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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 35702

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Dr. Shuai Han

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Guest Editor

Faculty of Construction and Environment, Hong Kong Polytechnic University, Kowloon 100872, Hong Kong
Interests: construction informatics; artificial intelligence; building information modeling; automation in construction
Special Issues, Collections and Topics in MDPI journals

Dr. Yantao Yu

E-Mail Website
Guest Editor

Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, China
Interests: construction engineering; construction informatics and automation; occupational safety and health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced technologies such as Digitalization, Simulation, Internet of Things, and Artificial Intelligence, are promoting the transformation and innovation of various industries, and the construction industry is no exception. In this context, smart construction comes into being, and is playing a more and more important role in modern society as it overcomes the drawbacks of traditional construction technologies in low efficiency, high pollution, and high energy consumption. Smart construction has entered into a booming period, but also with many challenging issues, especially many interdisciplinary-related problems which need further breakthroughs.

In this Special Issue “Advanced Technologies in Smart Construction and Artificial Intelligence”, we encourage researchers and practitioners to share their knowledge, creative ideas, research results, technologies, and methods related to smart construction. The articles may address, but are not limited to, the following subjects:

Artificial Intelligence in Construction
BIM
Civil Engineering
Structural Safety & Health Monitoring
Construction Environment Perception
Information Management
Personnel Safety & Health Monitoring
Precision Measurement & Control
Mechanization & Robot
Prefabrication & 3D Print
Simulation

Dr. Shuai HAN
Dr. Yantao Yu
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

automation in construction
machine learning
digital twin
building information model
physical information system
construction robot
simulation
computer vision
signal processing
sensoring

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Published Papers (10 papers)

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Research

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15 pages, 10002 KiB

Open AccessArticle

Building Surface Defect Detection Using Machine Learning and 3D Scanning Techniques in the Construction Domain

by Alexandru Marin Mariniuc, Dorian Cojocaru and Marian Marcel Abagiu

Buildings 2024, 14(3), 669; https://doi.org/10.3390/buildings14030669 - 2 Mar 2024

Cited by 7 | Viewed by 3665

Abstract

The rapid growth of the real estate market has led to the appearance of more and more residential areas and large apartment buildings that need to be managed and maintained by a single real estate developer or company. This scientific article details the development of a novel method for inspecting buildings in a semi-automated manner, thereby reducing the time needed to assess the requirements for the maintenance of a building. This paper focuses on the development of an application which has the purpose of detecting imperfections in a range of building sections using a combination of machine learning techniques and 3D scanning methodologies. This research focuses on the design and development of a machine learning-based application that utilizes the Python programming language and the PyTorch library; it builds on the team′s previous study, in which they investigated the possibility of applying their expertise in creating construction-related applications for real-life situations. Using the Zed camera system, real-life pictures of various building components were used, along with stock images when needed, to train an artificial intelligence model that could identify surface damage or defects such as cracks and differentiate between naturally occurring elements such as shadows or stains. One of the goals is to develop an application that can identify defects in real time while using readily available tools in order to ensure a practical and affordable solution. The findings of this study have the potential to greatly enhance the availability of defect detection procedures in the construction sector, which will result in better building maintenance and structural integrity. Full article

(This article belongs to the Special Issue Advanced Technologies in Smart Construction and Artificial Intelligence)

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19 pages, 2700 KiB

Open AccessArticle

Identification of Safety Risk Factors in Metro Shield Construction

by Chao Tang, Chuxiong Shen, Jiaji Zhang and Zeng Guo

Buildings 2024, 14(2), 492; https://doi.org/10.3390/buildings14020492 - 9 Feb 2024

Cited by 6 | Viewed by 2135

Abstract

Among the construction methods for subway projects, shield method construction technology has become a more widely used construction method for urban subway construction due to the advantages of a high degree of construction mechanization, low impact of the construction process on the environment, and strong adaptability of the shield machine to the stratum, etc. However, because of the complexity of the surrounding buildings (structures) in the subway construction, coupled with the diversity of the subway shield method construction activities and the uncertainties in the construction environment, to a certain extent, it is determined that the subway construction process is very complicated. The purpose of this study is based on the text mining method, where text is mined and utilized to realize the identification, extraction, and display of safety risk factors. Thus, it guides the safety management on site and provides a basis for knowledge reuse in other metro shield construction projects. Firstly, we analyze the shortcomings of safety risk management in domestic and international metro shield construction via a literature review, especially the utilization of safety risk text data. Secondly, we collect the risk reports submitted by all parties via the “Metro Project Safety Risk Early Warning System”, and manually screen the hidden danger statements with risk characterization to establish a corpus. Thirdly, we use the Jieba word separation package to extract and display the safety risk factors, so as to guide the on-site safety management. Subsequently, with the help of the Jieba word segmentation package for Chinese word segmentation, we develop a professional thesaurus to improve the effect of word segmentation; then, we use the TF-IDF parameter assignment to achieve the structural transformation of the text to extract high-frequency vocabulary; finally, from the high-frequency vocabulary to screen words containing the semantics of the risk to establish the risk of an initial set of words, we use the existing standards and norms to form the collection of safety risk factors of subway shield construction and generate the cloud diagram for visual display. Full article

