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Big Data and Machine/Deep Learning in Construction
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Big Data and Machine/Deep Learning in Construction

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 June 2025 | Viewed by 8695

Special Issue Editor

Dr. Fan Lei

E-Mail Website
Guest Editor
Department of Civil Engineering, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
Interests: point cloud and remotely sensed imagery; machine and deep learning; semantic segmentations; computer vision; monitoring of structures and geohazards

Special Issue Information

Dear Colleagues,

The use of big data and machine/deep learning in the field of construction has rapidly gained momentum in recent years, offering unprecedented opportunities to improve efficiency, safety and sustainability. This Special Issue aims to bring together cutting-edge research and innovative applications that harness the power of big data and machine/deep learning in the construction domain.

We invite researchers, practitioners and industry experts to submit high-quality original research and review articles that address, but are not limited to, the following topics:

  • Detection of objects, hazards and defects at construction sites;
  • Semantic segmentation of construction scenes;
  • Building information modeling (BIM);
  • Sustainable construction practices;
  • Energy efficiency in construction;
  • Data-driven decision making in construction;
  • Monitoring and quality control of construction processes;
  • Predictive construction-related maintenance;
  • Smart construction equipment;
  • Construction project management and resource optimization;
  • Construction risk management;
  • AI-enabled wearable technology in construction.

Submissions should present original research, case studies or comprehensive reviews that contribute to the theoretical and practical understanding of leveraging big data and machine/deep learning to advance the construction industry. We welcome interdisciplinary contributions that combine expertise from construction engineering, computer science, data analytics and related fields.

Dr. Fan Lei
Guest Editor

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

  • detection of objects, hazards and defects at construction sites
  • semantic segmentation of construction scenes
  • building information modeling (BIM)
  • sustainable construction practices
  • energy efficiency in construction
  • data-driven decision making in construction
  • monitoring and quality control of construction processes
  • predictive construction-related maintenance
  • construction project management and resource optimization
  • construction risk management
  • AI-enabled wearable technology in construction

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (6 papers)

