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Materials
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Rapid Inspection, Evaluation, and Repair Materials on Transportation Infrastructures
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Rapid Inspection, Evaluation, and Repair Materials on Transportation Infrastructures

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Construction and Building Materials".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 1389

Special Issue Editors

Dr. Changjun Zhou

E-Mail Website1 Website2
Guest Editor
School of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China
Interests: cement concrete pavement repair; ultra-thin friction course; pavement durability
Special Issues, Collections and Topics in MDPI journals
Dr. Hui Qin

E-Mail Website
Guest Editor
Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China
Interests: tunnel inspection and monitoring; non-destructive testing; tunnel maintenance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Transportation infrastructures are bases for logistics, information flow, and human travelling, which stimulate the economy and maintain a harmonic society. Damage to pavements, bridges, or in tunnels may cause delays, while maintenance activities may block traffic. Rapid inspection, evaluation, and especially repair materials are solutions for maintenance activities, especially for areas with heavy volumes of traffic.

In recent decades, innovative design methods, materials, and practices on rapid inspection, evaluation, and repairs for transportation infrastructures have been blooming, which are worth presenting for the interest of academics and engineers. Therefore, we dedicate this Special Issue to this topic and invite you to submit your most recent research and findings. Our intention for this Special Issue is to tackle research and practice activities in three scenarios, including pavements, bridges, and tunnels. We believe that this platform will promote the advancement of knowledge of rapid inspection, evaluation, and repair materials for transportation infrastructures, which will make them more sustainable and make the traffic on them flow more smoothly.

You may choose our Joint Special Issue in Buildings.

Dr. Changjun Zhou
Dr. Hui Qin
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. Materials 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

  • transportation infrastructures
  • rapid inspection
  • rapid evaluation
  • rapid repair materials
  • durability
  • sustainability

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Published Papers (1 paper)

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Research

20 pages, 12095 KiB  
Article
A Deep Learning-Based Watershed Feature Fusion Approach for Tunnel Crack Segmentation in Complex Backgrounds
by Haozheng Wang, Qiang Wang, Weikang Zhang, Junli Zhai, Dongyang Yuan, Junhao Tong, Xiongyao Xie, Biao Zhou and Hao Tian
Materials 2025, 18(1), 142; https://doi.org/10.3390/ma18010142 - 1 Jan 2025
Cited by 1 | Viewed by 937
Abstract
As highway tunnel operations continue over time, structural defects, particularly cracks, have been observed to increase annually. Coupled with the rapid expansion of tunnel networks, traditional manual inspection methods have proven inadequate to meet current demands. In recent years, machine vision and deep [...] Read more.
As highway tunnel operations continue over time, structural defects, particularly cracks, have been observed to increase annually. Coupled with the rapid expansion of tunnel networks, traditional manual inspection methods have proven inadequate to meet current demands. In recent years, machine vision and deep learning technologies have gained significant attention in civil engineering for the detection and analysis of structural defects. However, rapid and accurate defect identification in highway tunnels presents challenges due to complex background conditions, numerous interfering factors, and the relatively low proportion of cracks within the structure. Additionally, the intensive labor requirements and limited efficiency in labeling training datasets for deep learning pose significant constraints on the deployment of intelligent crack segmentation algorithms. To address these limitations, this study proposes an automatic labeling and optimization algorithm for crack sample sets, utilizing crack features and the watershed algorithm to enable efficient automated segmentation with minimal human input. Furthermore, the deep learning-based crack segmentation network was optimized through comparative analysis of various network depths and residual structure configurations to achieve the best possible model performance. Enhanced accuracy was attained by incorporating axis extraction and watershed filling algorithms to refine segmentation outcomes. Under diverse lining surface conditions and multiple interference factors, the proposed approach achieved a crack segmentation accuracy of 98.78%, with an Intersection over Union (IoU) of 72.41%, providing a robust solution for crack segmentation in tunnels with complex backgrounds. Full article
(This article belongs to the Special Issue Rapid Inspection, Evaluation, and Repair Materials on Transportation Infrastructures)
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