Editorial

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3 pages, 187 KiB

Open AccessEditorial

Intelligent and Computer Technologies’ Application in Construction

by Hongling Guo, Jia-Rui Lin and Yantao Yu

Buildings 2023, 13(3), 641; https://doi.org/10.3390/buildings13030641 - 28 Feb 2023

Cited by 3 | Viewed by 1281

Abstract

The construction industry is faced with many challenges, such as lagging productivity [...] Full article

(This article belongs to the Special Issue Intelligent and Computer Technologies Application in Construction)

Research

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27 pages, 2033 KiB

Open AccessArticle

Factors Affecting BIM Adoption in the Yemeni Construction Industry: A Structural Equation Modelling Approach

by Ali Hamoud Mssoud Al-sarafi, Aidi Hizami Alias, Helmi Zulhaidi Mohd. Shafri and Fauzan Mohd. Jakarni

Buildings 2022, 12(12), 2066; https://doi.org/10.3390/buildings12122066 - 25 Nov 2022

Cited by 3 | Viewed by 2721

Abstract

The construction sector is one of Yemen’s most important economic pillars. Building information modelling (BIM) is a new information technology implementation that can create an intelligent digital design of buildings to support a variety of tasks and provides a wide range of benefits [...] Read more.

The construction sector is one of Yemen’s most important economic pillars. Building information modelling (BIM) is a new information technology implementation that can create an intelligent digital design of buildings to support a variety of tasks and provides a wide range of benefits throughout the project life cycle. However, BIM is not widely embraced in Yemeni construction firms. Compared with other countries, Yemen presents a unique case for BIM adoption due to the ongoing war in the country, which will assist in rapid rebuilding processes. Thus, a complete and systematic investigation of the factors affecting BIM adoption in the Yemeni construction industry is required. This study utilises five categories of impacting factors: Technology, Process, Policy, People, and the Environment to model the strategic implementation for BIM in the Yemeni construction industry. A random sample was used to achieve homogeneity and increase the consistency and quality of data. Purposive sampling was used to choose participants for the framework validation. The data were analysed using partial least squares structural equation modelling (PLS-SEM), and the key factors influencing BIM adoption were determined and modelled. The results show multivariate results indicate a high correlation within the measurement model for all factors affecting BIM adoption in Yemen. In addition, the developed model was deemed to fit because the analysis result of the model’s coefficient of determination test (R²) is BIM adoption having 0.437, Environment at 0.589, and People having 0.310, demonstrating high acceptance. Moreover, the results reveal a high correlation between policy and people (>0.50), while the environment significantly affected BIM adoption (0.304). Overall, the model illustrated how various factors influence BIM adoption. The created framework highlights the importance of understanding BIM adoption concepts and challenges in the Yemeni construction industry. It is believed that this study highlights the BIM implementation in developing countries such as Yemen and the possibility of implementing the proposed method in other countries to develop their own BIM implementation strategy. Full article

(This article belongs to the Special Issue Intelligent and Computer Technologies Application in Construction)

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21 pages, 3367 KiB

Open AccessArticle

Maturity Assessment of Intelligent Construction Management

by Chao Lin, Zhen-Zhong Hu, Cheng Yang, Yi-Chuan Deng, Wei Zheng and Jia-Rui Lin

Buildings 2022, 12(10), 1742; https://doi.org/10.3390/buildings12101742 - 19 Oct 2022

Cited by 7 | Viewed by 2528

Abstract

In the new era of Construction 4.0, the application of a large number of intelligent information technologies (ITs) and advanced managerial approaches have brought about the rapid development of intelligent construction management (ICM). However, it is still unclear how to assess the maturity [...] Read more.

In the new era of Construction 4.0, the application of a large number of intelligent information technologies (ITs) and advanced managerial approaches have brought about the rapid development of intelligent construction management (ICM). However, it is still unclear how to assess the maturity of ICM. In this study, a maturity assessment system for ICM was formulated through literature reviews, questionnaires, expert discussions and a case study. A maturity scoring table containing five assessment dimensions and twenty assessment indicators was developed, and corresponding maturity levels and a radar chart of dimensions were designed. A case study of the assessments of two construction enterprises was conducted to validate that the proposed assessment system could be used by construction enterprises to quantitatively assess their ICM maturities and obtain both overall and specific assessment results. This study also proposed practical improvement methods to improve ICM maturities for construction enterprises with different maturity levels. Furthermore, the study also discussed the development direction of ICM at present and in the short-term future, which should be paid more attention to by the construction industry. Full article

