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Intelligence and Automation in Construction Industry
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Intelligence and Automation in Construction Industry

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 30 August 2025 | Viewed by 16762

Special Issue Editors

Dr. Jingjing Guo

E-Mail Website
Guest Editor
College of Civil Engineering, Hunan University, Changsha, 410012, China
Interests: smart construction; defect detection; quality inspection; computer vision; construction management
Prof. Dr. Qian Wang

E-Mail Website
Guest Editor
Department of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing 211189, China
Interests: BIM; LiDAR; quality inspection; 3D reconstruction
Dr. Weiwei Chen

E-Mail Website
Guest Editor
1. Bartlett School of Sustainable Construction, University College London, 1-19 Torrington Pl, London WC1E 7HB, UK
2. Department of Engineering, The University of Cambridge, Cambridge CB3 0FA, UK
Interests: HBIM; digital twins; point cloud; immersive technologies; data management; digital construction; facility management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the integration of intelligent technologies and automation has revolutionized various industries, leading to unprecedented efficiencies and advancements. However, the construction industry, in comparison, has seen limited enhancements in productivity and may even face challenges in this regard. Given the labor-intensive nature of construction, the implementation of artificial intelligence, robotics, and smart technologies has the potential to significantly reduce labor costs while simultaneously enhancing productivity and quality. Crucially, these technologies can contribute to a safer working environment by automating hazardous tasks. Therefore, research in the field of construction is crucial in order to understand the potential benefits and challenges associated with intelligence and automation technologies, and to formulate strategies for their effective and successful implementation. 

The primary aim of this Special Issue is to explore the recent developments and challenges associated with the application of intelligence and automation in construction. Topics include, but are not limited to, the following:

  • Robotics for Construction
  • Computer vision-based construction quality inspection
  • Planning of intelligence and automation techniques
  • Smart construction management
  • AI-driven decision support systems in construction
  • Human–Machine collaboration
  • Intelligent algorithms for construction data analysis
  • Investigation of the challenges in smart construction

Dr. Jingjing Guo
Prof. Dr. Qian Wang
Dr. Weiwei Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • automation
  • robotics
  • human-machine collaboration
  • smart construction

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (9 papers)