(This article belongs to the Special Issue Advanced Technologies in Smart Construction and Artificial Intelligence)

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24 pages, 15311 KiB

Open AccessEditor’s ChoiceArticle

Robot-Enabled Construction Assembly with Automated Sequence Planning Based on ChatGPT: RoboGPT

by Hengxu You, Yang Ye, Tianyu Zhou, Qi Zhu and Jing Du

Buildings 2023, 13(7), 1772; https://doi.org/10.3390/buildings13071772 - 12 Jul 2023

Cited by 27 | Viewed by 6933

Abstract

Robot-based assembly in construction has emerged as a promising solution to address numerous challenges such as increasing costs, labor shortages, and the demand for safe and efficient construction processes. One of the main obstacles in realizing the full potential of these robotic systems is the need for effective and efficient sequence planning for construction tasks. Current approaches, including mathematical and heuristic techniques or machine learning methods, face limitations in their adaptability and scalability to dynamic construction environments. To expand the current robot system’s sequential understanding ability, this paper introduces RoboGPT, a novel system that leverages the advanced reasoning capabilities of ChatGPT, a large language model, for automated sequence planning in robot-based assembly applied to construction tasks. The proposed system adapts ChatGPT for construction sequence planning and demonstrates its feasibility and effectiveness through experimental evaluation including two case studies and 80 trials involving real construction tasks. The results show that RoboGPT-driven robots can handle complex construction operations and adapt to changes on the fly. This paper contributes to the ongoing efforts to enhance the capabilities and performance of robot-based assembly systems in the construction industry, and it paves the way for further integration of large language model technologies in the field of construction robotics. Full article

(This article belongs to the Special Issue Advanced Technologies in Smart Construction and Artificial Intelligence)

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18 pages, 1500 KiB

Open AccessArticle

Quantitative Model of Multi-Subject Quality Responsibility in General Contracting Projects Based on Sailed Fish Optimizer

by Mingfei Chen, Ying He and Jie Gao

Buildings 2023, 13(6), 1375; https://doi.org/10.3390/buildings13061375 - 25 May 2023

Viewed by 1177

Abstract

In order to address the issue of the quantitative allocation of quality responsibility among different subjects in engineering general contracting projects, this paper proposed a quantitative model (M-ResQu) for multi-subject quality responsibility allocation based on quality behavior classification criteria. Firstly, utility theory and game theory were used to establish a behavioral choice model for construction units and general contractors, investigating the quality behavioral choice mechanisms in the general contracting mode. Secondly, the sailed fish optimizer (SFO) was used to screen potential laws across 84 practical judicial cases and obtain the type coefficients of three types of quality risk behaviors: technical defects, non-compliance management and non-standard behaviors. Thirdly, a fuzzy mathematics theory was employed to establish the M-ResQu model for multi-subject quality responsibility allocation in general contracting mode. Finally, a simulation analysis was conducted to demonstrate the applicability of the M-ResQu model, and results suggested that it can provide a valuable quantitative tool for quality dispute resolution in the general contracting mode. Full article

(This article belongs to the Special Issue Advanced Technologies in Smart Construction and Artificial Intelligence)

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20 pages, 4017 KiB

Open AccessArticle

Synthetic Datasets for Rebar Instance Segmentation Using Mask R-CNN

by Haoyu Wang, Zhiming Ye, Dejiang Wang, Haili Jiang and Panpan Liu

Buildings 2023, 13(3), 585; https://doi.org/10.3390/buildings13030585 - 22 Feb 2023

Cited by 17 | Viewed by 3357

Abstract

The construction and inspection of reinforcement rebar currently rely entirely on manual work, which leads to problems such as high labor requirements and labor costs. Rebar image detection using deep learning algorithms can be employed in construction quality inspection and intelligent construction; it can check the number, spacing, and diameter of rebar on a construction site, and guide robots to complete rebar tying. However, the application of deep learning algorithms relies on a large number of datasets to train models, while manual data collection and annotation are time-consuming and laborious. In contrast, using synthetic datasets can achieve a high degree of automation of annotation. In this study, using rebar as an example, we proposed a mask annotation methodology based on BIM software and rendering software, which can establish a large and diverse training set for instance segmentation, without manual labeling. The Mask R-CNN trained using both real and synthetic datasets demonstrated a better performance than the models trained using only real datasets or synthetic datasets. This synthetic dataset generation method could be widely used for various image segmentation tasks and provides a reference for other computer vision engineering tasks and deep learning tasks in related fields. Full article