(This article belongs to the Special Issue Intelligent and Computer Technologies Application in Construction)

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15 pages, 2384 KiB

Open AccessArticle

Expanding Domain Knowledge Elements for Metro Construction Safety Risk Management Using a Co-Occurrence-Based Pathfinding Approach

by Na Xu, Bo Zhang, Tiantian Gu, Jie Li and Li Wang

Buildings 2022, 12(10), 1510; https://doi.org/10.3390/buildings12101510 - 22 Sep 2022

Cited by 2 | Viewed by 1631

Abstract

Knowledge is a contribution factor leading to more effective and efficient construction safety management. Metro construction practitioners always find it difficult to determine what specialized knowledge is needed in order to lead to better safety risk management. Currently, domain knowledge elements are generally [...] Read more.

Knowledge is a contribution factor leading to more effective and efficient construction safety management. Metro construction practitioners always find it difficult to determine what specialized knowledge is needed in order to lead to better safety risk management. Currently, domain knowledge elements are generally determined by experts, which is coarse-grained and uncomprehensive. Therefore, this paper aims to provide a structure of domain knowledge elements, using an automatic approach to expand domain knowledge elements (DKEs) from a big dataset of unstructured text documents. First, the co-word co-occurrence network (CCN) was used to find the connected knowledge elements, and then the association rule mining (ARM) was compiled to prune the weakly related subnetworks, leaving the strong associated elements. Finally, a list of DKEs in the metro construction safety risk management was obtained. The result shows that the obtained DKEs are more comprehensive and valuable compared to previous studies. The proposed approach provides an automatic way to expand DKEs from a small amount of known knowledge, minimizing the expert bias. This study also contributes to building a fine-grained knowledge structure for metro construction safety risk management. The structure can be used to guide safety training and help knowledge-based safety risk management. Full article

(This article belongs to the Special Issue Intelligent and Computer Technologies Application in Construction)

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22 pages, 1855 KiB

Open AccessArticle

Factors Influencing the Adoption of Blockchain in the Construction Industry: A Hybrid Approach Using PLS-SEM and fsQCA

by Chunhao Li, Yuqian Zhang and Yongshun Xu

Buildings 2022, 12(9), 1349; https://doi.org/10.3390/buildings12091349 - 01 Sep 2022

Cited by 21 | Viewed by 5734

Abstract

Blockchain is considered a breakthrough technology in the construction industry, with the potential to improve the trust environment and workflow of construction stakeholders. Although recent research offers hints regarding possible contributing elements to blockchain adoption in the construction industry, no specific study has [...] Read more.

Blockchain is considered a breakthrough technology in the construction industry, with the potential to improve the trust environment and workflow of construction stakeholders. Although recent research offers hints regarding possible contributing elements to blockchain adoption in the construction industry, no specific study has addressed this topic. This knowledge gap hinders the adoption and promotion of blockchain in construction organizations. This study aimed to identify the determinants of blockchain adoption in the construction industry and verify the influence of the combination of various factors on adoption intention. Based on the technology–organization–environment framework, a conceptual model of blockchain adoption in the construction industry was constructed. Data were collected through the distribution of questionnaires, and 244 professionals in the construction field participated in this study. To evaluate the model hypotheses, we used a two-stage partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) combination. The PLS-SEM revealed that factors such as compatibility, top management support, relative advantage, regulatory support, cost, competitive pressure, organizational readiness, and firm size significantly influence blockchain adoption. The fsQCA indicated that six causal conditions achieve high adoption intention. This is one of the first empirical studies on blockchain adoption in the construction industry, which can aid organizations, policymakers, and project participants in making informed decisions regarding the adoption of blockchain. Full article

(This article belongs to the Special Issue Intelligent and Computer Technologies Application in Construction)

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17 pages, 6917 KiB

Open AccessArticle

Integrated Schematic Design Method for Shear Wall Structures: A Practical Application of Generative Adversarial Networks

by Yifan Fei, Wenjie Liao, Shen Zhang, Pengfei Yin, Bo Han, Pengju Zhao, Xingyu Chen and Xinzheng Lu

Buildings 2022, 12(9), 1295; https://doi.org/10.3390/buildings12091295 - 24 Aug 2022

Cited by 23 | Viewed by 3313

Abstract

The intelligent design method based on generative adversarial networks (GANs) represents an emerging structural design paradigm where design rules are not artificially defined but are directly learned from existing design data. GAN-based methods have exhibited promising potential compared to conventional methods in the [...] Read more.