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Research

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28 pages, 7956 KiB  
Article
Enabling High-Level Worker-Centric Semantic Understanding of Onsite Images Using Visual Language Models with Attention Mechanism and Beam Search Strategy
by Hui Deng, Kejie Fu, Binglin Yu, Huimin Li, Rui Duan, Yichuan Deng and Jia-rui Lin
Buildings 2025, 15(6), 959; https://doi.org/10.3390/buildings15060959 - 18 Mar 2025
Viewed by 541
Abstract
Visual information is becoming increasingly essential in construction management. However, a significant portion of this information remains underutilized by construction managers due to the limitations of existing image processing algorithms. These algorithms primarily rely on low-level visual features and struggle to capture high-order [...] Read more.
Visual information is becoming increasingly essential in construction management. However, a significant portion of this information remains underutilized by construction managers due to the limitations of existing image processing algorithms. These algorithms primarily rely on low-level visual features and struggle to capture high-order semantic information, leading to a gap between computer-generated image semantics and human interpretation. However, current research lacks a comprehensive justification for the necessity of employing scene understanding algorithms to address this issue. Moreover, the absence of large-scale, high-quality open-source datasets remains a major obstacle, hindering further research progress and algorithmic optimization in this field. To address this issue, this paper proposes a construction scene visual language model based on attention mechanism and encoder–decoder architecture, with the encoder built using ResNet101 and the decoder built using LSTM (long short-term memory). The addition of the attention mechanism and beam search strategy improves the model, making it more accurate and generalizable. To verify the effectiveness of the proposed method, a publicly available construction scene visual-language dataset containing 16 common construction scenes, SODA-ktsh, is built and verified. The experimental results demonstrate that the proposed model achieves a BLEU-4 score of 0.7464, a CIDEr score of 5.0255, and a ROUGE_L score of 0.8106 on the validation set. These results indicate that the model effectively captures and accurately describes the complex semantic information present in construction images. Moreover, the model exhibits strong generalization, perceptual, and recognition capabilities, making it well suited for interpreting and analyzing intricate construction scenes. Full article
(This article belongs to the Special Issue Intelligence and Automation in Construction Industry)
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22 pages, 8312 KiB  
Article
Evaluating Radiance Field-Inspired Methods for 3D Indoor Reconstruction: A Comparative Analysis
by Shuyuan Xu, Jun Wang, Jingfeng Xia and Wenchi Shou
Buildings 2025, 15(6), 848; https://doi.org/10.3390/buildings15060848 - 7 Mar 2025
Viewed by 1005
Abstract
An efficient and robust solution for 3D indoor reconstruction is crucial for various managerial operations in the Architecture, Engineering, and Construction (AEC) sector, such as indoor asset tracking and facility management. Conventional approaches, primarily relying on SLAM and deep learning techniques, face certain [...] Read more.
An efficient and robust solution for 3D indoor reconstruction is crucial for various managerial operations in the Architecture, Engineering, and Construction (AEC) sector, such as indoor asset tracking and facility management. Conventional approaches, primarily relying on SLAM and deep learning techniques, face certain limitations. With the recent emergence of radiance field (RF)-inspired methods, such as Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS), it is worthwhile to evaluate their capability and applicability for reconstructing built environments in the AEC domain. This paper aims to compare different RF-inspired methods with conventional SLAM-based methods and to assess their potential use for asset management and related downstream tasks in indoor environments. Experiments were conducted in university and laboratory settings, focusing on 3D indoor reconstruction and semantic asset segmentation. The results indicate that 3DGS and Nerfacto generally outperform other NeRF-based methods. In addition, this study provides guidance on selecting appropriate reconstruction approaches for specific use cases. Full article
(This article belongs to the Special Issue Intelligence and Automation in Construction Industry)
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20 pages, 6179 KiB  
Article
Non-Contact Dimensional Quality Inspection System of Prefabricated Components Using 3D Matrix Camera
by Wanqing Lyu, Xiwang Chen, Wenlong Han, Kun Ni, Rui Jing, Lin Tong, Junzheng Pan and Qian Wang
Buildings 2025, 15(5), 837; https://doi.org/10.3390/buildings15050837 - 6 Mar 2025
Viewed by 768
Abstract
Dimensional quality inspection of prefabricated components is crucial for ensuring building quality and safety. Currently, manual measurement methods are predominantly used in dimensional quality inspection of prefabricated components, which are both time-consuming and labor-intensive, constraining production efficiency. This study thus developed a non-contact [...] Read more.
Dimensional quality inspection of prefabricated components is crucial for ensuring building quality and safety. Currently, manual measurement methods are predominantly used in dimensional quality inspection of prefabricated components, which are both time-consuming and labor-intensive, constraining production efficiency. This study thus developed a non-contact image measurement system using an innovative three-dimensional (3D) matrix camera, which automatically performed dimensional quality inspection, utilizing technologies such as a parallel optical axis four-camera matrix imaging and machine learning algorithms. Compared to traditional techniques, this system exhibited enhanced adaptability to the manufacturing process of prefabricated components, along with desirable accuracy and efficiency. Building upon a comprehensive literature review, the hardware constituents of the 3D matrix camera image measurement system were meticulously introduced, followed by the underlying principles and implementations of data acquisition, processing and comparison methods, including parallel optical axis four-camera matrix imaging, automatic stitching algorithms for 3D point clouds, feature recognition algorithms, and matching principles. The feasibility of the proposed system was validated through a case study analysis. The application results indicated that the system was capable of automatically performing non-contact measurements of dimensional deviations in prefabricated components with an accuracy of ±3 mm, thereby enhancing production quality. Full article
