BIM technology, they will expect higher ROIs [1,80]. This promotes research on measuring and maximizing the ROI of BIM investments.

On the other hand, “organizational capabilities” are also an important area in the research field of BIM-management (Figure A5). Once companies adopt BIM technology, new organizational capabilities to effectively manage and utilize this technology will be needed. Therefore, how companies can develop these capabilities and leverage them to achieve competitive advantage will be studied. Moreover, “augmented reality” is expected to play an increasingly greater role, with more explorations on how BIM can improve communication efficiency between digital building information models and real-world structures at the stage of management [4,13,24]. Given the potential of augmented reality to improve stakeholder engagement, reduce errors and rework, and enhance collaboration between stakeholders, attention will be given to the effective integration of augmented reality with BIM [44,79].

Furthermore, utilizing technologies such as the IoT, cloud computing, and big data to finely perceive, connect, and manage urban resources and services can help achieve the goal of smart cities [10,17]. Simultaneously, through digitalization and information technology, the entire lifecycle of construction projects is integrated and managed by BIM. Detailed digital models of buildings and infrastructure are provided by the BIM-management platform, supporting more intelligent planning and decision making for smart city businesses [6,7,33]. Conversely, a smart city can provide real-time environmental and operational data to BIM, leading to more precise construction management. With the deep integration of BIM and smart cities, urban management is anticipated to shift toward a more green, intelligent, and collaborative direction [4,10,17].

5.4. BIM-Maintenance

The focuses on “point clouds”, “big data”, and “digital twin” as well as traditional methods such as “CityGML” and “CAD” in BIM-maintenance research (Figure A3), suggesting that scholars are investigating how BIM can facilitate technological transformation at both the building and urban scales [3,4,53,75].

At the urban scale, researchers are exploring how BIM can be applied to the management and maintenance of large-scale urban systems, including roads, bridges, and utilities [58,81]. Specifically, they are investigating how BIM can amplify its benefits by integrating with urban-level solutions such as Geographic Information Systems (GIS) and digital twins, aiming to transform infrastructure maintenance and sustainability [6,19,75,79]. Current research delves into the horizontal integration of BIM with city-level solutions and its vertical scaling from individual facilities to broader sociotechnical systems. This highlights BIM’s evolving role in enhancing not only building maintenance but also overall urban sustainability [1,15,17,82]. The technological focus of BIM-maintenance research reveals scholars’ interest in BIM-enabled innovation at both scales, displaying BIM’s potential to transform sociotechnical systems beyond individual facilities [9,32,75].

At the maintenance stage, BIM is expected to continue evolving with several research trends in the future. Firstly, there is the “construction process simulation”. Research following this trend is concerned with better planning and coordination during maintenance activities and will help identify potential issues and streamline workflows, leading to more efficient and cost-effective maintenance practices [58,75,77]. Secondly, there is the application of BIM in “historic building information modeling (HBIM)”. The preservation and maintenance of historic structures require specialized approaches, and HBIM provides a framework for creating accurate digital models of these buildings. This will promote research on refining HBIM methodologies, integrating historical data, and developing tools to support the preservation and ongoing maintenance of historic structures [17,25,58]. For example, common data environment (CDE) platforms are used to structure and transfer digital information within and between teams, while participants work across multiple media in both structured and unstructured ways [83]. Thirdly, there is “corrective maintenance”, corresponding to BIM’s ability to provide real-time data and accurate information about building components enables proactive maintenance practices. Future studies will explore advanced algorithms and machine learning techniques to predict maintenance needs, optimize scheduling, and improve the overall efficiency of corrective maintenance processes [1,15,17].

Lastly, but most importantly, the concept of the “digital twin” will also shape future research on BIM-maintenance. Digital twins and IoT technologies can be utilized to achieve virtual digital mapping for the entire building operation and maintenance process, and various devices and systems can be intelligently networked. Through the establishment of high correspondence between cyber and physical spaces, the real-time monitoring and optimization of operation, maintenance, and management be conducted, and the digital management of various operation and maintenance assets and facilities can be realized. This significantly elevates the level of automation, intelligence, and precision in building operation and management, marking an important direction in the development of BIM during the maintenance stage. With the aid of these technologies, BIM operation and maintenance management are expected to reach unprecedented levels of intelligence.

5.5. Comparison with Existing Research

Compared with previous reviews on BIM research, this study presents a significant distinction and introduces some innovations which are listed below.

Firstly, this study is stage-based. In contrast with previous studies, an in-depth comparative analysis of each stage of the BIM lifecycle has been conducted in this paper, im-plying that (1) the specific issues and characteristics of each stage are explored more extensively in this study, thereby enhancing the precision of the research; (2) new technologies and methods relevant to a particular stage are more likely to be identified; (3) the research boundaries of each stage are precisely defined, and potential connections between different stages are thoroughly investigated. More importantly, through comparison, this study has recognized the BIM research characteristics of each stage in the building lifecycle, and unveiled its underlying academic structure and knowledge flow, interpreting the evolution of BIM lifecycle research.

Secondly, novel methods were adopted. In the descriptive data-driven analysis, this study introduced TGCS and TGLS, achieving the quantitative characterizations of the domain influence and overall scientific impact of the BIM lifecycle stage-specific metrics. In the network-based analysis, this study incorporated a hybrid network analysis, facilitating an intuitive visualization of the knowledge flow throughout each stage of the BIM lifecycle.

