Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.4
3.1
Journals
Buildings
Special Issues
AI in Construction: Automation, Optimization, and Safety
share announcement

AI in Construction: Automation, Optimization, and Safety

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4202

Special Issue Editors

Dr. Jinhuan Shan

E-Mail Website
Guest Editor
School of Data Science and Artificial Intelligence, Chang’an University, Middle Section of Nan’er Huan Road, Xi’an 710064, China
Interests: intelligent infrastructure; artificial intelligence in civil engineering; UAV-based intelligent road inspection; structural health monitoring; remote sensing and smart monitoring
Dr. Zheng Tong

E-Mail Website
Guest Editor
School of Transportation, Southeast University, Nanjing 211189, China
Interests: prompt-guided large language models; evidential deep neural networks; decision-making under uncertainty; AI-enabled ground-penetrating radar detection
Special Issues, Collections and Topics in MDPI journals
Dr. Difei Wu

E-Mail Website
Guest Editor
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Interests: digital transportation infrastructure; road detection and evaluation; intelligent construction
Special Issues, Collections and Topics in MDPI journals
Dr. Zhen Liu

E-Mail
Guest Editor
Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong 999077, China
Interests: NDT technologies; structural health monitoring; advanced sensors; remote sensing; deep learning; digital twin
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is revolutionizing the architecture, engineering, and construction (AEC) industry, driving unprecedented advancements in automation, optimization, and safety. By leveraging AI technologies, construction processes are becoming more efficient, sustainable, and safe, enhancing productivity, reducing costs, and minimizing risks across the entire project lifecycle.

This Special Issue invites original research papers and systematic reviews exploring innovative applications of AI in construction. We encourage submissions addressing, but not limited to, the following topics:

  • AI-driven automated inspection and defect detection.
  • Computer vision and deep learning for structural health monitoring.
  • Intelligent robotics and autonomous machinery for construction site monitoring.
  • AI-based optimization for resource allocation and scheduling.
  • Machine learning for predictive maintenance and lifecycle management.
  • AI in building information modeling (BIM) and digital twin technologies.
  • Enhancing construction safety and risk management using AI.
  • AI-driven decision support systems in construction project management.
  • Big data analytics and AI for sustainable and green construction.
  • Integration of IoT and AI for smart construction site management.

Dr. Jinhuan Shan
Dr. Zheng Tong
Dr. Difei Wu
Dr. Zhen Liu
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

  • construction automation
  • smart construction
  • construction safety
  • structural health monitoring
  • automated inspection
  • building information modeling
  • artificial intelligence

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 7692 KB  
Article
Deployable Deep Learning Models for Crack Detection: Efficiency, Interpretability, and Severity Estimation
by Amna Altaf, Adeel Mehmood, Massimo Leonardo Filograno, Soltan Alharbi and Jamshed Iqbal
Buildings 2025, 15(18), 3362; https://doi.org/10.3390/buildings15183362 - 17 Sep 2025
Viewed by 434
Abstract
Concrete infrastructure inspection is essential for maintaining the safety and longevity of urban environments. Traditional manual crack detection methods are labor-intensive, inconsistent, and difficult to scale. Recent advancements in deep learning and computer vision offer automated alternatives, particularly when deployed via unmanned aerial [...] Read more.
Concrete infrastructure inspection is essential for maintaining the safety and longevity of urban environments. Traditional manual crack detection methods are labor-intensive, inconsistent, and difficult to scale. Recent advancements in deep learning and computer vision offer automated alternatives, particularly when deployed via unmanned aerial vehicles (UAVs) for enhanced coverage and flexibility. However, achieving real-time performance on embedded systems requires models that are not only accurate but also lightweight and computationally efficient. This study presents CrackDetect-Lite, a comparative analysis of three deep learning architectures for binary crack detection using the SDNET2018 benchmark dataset: CNNSimple (a custom lightweight model), RSNet (a shallow residual network), and MobileVNet (a fine-tuned MobileNetV2). Class imbalance was addressed using a weighted cross-entropy loss function, and models were evaluated across multiple criteria including classification accuracy, crack-class F1-score, inference latency, and model size. Among the models, MobileVNet achieved the best balance between detection performance and deployability, with an accuracy of 90.5% and a crack F1-score of 0.73, while maintaining a low computational footprint suitable for UAV-based deployment. These findings demonstrate that carefully selected lightweight CNN architectures can deliver reliable, real-time crack detection, supporting scalable and autonomous infrastructure monitoring in smart city systems. Full article
(This article belongs to the Special Issue AI in Construction: Automation, Optimization, and Safety)
►▼ Show Figures