(This article belongs to the Special Issue Intelligence and Automation in Construction Industry)
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23 pages, 8757 KiB  
Article
The Automated Inspection of Precast Utility Tunnel Segments for Geometric Quality Based on the BIM and LiDAR
by Zhigang Guo, Gang Wang, Zhengxiong Liu, Lingfeng Liu, Yakun Zou, Shengzhen Li, Ran Yang, Xin Hu, Shenghan Li and Daochu Wang
Buildings 2024, 14(9), 2717; https://doi.org/10.3390/buildings14092717 - 30 Aug 2024
Cited by 2 | Viewed by 1384
Abstract
The quality inspection of each precast utility tunnel segment is crucial, especially the cross-sectional dimensions and surface smoothness, since they influence the assembly precision at the construction site. Traditional manual inspection methods are not only time-consuming and costly but also limited in accuracy. [...] Read more.
The quality inspection of each precast utility tunnel segment is crucial, especially the cross-sectional dimensions and surface smoothness, since they influence the assembly precision at the construction site. Traditional manual inspection methods are not only time-consuming and costly but also limited in accuracy. In order to achieve a high-precision and high-efficiency geometric quality inspection for multi-type precast utility tunnel segments, this paper proposes an automated inspection method based on the Building Information Model (BIM) and Light Detection and Ranging (LiDAR). Initially, the point cloud data (PCD) of the precast utility tunnel segment are acquired through LiDAR and preprocessed to obtain independent point clouds of the precast utility tunnel segment. Then, the shape of the precast utility tunnel segment is identified using the proposed Cross-Sectional Geometric Ratio Feature Identification (CSGRFI) algorithm. Subsequently, the geometric features of the components are extracted based on preset conditions, and the geometric dimensions are calculated. Finally, the quality inspection results are obtained by comparing with the design information provided by the BIM. The proposed method was validated in a real precast component factory. The results indicate that the method achieved a 100% success rate in identifying the cross-sectional shapes of the segments. Compared with the manual measurement method, the proposed method demonstrated a higher accuracy in the geometric quality assessment and an improved time efficiency by 44%. The proposed method enables the efficient geometric quality inspection of tunnel segments, effectively addressing the construction industry’s need for large-scale, high-quality tunnel projects. Full article
(This article belongs to the Special Issue Intelligence and Automation in Construction Industry)
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23 pages, 13391 KiB  
Article
Enhancing Open BIM Interoperability: Automated Generation of a Structural Model from an Architectural Model
by Tandeep Singh, Mojtaba Mahmoodian and Shasha Wang
Buildings 2024, 14(8), 2475; https://doi.org/10.3390/buildings14082475 - 10 Aug 2024
Cited by 3 | Viewed by 2874
Abstract
Building information modelling (BIM) is an appreciated technology in the field of architecture and construction management. Collaboration of information in BIM has not been fully utilized in the structural engineering stream as many engineers keep on working with previous prevailing design approaches. Failure [...] Read more.
Building information modelling (BIM) is an appreciated technology in the field of architecture and construction management. Collaboration of information in BIM has not been fully utilized in the structural engineering stream as many engineers keep on working with previous prevailing design approaches. Failure to adequately facilitate automation in design could lead to structural defects, construction rework, or even structural clashes, with major financial implications. Given the inherent complexity of large-scale construction projects, the ‘manual design and detailing’ of structure is a challenging task and prone to human errors. Against this backdrop, this study developed a 4D building information management approach to facilitate automated structural models for professionals designing all the elements required in reinforced concrete (RC) structures like slabs, beams, and columns. The main contribution of this study is to obtain structural models directly from architecture models automatically, which reduces effort and possible errors in the previous prevailing approaches. The framework enables execution of all the model design works automatically through coding. This is achieved by executing a script which is beneficial for integrated project delivery (IPD). The 3D structural model in BIM software presented in this study extracts and transfers the geometrical data and links these data in Industry Foundation Classes (IFC) files using integration facilitated by Python 3.6 and IFCopenshell. The developed automated programme framework offers a cost-effective and accurate methodology to address the limitations and inefficiencies of traditional methods of structural modelling, which had been carried out manually. The authors have developed a novel tool to extract structural models from architectural models without proprietary software, greatly benefiting BIM managers by enhancing 3D BIM models. This advancement toward Open BIM, crucial for the architecture, engineering, and construction (AEC) industry’s future, is accessible to educators and beginners and highlights BIM’s effectiveness in improving structural analysis and productivity. The core finding of this study is to generate a structural model from an architecture model by automating the script with Python integration of IFC and IFCopenshell. The merits of the developed framework are reduced clashes, more economical structural modelling, and fully automated smart work as functions of the IPD. Full article
(This article belongs to the Special Issue Intelligence and Automation in Construction Industry)
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23 pages, 14390 KiB  
Article
Multi-Task Intelligent Monitoring of Construction Safety Based on Computer Vision
by Lingfeng Liu, Zhigang Guo, Zhengxiong Liu, Yaolin Zhang, Ruying Cai, Xin Hu, Ran Yang and Gang Wang
Buildings 2024, 14(8), 2429; https://doi.org/10.3390/buildings14082429 - 6 Aug 2024
Cited by 3 | Viewed by 2673
Abstract
Effective safety management is vital for ensuring construction safety. Traditional safety inspections in construction heavily rely on manual labor, which is both time-consuming and labor-intensive. Extensive research has been conducted integrating computer-vision technologies to facilitate intelligent surveillance and improve safety measures. However, existing [...] Read more.
Effective safety management is vital for ensuring construction safety. Traditional safety inspections in construction heavily rely on manual labor, which is both time-consuming and labor-intensive. Extensive research has been conducted integrating computer-vision technologies to facilitate intelligent surveillance and improve safety measures. However, existing research predominantly focuses on singular tasks, while construction environments necessitate comprehensive analysis. This study introduces a multi-task computer vision technology approach for the enhanced monitoring of construction safety. The process begins with the collection and processing of multi-source video surveillance data. Subsequently, YOLOv8, a deep learning-based computer vision model, is adapted to meet specific task requirements by modifying the head component of the framework. This adaptation enables efficient detection and segmentation of construction elements, as well as the estimation of person and machine poses. Moreover, a tracking algorithm integrates these capabilities to continuously monitor detected elements, thereby facilitating the proactive identification of unsafe practices on construction sites. This paper also presents a novel Integrated Excavator Pose (IEP) dataset designed to address the common challenges associated with different single datasets, thereby ensuring accurate detection and robust application in practical scenarios. Full article
(This article belongs to the Special Issue Intelligence and Automation in Construction Industry)
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20 pages, 8282 KiB  
Article
An Improved Trilateral Localization Technique Fusing Extended Kalman Filter for Mobile Construction Robot
by Lingdong Zeng, Shuai Guo, Mengmeng Zhu, Hao Duan and Jie Bai
Buildings 2024, 14(4), 1026; https://doi.org/10.3390/buildings14041026 - 7 Apr 2024
Cited by 5 | Viewed by 1808
Abstract
Semi-open and chaotic environments of building sites are considered primary challenges for the localization of mobile construction robots. To mitigate environmental limitations, an improved trilateral localization technique based on artificial landmarks fusing the extended Kalman filters (EKFs) is proposed in this paper. The [...] Read more.
Semi-open and chaotic environments of building sites are considered primary challenges for the localization of mobile construction robots. To mitigate environmental limitations, an improved trilateral localization technique based on artificial landmarks fusing the extended Kalman filters (EKFs) is proposed in this paper. The reflective intensity of the onboard laser is employed to identify artificial landmarks arranged in the ongoing construction environment. A trilateral positioning algorithm is then adopted and evaluated based on artificial landmarks. Multi-sensor fusion, combined with the EKF, is included to improve the positioning accuracy and reliability of the robot in complex conditions. We constructed validation scenarios in the Gazebo simulation environment to verify the required localization functionality. Concurrently, we established simulated testing environments in real-world settings, where the practicality of the proposed technique was validated through the fitting of ideal and actual localization trajectories. The effectiveness of the proposed technique was corroborated through comparative experimental results. Full article
(This article belongs to the Special Issue Intelligence and Automation in Construction Industry)
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24 pages, 1283 KiB  
Article
Synergistic Relationship, Agent Interaction, and Knowledge Coupling: Driving Innovation in Intelligent Construction Technology
by Wei Chen, Mingyu Yu and Jia Hou
Buildings 2024, 14(2), 542; https://doi.org/10.3390/buildings14020542 - 18 Feb 2024
Cited by 3 | Viewed by 2027
Abstract
The core driving force behind innovation in intelligent construction technology is synergistic relationships. It has become common practice to promote synergistic innovation through agent interaction and knowledge coupling in the development of intelligent construction technology. Drawing upon synergetics, social network theory, and the [...] Read more.
The core driving force behind innovation in intelligent construction technology is synergistic relationships. It has become common practice to promote synergistic innovation through agent interaction and knowledge coupling in the development of intelligent construction technology. Drawing upon synergetics, social network theory, and the knowledge base view as theoretical frameworks, this research examines the impact of synergistic relationship, agent interaction, and knowledge coupling on innovation in intelligent construction technology. An empirical analysis of 186 questionnaires revealed the following: (1) regarding synergistic relationships, both horizontal synergy and vertical synergy significantly positively impact innovation in intelligent construction technology. (2) Concerning agent interaction, strong interaction serves as a mediator between horizontal synergy and innovation in intelligent construction technology, while weak interaction serves as a mediator between vertical synergy and innovation in intelligent construction technology. (3) Knowledge coupling has a positive moderating effect on innovation in intelligent construction technology under a strong interaction and a negative moderating effect on innovation in intelligent construction technology under a weak interaction. This study contributes to expanding the theory of synergistic relationships and its application in the context of intelligent construction technology. Furthermore, it provides practical insights and guidance for construction companies seeking to enhance innovation in intelligent construction technology through the utilization of agent interaction and knowledge coupling. Full article
(This article belongs to the Special Issue Intelligence and Automation in Construction Industry)
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Review

