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Advances in Engineering Structure Inspection, Monitoring Technologies, and Post-Disaster Diagnosis,...
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Advances in Engineering Structure Inspection, Monitoring Technologies, and Post-Disaster Diagnosis, Maintenance, and Operation

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Materials, and Repair & Renovation".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 2223

Special Issue Editors

Dr. Lingkun Chen

E-Mail Website
Guest Editor
College of Architectural Science and Engineering, Yangzhou University, Yangzhou 225000, China
Interests: accelerated construction and design; building information modeling (BIM) technology; prefabrication building; modular construction; emergency management
Special Issues, Collections and Topics in MDPI journals
Dr. Qing Zeng

E-Mail Website
Guest Editor
School of Intelligent Civil and Ocean Engineering, Harbin Institute of Technology, Shenzhen 518055, China
Interests: vehicle-bridge interaction (vbi); rotation influence line (ril); bridge damage identification; vehicle–bridge interaction; random traffic flow
Dr. Qikai Sun

E-Mail Website
Guest Editor
School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: bridge engineering; steel–concrete composite structure; vehicle–bridge coupling vibration; structural dynamics; structural health monitoring; earthquake engineering; structural reliability
Special Issues, Collections and Topics in MDPI journals
Dr. Tao Wang

E-Mail Website
Guest Editor
School of Geography and Planning, Huaiyin Normal University, Huai’an 223300, China
Interests: facilities management; prefabricated construction; construction project management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Presently, next-generation information technologies (artificial intelligence, big data, the Internet of Things, 3D printing) are propelling the structural engineering field towards profound digitalization and intelligent transformation. To accelerate the intelligent development of structural engineering, this Special Issue focuses on three cutting-edge directions: intelligent structural design, intelligent structural monitoring and inspection, and intelligent enhancement of structural disaster prevention and mitigation. It aims to provide an innovative engine for the high-quality development of the civil infrastructure industry and contribute to the construction of an intelligent building ecosystem.

This Special Issue aims to showcase the latest research findings and recent advancements in non-destructive evaluation (NDE) and structural health monitoring (SHM) technologies, alongside intelligent algorithms tailored for structural engineering applications. We cordially invite high-quality original research papers and review articles covering, but not limited to, the following themes:

Intelligent operation, maintenance, and diagnosis of structures

UAV–robot collaborative operations and AI-based damage prediction

Deep-learning-driven damage identification

Digital twins and predictive maintenance

Multi-source information fusion and system integration

Intelligent operation, maintenance, and diagnostics for railway engineering structures

Intelligent structural design and construction

Green buildings and sustainable development

Smart materials and intelligent structural systems

Multi-hazard effects and disaster resilience of structures

Structural seismic isolation, vibration reduction, and control

Digital intelligence empowerment of structural testing and structural digitalization enhancement

Physics-informed neural networks (PINN) for solving structural dynamic responses

Urban renewal

Smart cities

Building operation and maintenance

Living preservation of historic buildings

Others

Dr. Lingkun Chen
Dr. Qing Zeng
Dr. Qikai Sun
Dr. Tao Wang
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-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

  • structural damage identification
  • physical information data mining
  • computer-vision-based structural health monitoring
  • digital twin
  • multi-source information fusion
  • intelligent operation, maintenance and diagnostics
  • intelligent design and construction
  • disaster resilience
  • UAV-robotics
  • physics-informed neural networks (PINN)
  • operation and maintenance
  • urban renewal
  • green building