Figure 1

19 pages, 8903 KB  
Article
LSH-YOLO: A Lightweight Algorithm for Helmet-Wear Detection
by Zhao Liu, Fuwei Wang, Weimin Wang, Shenyi Cao, Xinhao Gao and Mingxin Chen
Buildings 2025, 15(16), 2918; https://doi.org/10.3390/buildings15162918 - 18 Aug 2025
Viewed by 446
Abstract
This work addresses the high computational cost and excessive parameter count associated with existing helmet-wearing detection models in complex construction scenarios. This paper proposes a lightweight helmet detection model, LSH-YOLO (Lightweight Safety Helmet) based on improvements to YOLOv8. First, the KernelWarehouse (KW) dynamic [...] Read more.
This work addresses the high computational cost and excessive parameter count associated with existing helmet-wearing detection models in complex construction scenarios. This paper proposes a lightweight helmet detection model, LSH-YOLO (Lightweight Safety Helmet) based on improvements to YOLOv8. First, the KernelWarehouse (KW) dynamic convolution is introduced to replace the standard convolution in the backbone and bottleneck structures. KW dynamically adjusts convolution kernels based on input features, thereby enhancing feature extraction and reducing redundant computation. Based on this, an improved C2f-KW module is proposed to further strengthen feature representation and lower computational complexity. Second, a lightweight detection head, SCDH (Shared Convolutional Detection Head), is designed to replace the original YOLOv8 Detect head. This modification maintains detection accuracy while further reducing both computational cost and parameter count. Finally, the Wise-IoU loss function is introduced to further enhance detection accuracy. Experimental results show that LSH-YOLO increases mAP50 by 0.6%, reaching 92.9%, while reducing computational cost by 63% and parameter count by 19%. Compared to YOLOv8n, LSH-YOLO demonstrates clear advantages in computational efficiency and detection performance, significantly lowering hardware resource requirements. These improvements make the model highly suitable for deployment in resource-constrained environments for real-time intelligent monitoring, thereby advancing the fields of industrial edge computing and intelligent safety surveillance. Full article
(This article belongs to the Special Issue AI in Construction: Automation, Optimization, and Safety)
►▼ Show Figures

Figure 1

25 pages, 10205 KB  
Article
RTLS-Enabled Bidirectional Alert System for Proximity Risk Mitigation in Tunnel Environments
by Fatima Afzal, Farhad Ullah Khan, Ayaz Ahmad Khan, Ruchini Jayasinghe and Numan Khan
Buildings 2025, 15(15), 2667; https://doi.org/10.3390/buildings15152667 - 28 Jul 2025
Viewed by 587
Abstract
Tunnel construction poses significant safety challenges due to confined spaces, limited visibility, and the dynamic movement of labourers and machinery. This study addresses a critical gap in real-time, bidirectional proximity monitoring by developing and validating a prototype early-warning system that integrates real-time location [...] Read more.
Tunnel construction poses significant safety challenges due to confined spaces, limited visibility, and the dynamic movement of labourers and machinery. This study addresses a critical gap in real-time, bidirectional proximity monitoring by developing and validating a prototype early-warning system that integrates real-time location systems (RTLS) with long-range (LoRa) wireless communication and ultra-wideband (UWB) positioning. The system comprises Arduino nano microcontrollers, organic light-emitting diode (OLED) displays, and piezo buzzers to detect and signal proximity breaches between workers and equipment. Using an action research approach, three pilot case studies were conducted in a simulated tunnel environment to test the system’s effectiveness in both static and dynamic risk scenarios. The results showed that the system accurately tracked proximity and generated timely alerts when safety thresholds were crossed, although minor delays of 5–8 s and slight positional inaccuracies were noted. These findings confirm the system’s capacity to enhance situational awareness and reduce reliance on manual safety protocols. The study contributes to the tunnel safety literature by demonstrating the feasibility of low-cost, real-time monitoring solutions that simultaneously track labour and machinery. The proposed RTLS framework offers practical value for safety managers and informs future research into automated safety systems in complex construction environments. Full article
(This article belongs to the Special Issue AI in Construction: Automation, Optimization, and Safety)
►▼ Show Figures