The intelligent design method based on generative adversarial networks (GANs) represents an emerging structural design paradigm where design rules are not artificially defined but are directly learned from existing design data. GAN-based methods have exhibited promising potential compared to conventional methods in the schematic design phase of reinforced concrete (RC) shear wall structures. However, for the following reasons, it is challenging to apply GAN-based approaches in the industry and to integrate them into the structural design process. (1) The data form of GAN-based methods is heterogeneous from that of the widely used computer-aided design (CAD) methods, and (2) GAN-based methods have high requirements on the hardware and software environment of the user’s computer. As a result, this study proposes an integrated schematic design method for RC shear wall structures, providing a workable GAN application strategy. Specifically, (1) a preprocessing method of architectural CAD drawings is proposed to connect the GAN with the upstream architectural design; (2) a user-friendly cloud design platform is built to reduce the requirements of the user’s local computer environment; and (3) a heterogeneous data transformation method and a parametric modeling procedure are proposed to automatically establish a structural analysis model based on GAN’s design, facilitating downstream detailed design tasks. The proposed method makes it possible for the entire schematic design phase of RC shear wall structures to be intelligent and automated. A case study reveals that the proposed method has a heterogeneous data transformation accuracy of 97.3% and is capable of generating shear wall layout designs similar to the designs of a competent engineer, with 225 times higher efficiency. Full article

(This article belongs to the Special Issue Intelligent and Computer Technologies Application in Construction)

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16 pages, 972 KiB

Open AccessArticle

What Drives the Intelligent Construction Development in China?

by Xiaoli Yan, Yingxue Zhou, Tao Li and Feifei Zhu

Buildings 2022, 12(8), 1250; https://doi.org/10.3390/buildings12081250 - 15 Aug 2022

Cited by 3 | Viewed by 1917

Abstract

Intelligent construction (IC) integrates intelligent technologies with the construction industry to improve efficiency and sustainability. IC development involves many driving factors, but only the critical factors play essential roles. Thus, it is necessary to identify these key factors to understand and promote IC [...] Read more.

Intelligent construction (IC) integrates intelligent technologies with the construction industry to improve efficiency and sustainability. IC development involves many driving factors, but only the critical factors play essential roles. Thus, it is necessary to identify these key factors to understand and promote IC development thoroughly. Although there are many studies on IC-related technologies, a focus on identifying the driving factors of IC is lacking. We aimed to identify the key driving factors for IC development, analyze the relationship between the key factors and IC, and then produce general laws to guide IC by conducting an empirical study in China. We employed a five-stage research design and proposed the following general laws of how the key factors drive the development of IC: (1) initially, there exits the opportunity that drives companies to generate IC; (2) subsequently, the planning and pressure of a firm strategy, structure, and rivalry further drive companies to try to develop IC; (3) afterward, government policy vigorously promotes IC practices of the participating companies and accelerates the development of IC; and (4) finally, the market forces begin to play a leading role, and companies spontaneously carry out IC activities when the policy effect reaches a certain level. The findings indicate that policies to promote IC development should be consistent with its development stage, and the key driving factors of different stages should be paid attention to. Although the context of this study is China, the findings can provide references for IC’s development globally. Full article

(This article belongs to the Special Issue Intelligent and Computer Technologies Application in Construction)

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16 pages, 2944 KiB

Open AccessArticle

Feature-Based Deep Learning Classification for Pipeline Component Extraction from 3D Point Clouds

by Zhao Xu, Rui Kang and Heng Li

Buildings 2022, 12(7), 968; https://doi.org/10.3390/buildings12070968 - 07 Jul 2022

Cited by 3 | Viewed by 1643

Abstract

This paper proposes a novel method for construction component classification by designing a feature-based deep learning network to tackle the automation problem in construction digitization. Although scholars have proposed a variety of ways to achieve the use of deep learning to classify point [...] Read more.