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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21 pages, 1582 KB  
Article
Tile Debonding Detection Based on Acoustic Signal Features and a Dual-Branch Convolutional Neural Network
by Dejiang Wang and Bo Kang
Buildings 2026, 16(4), 870; https://doi.org/10.3390/buildings16040870 - 21 Feb 2026
Viewed by 321
Abstract
Tiles are commonly used as architectural finishing materials, but are prone to debonding defects due to construction and environmental factors in engineering applications. Therefore, effective detection of tile debonding holds significant engineering relevance. This study proposes a tile debonding detection method based on [...] Read more.
Tiles are commonly used as architectural finishing materials, but are prone to debonding defects due to construction and environmental factors in engineering applications. Therefore, effective detection of tile debonding holds significant engineering relevance. This study proposes a tile debonding detection method based on impact sound signal features and a dual-branch convolutional neural network. The sound signals collected through tapping are transformed into two types of two-dimensional feature maps using Mel-frequency cepstral coefficients (MFCCs) and continuous wavelet transform (CWT), which are then fed in parallel into the dual-branch convolutional neural network for feature extraction and fusion. Finally, tile debonding classification is performed in the classifier module. Experimental results show that the proposed model achieves a classification accuracy of 98.5% under laboratory conditions. Moreover, it demonstrates strong robustness under varying noise levels and sound pressure conditions, maintaining an accuracy of 82% in a 75 dB human voice noise environment. Field validation in real-world engineering environments yields an accuracy of 91.5%. These findings indicate that the proposed method, which combines MFCC and CWT features with a dual-branch convolutional neural network architecture, enables high-precision identification of tile debonding defects. Full article
(This article belongs to the Special Issue Advances in Engineering Structure Inspection, Monitoring Technologies, and Post-Disaster Diagnosis, Maintenance, and Operation)
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39 pages, 10679 KB  
Article
Classifying the Reuse Value of Industrial Heritage Sites Using Random Forest: A Case Study of Jiangsu’s Salt Reclamation Zone
by Xiang Meng, Jiang Chang, Xiao Liu and Fei Zhuang
Buildings 2026, 16(4), 796; https://doi.org/10.3390/buildings16040796 - 14 Feb 2026
Viewed by 507
Abstract
Industrial heritage embodies the complex interplay between historical continuity, technological development, and social spatial transformation. However, existing assessment methods often rely on qualitative judgments or fragmented criteria, limiting their ability to systematically evaluate the reuse potential in the context of heterogeneous heritage. To [...] Read more.
Industrial heritage embodies the complex interplay between historical continuity, technological development, and social spatial transformation. However, existing assessment methods often rely on qualitative judgments or fragmented criteria, limiting their ability to systematically evaluate the reuse potential in the context of heterogeneous heritage. To overcome this limitation, this study constructs an empirical evaluation framework that defines heritage value through quantifiable indicators and examines how different value dimensions affect reuse potential. Based on a dataset of 124 industrial heritage sites located on saline–alkali soil along the coast of Jiangsu Province, this study integrates multiple data sources such as archival records, field surveys, spatial data, and questionnaire surveys to construct a multidimensional indicator system. This system quantifies and analyzes four value dimensions: historical, architectural, technological, and socio-cultural, and employs machine learning methods for analysis. The study utilizes a Random Forest model to examine the relative impact of each dimension and assess their comprehensive explanatory power in classifying the potential for heritage reuse. The performance of the model is evaluated through cross-validation, yielding robust results (accuracy = 0.833, macro F1 = 0.812). A five-fold cross-validation is conducted to train a Random Forest classifier. The model achieves an accuracy of 0.833, a macro F1 score of 0.812, and an AUC of 0.871, outperforming the baseline classifier and validating the reliability of the analytical framework. The research findings indicate that the impact of architectural integrity and technical characteristics on reuse potential significantly outweighs symbolic or perceptual attributes, unveiling structural biases present in traditional heritage assessment practices. This study transcends descriptive assessments by empirically examining the operational modes of different value dimensions within a unified analytical framework, offering empirical insights into the mechanisms influencing the reuse of industrial heritage. The proposed framework provides a reproducible and transparent approach to support heritage conservation and adaptive reuse strategies in industrial transformation areas. Full article
(This article belongs to the Special Issue Advances in Engineering Structure Inspection, Monitoring Technologies, and Post-Disaster Diagnosis, Maintenance, and Operation)
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Review

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34 pages, 4007 KB  
Review
Symbiotic Intelligence for Sustainable Cities: A Decadal Review of Generative AI, Ethical Algorithms, and Global South Innovations in Urban Green Space Research
by Tianrong Xu, Ainoriza Mohd Aini, Nikmatul Adha Nordin, Qi Shen, Liyan Huang and Wenbo Xu
Buildings 2026, 16(1), 231; https://doi.org/10.3390/buildings16010231 - 5 Jan 2026
Cited by 1 | Viewed by 946
Abstract
Urban Green Spaces (UGS) are integral components of the built environment, significantly contributing to its ecological, social, and performance dimensions, including microclimate regulation, occupant well-being, and energy efficiency. This decadal review (2015–2025) systematically analyzes 70 high-impact studies to propose a “Symbiotic Intelligence” framework. [...] Read more.
Urban Green Spaces (UGS) are integral components of the built environment, significantly contributing to its ecological, social, and performance dimensions, including microclimate regulation, occupant well-being, and energy efficiency. This decadal review (2015–2025) systematically analyzes 70 high-impact studies to propose a “Symbiotic Intelligence” framework. This framework integrates Generative AI, ethical algorithms, and innovations from the Global South to revolutionize the planning, design, and management of UGS within building landscapes and urban fabrics. Our analysis reveals that Generative AI can optimize participatory design processes and generate efficient planning schemes, increasing public satisfaction by 41% and achieving fivefold efficiency gains. Metaverse digital twins enable high-fidelity simulation of UGS performance with a mere 3.2% error rate, providing robust tools for building environment analysis. Ethical algorithms, employing fairness metrics and SHAP values, are pivotal for equitable resource distribution, having been shown to reduce UGS allocation disparities in low-income communities by 67%. Meanwhile, innovations from the Global South, such as lightweight federated learning and low-cost sensors, offer scalable solutions for building-environment monitoring under resource constraints, reducing model generalization error by 18% and decreasing data acquisition costs by 90%. However, persistent challenges-including data heterogeneity, algorithmic opacity (with only 23% of studies adopting interpretability tools), and significant data gaps in the Global South (coverage < 15%)-hinder equitable progress. Future research should prioritize developing UGS-climate-building coupling models, decentralized federated frameworks for building management systems, and blockchain-based participatory planning to establish a more robust foundation for sustainable built environments. This study provides an interdisciplinary roadmap for integrating intelligent UGS into building practices, contributing to the advancement of green buildings, occupant-centric design, and the overall sustainability and resilience of our built environment. Full article
(This article belongs to the Special Issue Advances in Engineering Structure Inspection, Monitoring Technologies, and Post-Disaster Diagnosis, Maintenance, and Operation)
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