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Research

16 pages, 2177 KiB  
Article
Addressing Data Scarcity in Crack Detection via CrackModel: A Novel Dataset Synthesis Approach
by Jian Ma, Yuan Meng, Weidong Yan, Guoqi Liu and Xueyan Guo
Buildings 2025, 15(7), 1053; https://doi.org/10.3390/buildings15071053 - 25 Mar 2025
Viewed by 323
Abstract
The application of deep learning in crack detection has become a research hotspot in Structural Health Monitoring (SHM). However, the potential of detection models is often limited due to the lack of large-scale training data, and this issue is particularly prominent in the [...] Read more.
The application of deep learning in crack detection has become a research hotspot in Structural Health Monitoring (SHM). However, the potential of detection models is often limited due to the lack of large-scale training data, and this issue is particularly prominent in the crack detection of ancient wooden buildings in China. To address this challenge, “CrackModel”, an innovative dataset construction model, is proposed in this paper. This model is capable of extracting and storing crack information from hundreds of images of wooden structures with cracks and synthesizing the data with images of intact structures to generate high-fidelity data for training detection algorithms. To evaluate the effectiveness of synthetic data, systematic experiments were conducted using YOLO-based detection models on both synthetic images and real data. The results demonstrate that synthetic images can effectively simulate real data, providing potential data support for subsequent crack detection tasks. Additionally, these findings validate the efficacy of CrackModel in generating synthetic data. CrackModel, supported by limited baseline data, is capable of constructing crack datasets across various scenarios and simulating future damage, showcasing its broad application potential in the field of structural engineering. Full article
(This article belongs to the Special Issue Big Data and Machine/Deep Learning in Construction)
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17 pages, 1092 KiB  
Article
Achieving On-Site Trustworthy AI Implementation in the Construction Industry: A Framework Across the AI Lifecycle
by Lichao Yang, Gavin Allen, Zichao Zhang and Yifan Zhao
Buildings 2025, 15(1), 21; https://doi.org/10.3390/buildings15010021 - 25 Dec 2024
Cited by 1 | Viewed by 2090
Abstract
In recent years, the application of artificial intelligence (AI) technology in the construction industry has rapidly emerged, particularly in areas such as site monitoring and project management. This technology has demonstrated its great potential in enhancing safety and productivity in construction. However, concerns [...] Read more.
In recent years, the application of artificial intelligence (AI) technology in the construction industry has rapidly emerged, particularly in areas such as site monitoring and project management. This technology has demonstrated its great potential in enhancing safety and productivity in construction. However, concerns regarding the technical maturity and reliability, safety, and privacy implications have led to a lack of trust in AI among stakeholders and end users in the construction industry, which slows the intelligent transformation of the industry, particularly for on-site AI implementation. This paper reviews frameworks for AI system design across various sectors and government regulations and requirements for achieving trustworthy and responsible AI. The principles for the AI system design are then determined. Furthermore, a lifecycle design framework specifically tailored for AI systems deployed in the construction industry is proposed. This framework addresses six key phases, including planning, data collection, algorithm development, deployment, maintenance, and archiving, and clarifies the design principles and development priorities needed for each phase to enhance AI system trustworthiness and acceptance. This framework provides design guidance for the implementation of AI in the construction industry, particularly for on-site applications, aiming to facilitate the intelligent transformation of the construction industry. Full article
(This article belongs to the Special Issue Big Data and Machine/Deep Learning in Construction)
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23 pages, 12585 KiB  
Article
Evaluating YOLO Models for Efficient Crack Detection in Concrete Structures Using Transfer Learning
by Muhammad Sohaib, Muzamal Arif and Jong-Myon Kim
Buildings 2024, 14(12), 3928; https://doi.org/10.3390/buildings14123928 - 9 Dec 2024
Cited by 4 | Viewed by 2057
Abstract
The You Only Look Once (YOLO) network is considered highly suitable for real-time object detection tasks due to its characteristics, such as high speed, single-shot detection, global context awareness, scalability, and adaptability to real-world conditions. This work introduces a comprehensive analysis of various [...] Read more.
The You Only Look Once (YOLO) network is considered highly suitable for real-time object detection tasks due to its characteristics, such as high speed, single-shot detection, global context awareness, scalability, and adaptability to real-world conditions. This work introduces a comprehensive analysis of various YOLO models for detecting cracks in concrete structures, aiming to assist in the selection of an optimal model for future detection and segmentation tasks. The YOLO models are initially trained on a dataset containing both images with and without cracks, producing a generalized model capable of extracting abstract features beneficial for crack detection. Subsequently, transfer learning is employed using a dataset that reflects real-world conditions, such as occlusions, varying crack sizes, and rotations, to further refine the model. Crack detection in concrete remains challenging due to the wide variation in crack sizes, aspect ratios, and complex backgrounds. To achieve optimal performance, we test different versions of YOLO, a state-of-the-art single-shot detector, and aim to balance inference speed and mean average precision (mAP). Our results indicate that YOLOv10 demonstrates superior performance, achieving a mean average precision (mAP) of 74.52% with an inference time of 19.5 milliseconds per image, making it the most effective among the models tested. Full article
(This article belongs to the Special Issue Big Data and Machine/Deep Learning in Construction)
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38 pages, 14572 KiB  
Article
Study on Compression Bearing Capacity of Tapered Concrete-Filled Double-Skin Steel Tubular Members Based on Heuristic-Algorithm-Optimized Backpropagation Neural Network Model
by Xianghong Liu, Sital Kumar Dangi, Zixuan Yang, Yinxuan Song, Qing Sun and Jiantao Wang