This paper proposes a novel method for construction component classification by designing a feature-based deep learning network to tackle the automation problem in construction digitization. Although scholars have proposed a variety of ways to achieve the use of deep learning to classify point clouds, there are few practical engineering applications in the construction industry. However, in the process of building digitization, the level of manual participation has significantly reduced the efficiency of digitization and increased the application restrictions. To address this problem, we propose a robust classification method using deep learning networks, which is combined with traditional shape features for the point cloud of construction components. The proposed method starts with local and global feature extraction, where global features processed by the neural network and the traditional shape features are processed separately. Then, we generate a feature map and perform deep convolution to achieve feature fusion. Finally, experiments are designed to prove the efficiency of the proposed method based on the construction dataset we establish. This paper fills in the lack of deep learning applications of point clouds in construction component classification. Additionally, this paper provides a feasible solution to improve the construction digitization efficiency and provides an available dataset for future work. Full article

(This article belongs to the Special Issue Intelligent and Computer Technologies Application in Construction)

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21 pages, 3392 KiB

Open AccessArticle

Visual Relationship-Based Identification of Key Construction Scenes on Highway Bridges

by Chen Wang, Jingguo Lv, Yu Geng and Yiting Liu

Buildings 2022, 12(6), 827; https://doi.org/10.3390/buildings12060827 - 14 Jun 2022

Cited by 9 | Viewed by 1702

Abstract

Highway bridges play an important role in traffic construction; however, accidents caused by bridge construction occur frequently, resulting in significant loss of life and property. The identification of bridge construction scenes not only keeps track of the construction progress, but also enables real-time [...] Read more.

Highway bridges play an important role in traffic construction; however, accidents caused by bridge construction occur frequently, resulting in significant loss of life and property. The identification of bridge construction scenes not only keeps track of the construction progress, but also enables real-time monitoring of the construction process and the timely detection of safety hazards. This paper proposes a deep learning method in artificial intelligence (AI) for identifying key construction scenes of highway bridges based on visual relationships. First, based on the analysis of bridge construction characteristics and construction process, five key construction scenes are selected. Then, by studying the underlying features of the five scenes, a construction scene identification feature information table is built, and construction scene identification rules are formulated. Afterward, a bridge key construction scene identification model (CSIN) is built; this model comprises target detection, visual relationship extraction, semantic conversion, scene information fusion, and identification results output. Finally, the effectiveness of the proposed method is verified experimentally. The results show that the proposed method can effectively identify key construction scenes for highway bridges with an accuracy rate of 94%, and enable the remote intelligent monitoring of highway bridge construction processes to ensure that projects are carried out safely. Full article

(This article belongs to the Special Issue Intelligent and Computer Technologies Application in Construction)

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19 pages, 4940 KiB

Open AccessArticle

Automated Selection and Localization of Mobile Cranes in Construction Planning

by Hongling Guo, Ying Zhou, Zaiyi Pan, Zhitian Zhang, Yantao Yu and Yan Li

Buildings 2022, 12(5), 580; https://doi.org/10.3390/buildings12050580 - 29 Apr 2022

Cited by 6 | Viewed by 2686

Abstract

Accurate selection and location of mobile cranes is a critical issue on construction sites, being able to contribute to the improvement of the safety and efficiency of lifting operations. Considering the complexities and dynamics of construction sites, this study aimed to develop a [...] Read more.

Accurate selection and location of mobile cranes is a critical issue on construction sites, being able to contribute to the improvement of the safety and efficiency of lifting operations. Considering the complexities and dynamics of construction sites, this study aimed to develop a useful approach for automated selection and localization of mobile cranes based on the simulation of crane operations. First, the information required for crane selection and localization is analyzed and extracted from BIM (building information modeling). Then, mainly considering the crane capacity, the initial crane type is selected with candidate location points. Based on the simulation of lifting operation at the candidate points, feasible location points and crane types are determined through three constraint checks (i.e., environment constraint, operation constraint, and safety constraint). Besides, two kinds of efficiency optimization, namely lifting time minimization and crane movement minimization, are presented to figure out the best location points from the feasible points. Finally, the proposed approach is validated using a case study. This research contributes to not only crane operation planning but also automatic construction simulation, thus supporting the implementation of intelligent construction in the future. Full article

(This article belongs to the Special Issue Intelligent and Computer Technologies Application in Construction)

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26 pages, 15496 KiB

Open AccessArticle

BIM-Based Dynamic Construction Safety Rule Checking Using Ontology and Natural Language Processing

by Qiyu Shen, Songfei Wu, Yichuan Deng, Hui Deng and Jack C. P. Cheng

Buildings 2022, 12(5), 564; https://doi.org/10.3390/buildings12050564 - 27 Apr 2022

Cited by 14 | Viewed by 2995

Abstract

Real-time identification and prevention of safety risks in dynamic construction activities are demanded by construction safety managers to cope with the growing complexity of the construction site. Most of the studies on BIM-based construction safety inspection and prevention use data from the planning [...] Read more.

