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Infrastructures
Special Issues
Advances in Damage Detection for Concrete Structures
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Advances in Damage Detection for Concrete Structures

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: 10 December 2025 | Viewed by 1026

Special Issue Editors

Dr. Seyed Sasan Khedmatgozar Dolati

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA
Interests: non-destructive testing (NDT); non-destructive evaluation (NDE); steel; bridges; inspection; damage detection; vibration method; vision-based method; robotics; base isolation; laminated rubber bearing pads; viscous damper; nonlinear time history analysis; seismic design; earthquake; ground motions; restrainer; opensees; residual displacement
Prof. Dr. Darius Bačinskas

E-Mail Website
Guest Editor
Department of Reinforced Concrete Structures and Geotechnical Engineering, Vilnius Gediminas Technical University (VILNIUS TECH), Vilnius, Lithuania
Interests: constitutive modeling and serviceability analysis of reinforced concrete and composite structures; nonlinear analysis of bridges subjected to different types of external actions

Special Issue Information

Dear Colleagues,

Concrete is utilized extensively in construction, serving as a cornerstone of resilient and durable infrastructure. Despite its strength, concrete is vulnerable to damage caused by aging, environmental factors, overloading, fatigue, and extreme natural hazards such as earthquakes, floods, and hurricanes. Accurate and timely damage detection is therefore critical for ensuring the safety, reliability, and extended service life of concrete structures.

The field of damage detection has transformed in recent years. Traditional methods such as visual inspection and destructive testing have been complemented by modern approaches, including non-destructive testing (NDT), structural health monitoring (SHM), and finite element analysis (FEA). These tools enable the early identification of damage, the assessment of deterioration, and the prediction of structural performance under various conditions.

Emerging technologies such as acoustic emission analysis, infrared thermography, ultrasonic methods, and digital image processing have further revolutionized the field of damage detection. Additionally, the integration of artificial intelligence, machine learning, and Internet of Things (IoT) into structural monitoring systems has enhanced the accuracy of real-time assessment.

This Special Issue, entitled "Advances in Damage Detection for Concrete Structures", seeks to showcase innovative research and practical applications in damage detection, providing insights into the challenges and opportunities associated with safeguarding concrete infrastructure.

Dr. Seyed Sasan Khedmatgozar Dolati
Prof. Dr. Darius Bačinskas
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. Infrastructures is an international peer-reviewed open access monthly 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 1800 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

  • damage detection
  • concrete structures
  • non-destructive testing (NDT)
  • structural health monitoring (SHM)
  • artificial intelligence in structural monitoring
  • crack detection
  • digital image processing
  • predictive maintenance
  • machine learning applications
  • real-time monitoring
  • self-sensing concrete

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

Published Papers (1 paper)

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Research

23 pages, 3585 KB  
Article
Deep Learning for Underwater Crack Detection: Integrating Physical Models and Uncertainty-Aware Semantic Segmentation
by Wenji Ai, Zongchao Liu, Shuai Teng, Shaodi Wang and Yinghou He
Infrastructures 2025, 10(10), 255; https://doi.org/10.3390/infrastructures10100255 - 23 Sep 2025
Viewed by 446
Abstract
Underwater crack detection is critical for ensuring the safety and longevity of submerged infrastructures, yet it remains challenging due to water-induced image degradation, limited labeled data, and the poor generalization of existing models. This paper proposes a novel deep learning framework that integrates [...] Read more.
Underwater crack detection is critical for ensuring the safety and longevity of submerged infrastructures, yet it remains challenging due to water-induced image degradation, limited labeled data, and the poor generalization of existing models. This paper proposes a novel deep learning framework that integrates physical priors and uncertainty modeling to address these challenges. Our approach introduces a physics-guided enhancement module that leverages underwater light propagation models, and a dual-branch segmentation network that combines semantic and geometry-aware curvature features to precisely delineate irregular crack boundaries. Additionally, an uncertainty-aware Transformer module quantifies prediction confidence, reducing the number of overconfident errors in ambiguous regions. Experiments on a self-collected dataset demonstrate State-of-the-Art performance, achieving 81.2% mIoU and 83.9% Dice scores, with superior robustness in turbid water and uneven lighting. The proposed method introduces a novel synergy of physical priors and uncertainty-aware learning, advancing underwater infrastructure inspection beyond the current data-driven approaches. Our framework offers significant improvements in accuracy, robustness, and interpretability, particularly in challenging conditions like turbid water and non-uniform lighting. Full article
(This article belongs to the Special Issue Advances in Damage Detection for Concrete Structures)
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