Buildings 2024, 14(11), 3375; https://doi.org/10.3390/buildings14113375 - 24 Oct 2024
Cited by 2 | Viewed by 873
Abstract
A tapered concrete-filled double-skin steel tubular (TCFDST) structure has been used as the main framework in transmission towers, offshore facility platforms, and turbine towers owing to its excellent mechanical properties. In order to solve the difficulties of calculating the axial compressive capacity of [...] Read more.
A tapered concrete-filled double-skin steel tubular (TCFDST) structure has been used as the main framework in transmission towers, offshore facility platforms, and turbine towers owing to its excellent mechanical properties. In order to solve the difficulties of calculating the axial compressive capacity of TCFDST members due to the variations in cross-section, this paper applied heuristic optimization algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO) to enhance a Backpropagation Neural Network (BPNN) model. A predictive model incorporating both global and local optimization strategies for the axial compressive capacity of a TCFDST structure is proposed. A comprehensive axial database for TCFDST members, comprising 1327 sets of experimental and finite element analysis results, was established, with ten types of component dimensions and material parameters selected as input variables and compressive bearing capacity as the output variable. This study developed and assessed four BPNN models, each optimized by a different heuristic algorithm, against various machine learning algorithms and standards. The heuristic-algorithm-optimized BPNN models demonstrated superior accuracy in predicting the axial compressive capacity of TCFDST members. Through parametric analysis, this study identified the relationship between the model’s bearing capacity predictions and each input parameter, confirming the model’s broad applicability. The optimized BPNN model, refined with heuristic algorithms, provides a significant reference for addressing the computational challenges associated with the load-bearing capacity of TCFDST structures and facilitating their application. Full article
(This article belongs to the Special Issue Big Data and Machine/Deep Learning in Construction)
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20 pages, 13214 KiB  
Article
Algorithm-Driven Extraction of Point Cloud Data Representing Bottom Flanges of Beams in a Complex Steel Frame Structure for Deformation Measurement
by Yang Zhao, Dufei Wang, Qinfeng Zhu, Lei Fan and Yuanfeng Bao
Buildings 2024, 14(9), 2847; https://doi.org/10.3390/buildings14092847 - 10 Sep 2024
Cited by 1 | Viewed by 1390
Abstract
Laser scanning has become a popular technology for monitoring structural deformation due to its ability to rapidly obtain 3D point clouds that provide detailed information about structures. In this study, the deformation of a complex steel frame structure is estimated by comparing the [...] Read more.
Laser scanning has become a popular technology for monitoring structural deformation due to its ability to rapidly obtain 3D point clouds that provide detailed information about structures. In this study, the deformation of a complex steel frame structure is estimated by comparing the associated point clouds captured at two epochs. To measure its deformations, it is essential to extract the bottom flanges of the steel beams in the captured point clouds. However, manual extraction of numerous bottom flanges is laborious and the separation of beam bottom flanges and webs is especially challenging. This study presents an algorithm-driven approach for extracting all beams’ bottom flanges of a complex steel frame. RANdom SAmple Consensus (RANSAC), Euclidean clustering, and an originally defined point feature is sequentially used to extract the beam bottom flanges. The beam bottom flanges extracted by the proposed method are used to estimate the deformation of the steel frame structure before and after the removal of temporary supports to beams. Compared to manual extraction, the proposed method achieved an accuracy of 0.89 in extracting the beam bottom flanges while saving hours of time. The maximum observed deformation of the steel beams is 100 mm at a location where the temporal support was unloaded. The proposed method significantly improves the efficiency of the deformation measurement of steel frame structures using laser scanning. Full article
(This article belongs to the Special Issue Big Data and Machine/Deep Learning in Construction)
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19 pages, 8335 KiB  
Article
MSFA-Net: A Multiscale Feature Aggregation Network for Semantic Segmentation of Historical Building Point Clouds
by Ruiju Zhang, Yaqian Xue, Jian Wang, Daixue Song, Jianghong Zhao and Lei Pang
Buildings 2024, 14(5), 1285; https://doi.org/10.3390/buildings14051285 - 1 May 2024
Viewed by 1177
Abstract
In recent years, research on the preservation of historical architecture has gained significant attention, where the effectiveness of semantic segmentation is particularly crucial for subsequent repair, protection, and 3D reconstruction. Given the sparse and uneven nature of large-scale historical building point cloud scenes, [...] Read more.
In recent years, research on the preservation of historical architecture has gained significant attention, where the effectiveness of semantic segmentation is particularly crucial for subsequent repair, protection, and 3D reconstruction. Given the sparse and uneven nature of large-scale historical building point cloud scenes, most semantic segmentation methods opt to sample representative subsets of points, often leading to the loss of key features and insufficient segmentation accuracy of architectural components. Moreover, the geometric feature information at the junctions of components is cluttered and dense, resulting in poor edge segmentation. Based on this, this paper proposes a unique semantic segmentation network design called MSFA-Net. To obtain multiscale features and suppress irrelevant information, a double attention aggregation module is first introduced. Then, to enhance the model’s robustness and generalization capabilities, a contextual feature enhancement and edge interactive classifier module are proposed to train edge features and fuse the context data. Finally, to evaluate the performance of the proposed model, experiments were conducted on a self-curated ancient building dataset and the S3DIS dataset, achieving OA values of 95.2% and 88.7%, as well as mIoU values of 86.2% and 71.6%, respectively, further confirming the effectiveness and superiority of the proposed method. Full article
(This article belongs to the Special Issue Big Data and Machine/Deep Learning in Construction)
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