Advances in Damage Detection for Concrete Structures
A special issue of Infrastructures (ISSN 2412-3811).
Deadline for manuscript submissions: 10 December 2025 | Viewed by 298
Special Issue Editors
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
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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