Figure 1

26 pages, 5344 KB  
Article
Real-Time Progress Monitoring of Bricklaying
by Ramez Magdy, Khaled A. Hamdy and Yasmeen A. S. Essawy
Buildings 2025, 15(14), 2456; https://doi.org/10.3390/buildings15142456 - 13 Jul 2025
Viewed by 762
Abstract
The construction industry is one of the largest contributors to the world economy. However, the level of automation and digitalization in the construction industry is still at its infancy in comparison with other industries due to the complex nature and the large size [...] Read more.
The construction industry is one of the largest contributors to the world economy. However, the level of automation and digitalization in the construction industry is still at its infancy in comparison with other industries due to the complex nature and the large size of construction projects. Meanwhile, construction projects are prone to cost overruns and schedule delays due to the adoption of traditional progress monitoring techniques to retrieve progress on-site, having indoor activities participating with an accountable ratio of these works. Improvements in deep learning and Computer Vision (CV) algorithms provide promising results in detecting objects in real time. Also, researchers have investigated the probability of using CV as a tool to create a Digital Twin (DT) for construction sites. This paper proposes a model utilizing the state-of-the-art YOLOv8 algorithm to monitor the progress of bricklaying activities, automatically extracting and analyzing real-time data from construction sites. The detected data is then integrated into a 3D Building Information Model (BIM), which serves as a DT, allowing project managers to visualize, track, and compare the actual progress of bricklaying with the planned schedule. By incorporating this technology, the model aims to enhance accuracy in progress monitoring, reduce human error, and enable real-time updates to project timelines, contributing to more efficient project management and timely completion. Full article
(This article belongs to the Special Issue AI in Construction: Automation, Optimization, and Safety)
►▼ Show Figures

Figure 1

22 pages, 2474 KB  
Article
A Rapid Sand Gradation Detection Method Based on Dual-Camera Fusion
by Shihao Zhang, Yang Zhang, Song Sun, Xinghai Yuan, Haoxuan Sun, Heng Wang, Yi Yuan, Dan Luo and Chuanyun Xu
Buildings 2025, 15(14), 2404; https://doi.org/10.3390/buildings15142404 - 9 Jul 2025
Viewed by 365
Abstract
Precise grading of manufactured sand is vital to concrete performance, yet standard sieve tests, though accurate, are too slow for online quality control. Thus, we devised an image-based inspection method combining a dual-camera module with a Temporal Interval Sampling Strategy (TISS) to enhance [...] Read more.
Precise grading of manufactured sand is vital to concrete performance, yet standard sieve tests, though accurate, are too slow for online quality control. Thus, we devised an image-based inspection method combining a dual-camera module with a Temporal Interval Sampling Strategy (TISS) to enhance throughput while maintaining precision. In this design, a global wide-angle camera captures the entire particle field, whereas a local high-magnification camera focuses on fine fractions. TISS selects only statistically representative frames, effectively eliminating redundant data. A lightweight segmentation algorithm based on geometric rules cleanly separates overlapping particles and assigns size classes using a normal-distribution classifier. In tests on ten 500 g batches of manufactured sand spanning fine, medium, and coarse gradations, the system processed each batch in an average of 7.8 min using only 34 image groups. It kept the total gradation error within 12% and the fineness-modulus deviation within ±0.06 compared to reference sieving. These results demonstrate that the combination of complementary optics and targeted sampling can provide a scalable, real-time solution. Full article
(This article belongs to the Special Issue AI in Construction: Automation, Optimization, and Safety)
►▼ Show Figures

Figure 1

19 pages, 4353 KB  
Article
The Lightweight Method of Ground Penetrating Radar (GPR) Hidden Defect Detection Based on SESM-YOLO
by Yu Yan, Guangxuan Jiao, Minxing Cui and Lei Ni
Buildings 2025, 15(13), 2345; https://doi.org/10.3390/buildings15132345 - 3 Jul 2025
Viewed by 931
Abstract
Ground Penetrating Radar (GPR) is a high-resolution nondestructive technique for detecting subsurface defects, yet its image interpretation suffers from strong subjectivity, low efficiency, and high false-alarm rates. To establish a customized underground GPR defect detection algorithm, this paper introduces SESM-YOLO which is an [...] Read more.
Ground Penetrating Radar (GPR) is a high-resolution nondestructive technique for detecting subsurface defects, yet its image interpretation suffers from strong subjectivity, low efficiency, and high false-alarm rates. To establish a customized underground GPR defect detection algorithm, this paper introduces SESM-YOLO which is an enhancement of YOLOv8n tailored for GPR images: (1) A Slim_Efficient_Block module replaces the bottleneck in the backbone, enhancing feature extraction while maintaining lightweight properties through a conditional gating mechanism. (2) A feature fusion network named Efficient_MS_FPN is designed, which significantly enhances the feature representation capability and performance. Additionally, the SCSA attention mechanism is introduced before the detection head, enabling precise extraction of defect object features. (3) As a novel loss function, MPDIoU is proposed to reduce the disparity between the corners of the predicted bounding boxes and those of the ground truth boxes. Experimental results on a custom dataset show that SESM-YOLO achieves an average precision of 92.8% in detecting hidden road defects, which is 6.2% higher than the YOLOv8n baseline. The model also shows improvements in precision (92.4%) and recall (86.7%), with reductions in parameters and computational load, demonstrating significant advantages over current mainstream detection models. Full article
(This article belongs to the Special Issue AI in Construction: Automation, Optimization, and Safety)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop