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Smart Sensors for Transportation Infrastructure Health Monitoring
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Smart Sensors for Transportation Infrastructure Health Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 25 April 2025 | Viewed by 6482

Special Issue Editors

Dr. Ya Wei

E-Mail Website
Guest Editor
Department of Civil Engineering, Tsinghua University, Beijing 100084, China
Interests: ultra-high-performance cement-based materials and intelligent materials; multi-scale characterizing and simulating mechanical properties of cement-based materials; developing smart MEMS acceleration sensor system for transportation infrastructure monitoring; pavement health monitoring and low-cost; rapid repair technology
Special Issues, Collections and Topics in MDPI journals
Dr. Zhoujing Ye

E-Mail Website
Guest Editor
National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China
Interests: IoT-enabled service safety assessment for transportation infrastructure; data analytics for transportation infrastructure monitoring; resilience and safety analysis of transportation infrastructure under extreme disasters; maintenance and safety management for transportation infrastructure

Special Issue Information

Dear Colleagues,

The serviceability, safety, and resilience of transportation infrastructure, such as roads, bridges, railways, and tunnels, have been the long-lasting interests of many researchers. Emerging sensing technologies provide chances and measures for the health monitoring of the in-service transportation infrastructure. This Special Issue aims to attract research papers on the development and deployment of smart sensors tailored for infrastructure health monitoring, as well as analyzing them based on the big data collected from smart sensors to resolve the status of the serviceability and safety of the in-service transportation infrastructure. This Special Issue will present the current newest sensing technologies occurring in transportation infrastructure. The scope of this Special Issue includes but is not limited to:

  • Development of smart sensor;
  • Testing of smart sensor;
  • Packaging of smart sensor;
  • The IOT system of smart sensing;
  • Deploying smart sensor in roads, bridges, tunnels, railways;
  • Big-data analysis based on smart sensing;
  • Serviceability and safety evaluation based on the smart sensing.

Dr. Ya Wei
Dr. Zhoujing Ye
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. Sensors 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

  • pavement
  • moving vehicle loads
  • loss of slab support
  • vibration sensing
  • sensor packaging
  • structural integrity
  • hazard detection
  • risk assessment

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Published Papers (3 papers)

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Research

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19 pages, 15925 KiB  
Article
YOLO-RD: A Road Damage Detection Method for Effective Pavement Maintenance
by Wei Wang, Xiaoru Yu, Bin Jing, Ziqi Tang, Wei Zhang, Shengyu Wang, Yao Xiao, Shu Li and Liping Yang
Sensors 2025, 25(5), 1442; https://doi.org/10.3390/s25051442 - 27 Feb 2025
Viewed by 704
Abstract
Road damage detection is crucial for ensuring road safety and minimizing maintenance costs. However, detecting small damage, managing complex backgrounds, and identifying irregular damage shapes remain significant challenges. To address these issues, we propose YOLO-RD, an advanced detection framework that integrates innovative modules [...] Read more.
Road damage detection is crucial for ensuring road safety and minimizing maintenance costs. However, detecting small damage, managing complex backgrounds, and identifying irregular damage shapes remain significant challenges. To address these issues, we propose YOLO-RD, an advanced detection framework that integrates innovative modules for feature enhancement, multi-scale robustness, and detail preservation. Specifically, the Star Operation Module (SOM) improves sensitivity to small-scale damage, the Multi-dimensional Auxiliary Fusion (MAF) module strengthens robustness in complex environments, and the Wavelet Transform Convolution (WTC) enables adaptive focus on irregular shapes. On the Japanese road dataset in RDD2022, YOLO-RD achieves a detection accuracy of 25.75%, with a notable 4.93% improvement in small object detection over the baseline YOLOv8. These results demonstrate the effectiveness and practicality of YOLO-RD in addressing diverse and challenging real-world scenarios, establishing it as a robust solution for automated road condition monitoring. Full article
(This article belongs to the Special Issue Smart Sensors for Transportation Infrastructure Health Monitoring)
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24 pages, 9364 KiB  
Article
Optimising Concrete Crack Detection: A Study of Transfer Learning with Application on Nvidia Jetson Nano
by C. Long Nguyen, Andy Nguyen, Jason Brown, Terry Byrne, Binh Thanh Ngo and Chieu Xuan Luong
Sensors 2024, 24(23), 7818; https://doi.org/10.3390/s24237818 - 6 Dec 2024
Cited by 1 | Viewed by 1359
Abstract
The use of Artificial Intelligence (AI) to detect defects such as concrete cracks in civil and transport infrastructure has the potential to make inspections less expensive, quicker, safer and more objective by reducing the need for on-site human labour. One deployment scenario involves [...] Read more.
The use of Artificial Intelligence (AI) to detect defects such as concrete cracks in civil and transport infrastructure has the potential to make inspections less expensive, quicker, safer and more objective by reducing the need for on-site human labour. One deployment scenario involves using a drone to carry an embedded device and camera, with the device making localised predictions at the edge about the existence of defects using a trained convolutional neural network (CNN) for image classification. In this paper, we trained six CNNs, namely Resnet18, Resnet50, GoogLeNet, MobileNetV2, MobileNetV3-Small and MobileNetV3-Large, using transfer learning technology to classify images of concrete structures as containing a crack or not. To enhance the model’s robustness, the original dataset, comprising 3000 images of concrete structures, was augmented using salt and pepper noise, as well as motion blur, separately. The results show that Resnet50 generally provides the highest validation accuracy (96% with the original dataset and a batch size of 16) and the highest validation F1-score (95% with the original dataset and a batch size of 16). The trained model was then deployed on an Nvidia Jetson Nano device for real-time inference, demonstrating its capability to accurately detect cracks in both laboratory and field settings. This study highlights the potential of using transfer learning on Edge AI devices for Structural Health Monitoring, providing a cost-effective and efficient solution for automated crack detection in concrete structures. Full article
(This article belongs to the Special Issue Smart Sensors for Transportation Infrastructure Health Monitoring)
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Other

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18 pages, 56141 KiB  
Perspective
A Vision and Proof of Concept for New Approach to Monitoring for Safer Future Smart Transportation Systems
by Kent X. Eng, Yang Xie, Mauricio Pereira, Zygmunt J. Haas, Samir R. Das, Petar M. Djurić, Branko Glisic and Milutin Stanaćević
Sensors 2024, 24(18), 6018; https://doi.org/10.3390/s24186018 - 18 Sep 2024
Viewed by 1213
Abstract
Transportation infrastructure experiences distress due to aging, overuse, and climate changes. To reduce maintenance costs and labor, researchers have developed various structural health monitoring systems. However, the existing systems are designed for short-term monitoring and do not quantify structural parameters. A long-term monitoring [...] Read more.
Transportation infrastructure experiences distress due to aging, overuse, and climate changes. To reduce maintenance costs and labor, researchers have developed various structural health monitoring systems. However, the existing systems are designed for short-term monitoring and do not quantify structural parameters. A long-term monitoring system that quantifies structural parameters is needed to improve the quality of monitoring. In this work, a novel Transportation Rf-bAsed Monitoring (TRAM) system is proposed. TRAM is a multi-parameter monitoring system that relies on embeddable backscatter-based, batteryless, and radio-frequency sensors. The system can monitor structural parameters with 3D spatial and temporal information. Laboratory experiments were conducted on a 1D scale to evaluate and examine the sensitivity and reliability of the monitored structural parameters, which are displacement and water content. In contrast to other existing methods, TRAM correlates phase change to the change in concerned parameters, enabling long-term monitoring. Full article
(This article belongs to the Special Issue Smart Sensors for Transportation Infrastructure Health Monitoring)
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