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Buildings
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AI-Enhanced Defect Detection and Quality Assurance in Building Structures
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AI-Enhanced Defect Detection and Quality Assurance in Building Structures

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: 30 December 2026 | Viewed by 1083

Editors

Dr. Hesheng Tang

E-Mail Website
Guest Editor
1. Department of Disaster Mitigation for Structures, Tongji University, Shanghai 200092, China
2. State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China
Interests: structural vibration control and health monitoring; machine learning and data mining; uncertain computation of forward and inversion problems; damage mechanics of structures and their materials; artificial intelligence and optimal design
Special Issues, Collections and Topics in MDPI journals
Dr. Yajuan Xie

E-Mail Website
Guest Editor
School of Economics and Management, Anhui Jianzhu University, 856 Jinzhai Road, Hefei, China
Interests: structural health monitoring; deep learning; intelligent building operations
Dr. Yangyang Liao

E-Mail Website
Guest Editor
College of Civil Engineering, Anhui Jianzhu University, 292 Ziyun Road, Hefei, China
Interests: AI for fluid–structure interaction; vortex-induced vibration; uncertain computation of forward and inversion problems

Special Issue Information

Dear Colleagues,

Defect detection and quality assurance in building structures are critical components for ensuring safety, durability, and long-term performance. Traditional methods for defect detection and quality assurance typically rely on manual inspections, which are often time-consuming, prone to human error, and struggle to identify subtle or hidden defects. With the rapid advancement of Artificial Intelligence (AI) technologies, the field of structural defect detection and quality assurance has entered an entirely new era.

This Special Issue explores innovative AI applications in enhancing defect detection and quality assurance in building structures. Topics of interest include, but are not limited to, AI-based image recognition techniques for identifying cracks, corrosion, and other structural anomalies; the utilization of machine learning algorithms for predictive maintenance; and AI-driven quality assurance systems capable of continuously monitoring structural integrity. The integration of AI with Non-Destructive Testing (NDT) methods—such as ultrasonic testing, infrared scanning, and others—also constitutes a key focus of this Special Issue.

We invite original research articles, reviews, and case studies that demonstrate the latest advancements in AI applications for defect detection, structural health monitoring, and quality assurance. This Special Issue aims to showcase how AI can enhance the accuracy, efficiency, and cost-effectiveness of quality control processes in building construction and maintenance, ultimately contributing to the safety and sustainability of built environments.

Dr. Hesheng Tang
Dr. Yajuan Xie
Dr. Yangyang Liao
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • AI-driven defect detection
  • structural health monitoring (SHM)
  • predictive maintenance
  • quality assurance
  • non-destructive testing (NDT)
  • performance evaluation and prediction
  • damage modeling and simulation
  • intelligent construction
  • vibration control
  • forward and inverse problems

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

Published Papers (2 papers)

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Research

24 pages, 12524 KB  
Article
Semi-Supervised Domain Adaptation Networks for Self-Adaptive Identification of Grouting Sleeve Internal Defect
by Yajuan Xie, Yangyang Liao, Xianzhi Li, Yijun Xie and Hesheng Tang
Buildings 2026, 16(11), 2223; https://doi.org/10.3390/buildings16112223 - 1 Jun 2026
Viewed by 328
Abstract
The intelligent identification of grouting defects in grouted sleeves in prefabricated structures is critical for maintaining structural integrity. However, current deep learning-based identification methods face limitations, including insufficient model adaptability and the difficulty of obtaining labeled data. Models trained on one domain struggle [...] Read more.
The intelligent identification of grouting defects in grouted sleeves in prefabricated structures is critical for maintaining structural integrity. However, current deep learning-based identification methods face limitations, including insufficient model adaptability and the difficulty of obtaining labeled data. Models trained on one domain struggle to generalize to others due to differences in data distributions, making these methods challenging to apply in real-world scenarios. To address this engineering challenge, this paper investigates the applicability of maximum mean discrepancy-based domain adaptation (MMD-based DA) and domain adversarial training (DAT) approaches for cross-domain grouting defect identification. Acceleration signals collected by accelerometers near the grouted sleeves are used as the model input. The model’s ability to generalize across domains is evaluated by training on labeled data from one working condition and testing its performance on other working conditions using only unlabeled data. And these methods are compared with traditional Convolutional Neural Networks (CNNs). Experiments were conducted on a two-layer prefabricated frame structure. The experimental results demonstrated the effectiveness of the MMD-based DA method in improving the accuracy and robustness of defect identification across different domains, with the use of unlabeled data. Full article
(This article belongs to the Special Issue AI-Enhanced Defect Detection and Quality Assurance in Building Structures)
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40 pages, 15849 KB  
Article
Incorporating Structural Prior Knowledge into YOLO for Robust Infrastructure Damage Detection
by Zichen Zhang and Chengjun Guo
Buildings 2026, 16(11), 2105; https://doi.org/10.3390/buildings16112105 - 25 May 2026
Viewed by 329
Abstract
Vision-based structural defect detection methods based on YOLOv11 have achieved promising performance in recent years; however, their robustness in real engineering environments remains limited due to illumination variation, shadow occlusion, surface contamination, and complex background textures. Existing data-driven approaches primarily rely on visual [...] Read more.
Vision-based structural defect detection methods based on YOLOv11 have achieved promising performance in recent years; however, their robustness in real engineering environments remains limited due to illumination variation, shadow occlusion, surface contamination, and complex background textures. Existing data-driven approaches primarily rely on visual appearance features while neglecting the intrinsic geometric continuity and morphological characteristics associated with structural failures such as cracks and spalling. To address these challenges, this study proposes an enhanced defect detection framework termed GCA-YOLO for intelligent structural inspection. The proposed method integrates a Geometric Constraint Attention (GCA) module and a Residual Efficient Channel Attention (RECA) module to improve feature representation. Instead of explicit physical simulation, the GCA module embeds morphology-guided geometric priors into the attention mechanism using differentiable gradient and Laplacian operators. This enforces structural continuity perception and suppresses geometrically inconsistent responses caused by background noise. Furthermore, a geometry confidence gating mechanism adaptively modulates the contribution of morphological features, while the RECA module recalibrates channel-wise responses to enhance the representation of weak and low-contrast defects. To comprehensively evaluate the proposed method, experiments were conducted on three representative datasets, including a public crack dataset and two self-built datasets (one for peeling/detachment and one for crack defects). These datasets were collected from diverse civil infrastructure scenarios such as bridges, tunnels, and pavements under challenging conditions including low illumination, shadow occlusion, complex textures, and heterogeneous backgrounds. Compared with the baseline YOLOv11 model, the proposed GCA-YOLO framework improves mAP@0.5 by 2.2%, 2.5%, and 15.9% on the public crack dataset, the self-built peeling/detaching dataset, and the self-built crack dataset, respectively. Meanwhile, Recall is improved by 4.6%, 3.8%, and 33.1%, respectively, demonstrating the effectiveness of the proposed dual-attention framework in enhancing the completeness of defect localization and reducing missed detections. Despite these performance gains, the proposed framework maintains a lightweight architecture and does not introduce significant computational overhead. Experimental results demonstrate that the proposed framework achieves strong robustness, stable generalization capability, and favorable detection efficiency across different defect categories and engineering scenarios, demonstrating promising potential for intelligent infrastructure inspection, urban safety monitoring, and practical engineering deployment. Full article
(This article belongs to the Special Issue AI-Enhanced Defect Detection and Quality Assurance in Building Structures)
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