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21 pages, 3188 KiB  
Review
A Science Mapping Approach-Based Review of Construction Workers’ Safety-Related Behavior
by Jing Feng, Xin Gao, Hujun Li, Baijian Liu and Xiaoying Tang
Buildings 2024, 14(4), 1162; https://doi.org/10.3390/buildings14041162 - 19 Apr 2024
Cited by 3 | Viewed by 2358
Abstract
Promoting safe behaviors among construction workers and mitigating unsafe behaviors is an effective approach to enhancing safety performance in the construction industry. Although progress has been made, the research themes concerning construction workers’ safety-related behaviors (CWSRB) and the detailed progress of [...] Read more.
Promoting safe behaviors among construction workers and mitigating unsafe behaviors is an effective approach to enhancing safety performance in the construction industry. Although progress has been made, the research themes concerning construction workers’ safety-related behaviors (CWSRB) and the detailed progress of each theme remain unclear due to differences in review perspectives and conceptual scopes. This study utilized CiteSpace software (V6.2R3 version) to conduct an analysis of co-authorship networks, co-word networks, and co-citations on 563 published articles in this field from 2013 to 2023. This study’s outcomes highlight several key insights: (1) journals such as Safety Science play a pivotal role in the domain; (2) institutions such as the City University of Hong Kong and Hong Kong Polytechnic University, along with prolific authors like Li, are major contributors to the field; (3) the focus of research has evolved from early organizational factors towards a more diverse range of topics, with deep learning emerging as a significant current research hotspot; (4) this study has identified high-cited literature and 11 primary clusters within the field. Current research focuses on five areas: safety-related behavior concepts, influencing factors and consequences, formation mechanisms, interventions, and applications of new technologies. Establishing clear classification criteria for unsafe behaviors, comprehensively understanding the formation mechanisms of safety-related behaviors, evaluating the effectiveness of intervention strategies, and exploring the practical applications of new technologies are future research directions. This study provides researchers with a holistic view of the present state of research and potential avenues for future exploration, thereby deepening the knowledge and comprehension of stakeholders within this domain. Full article
(This article belongs to the Special Issue Intelligence and Automation in Construction Industry)
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