(This article belongs to the Special Issue Advanced Technologies in Smart Construction and Artificial Intelligence)

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21 pages, 2753 KiB

Open AccessArticle

Influence of Safety Experience and Environmental Conditions on Site Hazard Identification Performance

by Xiazhong Zheng, Yu Wang, Yun Chen, Qin Zeng and Lianghai Jin

Buildings 2023, 13(1), 251; https://doi.org/10.3390/buildings13010251 - 16 Jan 2023

Cited by 6 | Viewed by 3547

Abstract

Improving the hazard identification ability of workers is an important way to reduce safety accidents at construction sites. Although previous studies have succeeded in improving hazard identification performance, an important gap is that they consider only two factors, the worker’s safety experience and objective environmental conditions, to analyze the impact on hazard identification performance. To fill the above gap, a visual cognitive model of hazard identification was established. Sixteen field scenes were selected to represent construction sites in each environmental condition. Eye-movement data were extracted through eye-tracking experiments, and the differences between experts’ and novices’ gazes during danger recognition in these scenes were analyzed. The results indicate the following: bright construction sites can significantly improve the correct recognition rate and information processing in hazard identification; tidy construction sites can improve the search efficiency and correct recognition rate of hazard identification; safety experience can improve workers’ correct recognition rates and information processing; and reducing distractions can effectively improve the correct identification rate of hazards. Overall, optimal site brightness needs to be further studied to improve the efficiency of hazard search and reduce the distraction effect. This study provides recommendations for the direction of safety training and safety management on site. Full article

(This article belongs to the Special Issue Advanced Technologies in Smart Construction and Artificial Intelligence)

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19 pages, 4862 KiB

Open AccessArticle

Research on Design Framework of Middle School Teaching Building Based on Performance Optimization and Prediction in the Scheme Design Stage

by Meng Wang, Shuqi Cao, Daxing Chen, Guohua Ji, Qiang Ma and Yucheng Ren

Buildings 2022, 12(11), 1897; https://doi.org/10.3390/buildings12111897 - 5 Nov 2022

Cited by 7 | Viewed by 3354

Abstract

The good indoor light environment and comfort of the teaching space are very important for students’ physical and mental health. Meanwhile, China advocates energy conservation and emission reduction policies. However, in order to obtain lower building energy consumption, higher thermal comfort, and daylighting, architects use performance simulation software to repeatedly simulate and refine, which is time-consuming and difficult to obtain the best results from three performances. Given this problem, we constructed the design framework in the early stage of the architectural design of the teaching building. In the first stage of the framework, architects optimized the performance objectives of lighting, thermal comfort, and energy consumption, and performed a cluster analysis on the optimized non-dominated solution to provide a reference for the architect. In the second stage of the framework, architects used the data generated in the optimization process to train the BP neural network and use the trained BP neural network to predict the performance of the building. In this paper, we selected Nanjing Donglu Middle School as a case study. The optimization of the building performance was assessed by a genetic algorithm, generating 3000 sets of sample data during the optimization iteration. Then, we analyzed the non-dominated solution of the sample data through the method of cluster analysis and trained the BP neural network with the sample data as a data set. The prediction model with R-values of 0.998 in the training set and test set was obtained by repeatedly debugging the number of neurons in the BP neural network. Finally, five groups of design parameters were randomly selected and brought into the trained BP neural network, and the predictive value was close to the simulated value. The construction of the framework provides design ideas for architects in the early teaching of building design and helps designers to make better decisions. Full article

(This article belongs to the Special Issue Advanced Technologies in Smart Construction and Artificial Intelligence)

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15 pages, 5550 KiB

Open AccessArticle

Experimental Research on the Compression Property of Geopolymer Concrete with Molybdenum Tailings as a Building Material

by Ming Sun, Yin Fu, Weixin Wang, Youzhi Yang and An Wang

Buildings 2022, 12(10), 1596; https://doi.org/10.3390/buildings12101596 - 3 Oct 2022

Cited by 21 | Viewed by 2065

Abstract

This paper experimentally studied the effects of different molybdenum tailings (MoT) content, standard curing and 60 °C water curing conditions on the compressive strength of fly ash-based geopolymers at different ages. X-ray diffraction (XRD), scanning electron microscopy/energy dispersive spectrometer (SEM/EDS) and Fourier-transform infrared spectroscopy (FTIR) were applied to investigate the effect of the content of MoT and different curing conditions on the reaction products, microstructure and chemical composition of fly ash-based geopolymers. The results show that MoT content and curing conditions have synergistic effects on the compressive strength of fly ash-based geopolymers. For standard curing, the increase in MoT content is detrimental to the development of compressive strength, and an obvious weak interfacial transition zone between MoT and the gel product is observed in specimen containing 40 wt% MoT; meanwhile, under water curing conditions, the compressive strength of geopolymers first increases and then decreases with the increase in MoT, and the 28-day compressive strength can reach 90.3 MPa when the content of MoT is 10 wt%. The SEM results show that the curing conditions have a great influence on the microstructure of the geopolymer matrix, and the microstructure of the specimens under the water curing conditions is smoother and denser, with fewer pores. EDS analyses show that the gel product constituting the geopolymer matrix is N(C)-A-S-H gel; MoT can participate in the reaction, and the mass ratio of Ca/(Si + Al) of N(C)-A-S-H gel increases with the increase in MoT, resulting in a decrease in compressive strength. In addition, the results of the FTIR confirm that water curing can increase the degree of crosslinks in the gel phase. Full article

(This article belongs to the Special Issue Advanced Technologies in Smart Construction and Artificial Intelligence)

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20 pages, 5546 KiB

Open AccessArticle

Big Data-Based Performance Analysis of Tunnel Boring Machine Tunneling Using Deep Learning

by Ye Zhang, Jinqiao Chen, Shuai Han and Bin Li

Buildings 2022, 12(10), 1567; https://doi.org/10.3390/buildings12101567 - 29 Sep 2022

Cited by 8 | Viewed by 3735

Abstract

In tunnel boring machine (TBM) construction, the advance rate is a crucial parameter that affects the TBM driving efficiency, project schedule, and construction cost. During the operation process, various types of indicators that are monitored in real-time can help to control the advance rate of TBM. Although some studies have already been carried out in advance rate prediction, the research is almost all based on statistical methods and shallow machine learning algorithms, thereby having difficulties in dealing with a very large amount of monitored data and in modeling the time-dependent characteristics of the parameters. To solve this problem, a deep learning model is proposed based on the CNN architecture, bidirectional Long Short-Term Memory module, and the attention mechanism, which is called the CNN-Bi-LSTM-Attention model. In the first step, the monitored data is processed, and the CNN architecture is adopted to extract features from the data sequence. Then the Bi-LSTM module is adopted to obtain the time-dependent indicators. The significant features can be addressed by the added attention mechanism. In the model training process, the rotation speed of the cutter head (N), thrust (F), torque (T), penetration rate (P), and chamber earth pressure (Soil_P) are adopted to predict the advance rate. The influence of the training periods on the model performance is also discussed. The result shows that not only the data amount, but also the data periods have an influence on the prediction. The long-term data may lead to a failure of the advance rate of TBM. The model evaluation result on the test data shows that the proposed model cannot predict the monitored data in the starting stage, which denotes that the working state of TBM in the starting stage is not stable. Especially when the TBM starts to work, the prediction error is big. The proposed model is also compared with several traditional machine methods, and the result shows the excellent performance of the proposed model. Full article

(This article belongs to the Special Issue Advanced Technologies in Smart Construction and Artificial Intelligence)

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Review

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20 pages, 12920 KiB

Open AccessReview

Four-Dimensional (4D) Millimeter Wave-Based Sensing and Its Potential Applications in Digital Construction: A Review

by Shuai Han, Jiawen Zhang, Zeeshan Shahid Shaikh, Jia Wang and Wei Ren

Buildings 2023, 13(6), 1454; https://doi.org/10.3390/buildings13061454 - 2 Jun 2023

Cited by 3 | Viewed by 3974

Abstract

Digital construction relies on effective sensing to enhance the safety, productivity, and quality of its activities. However, current sensing devices (e.g., camera, LiDAR, infrared sensors) have significant limitations in different aspects. In light of the substantial advantages offered by emerging 4D mmw technology, it is believed that this technology can overcome these limitations and serve as an excellent complement to current construction sensing methods due to its robust imaging capabilities, spatial sensing abilities, velocity measurement accuracy, penetrability features, and weather resistance properties. To support this argument, a scientometric review of 4D mmw-based sensing is conducted in this study. A total of 213 articles published after the initial invention of 4D mmw technology in 2019 were retrieved from the Scopus database, and six kinds of metadata were extracted from them, including the title, abstract, keywords, author(s), publisher, and year. Since some papers lack keywords, the GPT-4 model was used to extract them from the titles and abstracts of these publications. The preprocessed metadata were then integrated using Python and fed into the Citespace 6.2.R3 for further statistical, clustering, and co-occurrence analyses. The result revealed that the primary applications of 4D mmw are autonomous driving, human activity recognition, and robotics. Subsequently, the potential applications of this technology in the construction industry are explored, including construction site monitoring, environment understanding, and worker health monitoring. Finally, the challenges of adopting this emerging technology in the construction industry are also discussed. Full article

(This article belongs to the Special Issue Advanced Technologies in Smart Construction and Artificial Intelligence)

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