Finally, new findings have been discovered. Diverging from existing related studies, for each stage of BIM lifecycle, findings of this study not only reveal their characteristics and potential connections but also points out their challenges, enhancing research granularity across the BIM lifecycle and aiding scholars in anticipatorily perceiving potential new technologies or methods of BIM.

In summary, through an in-depth and systematic quantitative analysis, the characteristics and developmental trends of each stage of the BIM lifecycle were explored in this study. The preset study objectives have been successfully achieved: (1) offering more precise analytical conclusions compared to previous research; (2) unveiling different emphases of BIM at different stages, such as: a greater focus on the production of BIM components, collaborative design, and data sharing based on CDEs during the BIM-planning stage; an emphasis on virtual-real coordination and construction progress management technologies during the BIM-construction stage; attention to technologies related to the interaction between buildings and people, returns on building assets, and sustainable development during the BIM-management stage; and, during the BIM-maintenance stage, a focus on the fusion of BIM with other urban management fields, including digital twins, big data, GIS, and related technologies; and (3) providing theoretical references for subsequent research on common key technologies throughout the BIM lifecycle.

6. Conclusions

A data-driven strategy was employed in this study to explore the research characteristics of each stage in the BIM lifecycle globally from 1999 to 2023. Under preset restrictive search criteria, in-depth data collection was conducted on the WoS database, resulting in the acquisition of 13,228 records. Utilizing bibliometric indicators and network analysis methods, thorough analyses of current research hotspots and global trends were performed. According to the analyses, the following conclusions were drawn:

(1)

During the research period, there were continuous increases in the relevant articles at each stage in the BIM lifecycle, with the annual article output leaping from 0 to 1150. In terms of the number of published articles, Automation in Construction, which was recognized as an authoritative journal in both the BIM research field and the entire scientific community, was the most active platform for BIM research. Additionally, journals such as Advanced Engineering Informatics, Construction and Architectural Management, and International Journal of Architectural Heritage were also widely regarded as authoritative in this field. From a comprehensive perspective of all three indicators (Rec, TLCS, TGCS), Hong Kong Polytechnic University, University of Hong Kong, and Curtin University were all leaders in research on BIM-planning, BIM-construction, and BIM-management. Meanwhile, the Polytechnic University of Milan displayed the highest activity in BIM-maintenance research and gained high recognition within the industry.

(2)

Utilizing network analysis methods, the primary research focuses across stages of the BIM lifecycle were revealed, and global research trends were captured. Emerging digital technologies, such as virtual reality, augmented reality, and digital twins, were identified as prominent topics of interest within the academic community. Over the past 25 years, diverse research hotspots emerged during different periods. From 2017, the introduction of new technologies such as digital twins and virtual reality, the optimization of deep learning algorithms, and the deployment of artificial intelligence technology greatly catalyzed the global research evolution across the BIM lifecycle. Institutions including Hong Kong Polytechnic University, University of Hong Kong, and Curtin University covered almost all popular domains related to BIM-planning and BIM-management. Additionally, the analyses support an expectation of an increasingly pivotal role of BIM technology in research fields such as the preservation of cultural heritage and historical artifacts.

(3)

The future of the BIM lifecycle research should focus on various aspects, such as interorganizational collaboration, CDEs information sharing, open-source system of BIM/HBIM, visualization, decision making, sustainability, construction engineering, innovation, and urban infrastructure management. The integration of emerging and digital technologies such as AR, digital twins, and AI will play a crucial role in driving advancements in project management, collaboration, communication, and decision-making in the construction industry. In addition, the optimization of construction engineering, stakeholder expectations, organizational capabilities, and proactive maintenance practices will result in the better management of urban infrastructure and preservation of historic structures. Overall, the future of BIM lifecycle research will be characterized by multidisciplinary approaches that integrate technology with sustainable practices and stakeholder collaboration.

Despite the findings mentioned above, this study has several limitations. First, the analysis results were significantly influenced by the search keywords. The impracticality of achieving a complete retrieval of all literature in a particular domain using keywords or searches leads to potential omissions of essential studies. Moreover, the data were exclusively sourced from the WoS database. Although WoS is widely acknowledged as a trustworthy source for tracing research trends in specific fields and many research topics have been shown by existing studies to display similarities across databases, an exclusive reliance on WoS could render the analysis less comprehensive, thereby affecting the reliability of the outcomes. Last, the vast range of literature encompassed by this study makes exploring the specifics and contributions of each paper even harder, resulting in a lack of depth in the analysis.

In the future, research on the BIM lifecycle will expand on the following aspects. Firstly, concerning data collection, the inclusion of a broader range of mainstream databases is planned, and the search criteria will be further expanded. Variants of search keywords will be employed to ensure the comprehensiveness of the retrieval results. Second, in terms of the research breadth, exploration into the application of BIM in additional stages, such as manufacturing supply and demolition, will be undertaken. Finally, from an in-depth perspective, attention will be directed towards understanding how to efficiently utilize resources throughout the entire lifecycle of a building project with the aid of BIM. Moreover, the integration of cutting-edge technologies such as artificial intelligence and virtual reality will be explored to further enhance the efficiency and overall quality of project execution.