Real-time identification and prevention of safety risks in dynamic construction activities are demanded by construction safety managers to cope with the growing complexity of the construction site. Most of the studies on BIM-based construction safety inspection and prevention use data from the planning and design stage. Meanwhile, safety managers still need to spend a lot of time gathering reports about construction safety risks in certain periods or areas from inferred results in BIM. Therefore, this paper proposed an automatic safety risk identification and prevention mechanism for the construction process by integrating a safety rule library based on ontology technology and Natural Language Processing. An automatic inspection mechanism integrating BIM and safety rules is constructed, and a presentation mechanism of intelligent detection results based on Natural Language Processing is designed. The construction process safety rule checking system was developed, and the effectiveness of the system was verified by a case study. The outcome of this paper contributes to the development and application of ontology in construction safety research, and the NLP-based safety rule checking result presentation will benefit safety inspectors and construction managers in practice. Full article

(This article belongs to the Special Issue Intelligent and Computer Technologies Application in Construction)

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25 pages, 7482 KiB

Open AccessArticle

Development of a BIM-Based Framework Using Reverberation Time (BFRT) as a Tool for Assessing and Improving Building Acoustic Environment

by Antonio J. Aguilar, María L. de la Hoz-Torres, Mª Dolores Martínez-Aires and Diego P. Ruiz

Buildings 2022, 12(5), 542; https://doi.org/10.3390/buildings12050542 - 24 Apr 2022

Cited by 3 | Viewed by 2025

Abstract

Both the building design and the construction process determine the indoor acoustic quality of enclosures. A suitable indoor acoustic environment is crucial for the productivity and well-being of users. For this purpose, Reverberation Time (RT) is often calculated or measured in situ. Recently, [...] Read more.

Both the building design and the construction process determine the indoor acoustic quality of enclosures. A suitable indoor acoustic environment is crucial for the productivity and well-being of users. For this purpose, Reverberation Time (RT) is often calculated or measured in situ. Recently, Building Information Modelling (BIM) has provided a new paradigm to face building projects. Nevertheless, little research has been conducted on the optimisation of indoor acoustics using BIM methodology. In this context, the objective of this work is to propose and develop a BIM-based framework for the analysis, evaluation and optimization of the RT. The proposed procedure allows designers to explore alternatives in order to achieve an adequate acoustic performance without any further needs of specific software. This proposal is devised to consider some important characteristics of the project, such as its location, applicable regulations, room uses, materials and costs. This framework calculates the solution set that meets the requirements, showing the set of optimal solutions according to the minimization of both the cost and the optimum absorbent surface area. BFRT contributes by offering a tool to support the decision making process of designers during the initial design phase in the field of acoustic conditioning of buildings. Full article

(This article belongs to the Special Issue Intelligent and Computer Technologies Application in Construction)

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19 pages, 2256 KiB

Open AccessArticle

Research on Factors Influencing Intelligent Construction Development: An Empirical Study in China

by Tao Li, Xiaoli Yan, Wenping Guo and Feifei Zhu

Buildings 2022, 12(4), 478; https://doi.org/10.3390/buildings12040478 - 12 Apr 2022

Cited by 8 | Viewed by 2243

Abstract

Intelligent construction (IC) is an innovative development model of the construction industry in which construction is integrated with digital technologies against the backdrop of the new technological revolution. The development of IC involves many influencing factors which are actively promoting IC development. However, [...] Read more.

Intelligent construction (IC) is an innovative development model of the construction industry in which construction is integrated with digital technologies against the backdrop of the new technological revolution. The development of IC involves many influencing factors which are actively promoting IC development. However, investigations focusing on identifying and examining the relationships among the factors necessary for IC development are limited. In contributing to bridging this gap, this paper investigated and analyzed influencing factors for IC development by developing structural equation modeling (SEM) based on 5 variables and 28 measures, including (1) identifying the factors and examining their influence on IC development in China and (2) clarifying the paths and key measures for successful IC development. The results showed that (1) the three variables of government, company, and technology had a direct and significant impact on the development of IC, (2) the three variables of industry, company, and technology actually formed a “closed-loop” within which they interact and promote each other, and (3) it was widely realized and accepted that IC development has bright prospects in China. Furthermore, four paths for IC development were obtained and the key measures of the five variables were further analyzed. This research contributes to the body of knowledge on IC by identifying the factors influencing IC development. The four paths and key measures were proposed to clarify the relationship between factors. Recommendations were put forward to promote IC development. Full article

(This article belongs to the Special Issue Intelligent and Computer Technologies Application in Construction)

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19 pages, 8146 KiB

Open AccessFeature PaperArticle

A Framework for Prefabricated Component Hoisting Management Systems Based on Digital Twin Technology

by Yuhong Zhao, Cunfa Cao and Zhansheng Liu

Buildings 2022, 12(3), 276; https://doi.org/10.3390/buildings12030276 - 01 Mar 2022

Cited by 22 | Viewed by 3765

Abstract

The hoisting of prefabricated components (PCs) is a key step during the construction of prefabricated buildings. Aiming at the problems existing in the control of PC hoisting, an innovative hoisting management system framework based on the digital twin (DT) is established in this [...] Read more.

The hoisting of prefabricated components (PCs) is a key step during the construction of prefabricated buildings. Aiming at the problems existing in the control of PC hoisting, an innovative hoisting management system framework based on the digital twin (DT) is established in this paper. The system framework comprehensively utilizes the building information model (BIM) and Internet of Things (IoT) to establish a digital twin model (DTm) for PC hoisting control and uses Dijkstra’s algorithm to conduct hoisting route planning according to the BIM data in the model. Meanwhile, long-range radio (LoRa) technology was used for data acquisition and transmission to monitor the movement state of the PCs in the hoisting process. By testing it in a prefabricated building project, the DT control method was conducted to realize the functions of real-time information collection, hoisting path planning and PC positioning, which proved the feasibility and effectiveness of the method. As a key technology to realize intelligent manufacturing, DT has been widely studied in academia. The DTm of the hoisting process of PCs is established in this study; it improves the level of intelligent management of prefabricated building construction and provides a new idea for intelligent building construction. Full article

(This article belongs to the Special Issue Intelligent and Computer Technologies Application in Construction)

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Review

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29 pages, 1424 KiB

Open AccessFeature PaperReview

Green Building Construction: A Systematic Review of BIM Utilization

by Yu Cao, Syahrul Nizam Kamaruzzaman and Nur Mardhiyah Aziz

Buildings 2022, 12(8), 1205; https://doi.org/10.3390/buildings12081205 - 10 Aug 2022

Cited by 23 | Viewed by 7343

Abstract

As a multi-function method, Building Information Modeling (BIM) can assist construction organizations in improving their project’s quality, optimize collaboration efficiency, and reduce construction periods and expenditure. Given the distinguished contributions of BIM utilization, there is a trend that BIM has significant potential to [...] Read more.

As a multi-function method, Building Information Modeling (BIM) can assist construction organizations in improving their project’s quality, optimize collaboration efficiency, and reduce construction periods and expenditure. Given the distinguished contributions of BIM utilization, there is a trend that BIM has significant potential to be utilized in the construction phase of green buildings. Compared with traditional buildings, green buildings have more stringent requirements, including environmental protection, saving energy, and residents’ comfort. Although BIM is deemed an effective method to achieve the abovementioned requirements in the construction process of green buildings, there are few systematic reviews that explore the capabilities of BIM in the construction phase of green buildings. This has hindered the utilization of BIM in the construction of green buildings. To bridge this research gap and review the latest BIM capabilities, this study was developed to perform a systematic review of the BIM capabilities in the construction phase of green buildings. In this systematic review, the PRISMA protocol has been used as the primary procedure for article screening and review. The entire systematic review was performed from January 2022 to April 2022. In this process, 165 articles were included, reviewed, and discussed. Web of Science (WoS) and Scopus were adopted as the databases. Through this systematic review, it can be identified that BIM capabilities have significant advantages in project quality improvement, lifecycle data storage and management, collaboration optimization, planning, and schedule management optimization in the construction phase of green buildings. Through the discussion, it can be concluded that BIM utilization can be adopted from the pre-construction phase to the post-construction stage in the green building construction process. Besides these, the barriers to BIM utilization in the green building construction phase are also revealed in the discussion section, including the non-uniform data format, insufficient interactivity, ambiguous ownership, insufficient BIM training, and hesitation toward BIM adoption. Moreover, the challenges and future directions of BIM utilization in green building construction are identified. The findings of this study can facilitate construction personnel to be acquainted with BIM capabilities in the construction of green buildings to promote the utilization and optimization of BIM capabilities in the green building construction process. Full article

(This article belongs to the Special Issue Intelligent and Computer Technologies Application in Construction)

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