Toward Green and Intelligent Transportation Infrastructure: Road Non-destructive Testing and Structural Health Monitoring Technologies
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".
Deadline for manuscript submissions: 20 November 2024 | Viewed by 8687
Special Issue Editors
Interests: NDT technologies; structural health monitoring; advanced sensors; remote sensing; green materials
Special Issues, Collections and Topics in MDPI journals
Interests: non-destructive testing; ground-penetrating radar; deep learning; remote sensing technologies; digital twin
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The detection and evaluation of transportation infrastructure are critical to system safety and asset management. However, traditional destructive testing and manual evaluation methods are expensive, ineffective, and time-consuming. Recently, transportation infrastructures have been extensively tested using non-destructive (NDT) and structural health monitoring (SHM) technologies based on green and intelligent needs. These technologies include remote sensing, unmanned aerial vehicles (UAVs), ground-penetrating radar (GPR), falling weight deflectometers (FWD), seismic wave, fibre Bragg grating (FBG) sensors, etc. They have greatly improved the efficiency of detection and evaluation of transportation infrastructure, and at the same time, large quantities of detection data are in urgent need of automated processing.
Recent technological breakthroughs in artificial intelligence, machine learning, BIM, etc., have provided new ideas for the processing of big data for transportation infrastructure detection. Therefore, the aim of this Special Issue is to collect recent research advances and progress in NDT and SHM in the fields of transportation infrastructure. Additionally, papers focusing on maintenance management systems, life-cycle assessment and asset assessment of transportation infrastructures are also welcome. Topics of interest include, but are not limited to:
- Innovative NDT of transportation infrastructures;
- Recent developments in existing NDT technologies, including GPR, FWD, etc.;
- Remote sensing techniques for damage identification;
- The modeling of infrastructure performance based on NDT or multi-source data;
- Green and intelligent sensors and networks for road structural monitoring;
- Damage evaluation using artificial intelligence and deep learning;
- Pavement dynamic monitoring using accelerated loading testing;
- Unmanned aerial vehicles for road extraction and 3D modeling;
- Life cycle assessments on transportation infrastructures based on NDT tests.
Prof. Dr. Xingyu Gu
Dr. Zhen Liu
Guest Editors
Manuscript Submission Information
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Keywords
- transportation infrastructures
- road structural monitoring
- road damage evaluation
- pavement monitoring
- remote sensing
- UAV
- intelligent sensors
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Identification of Ice Front in Freezing Soil Using Optimized Circular Capacitance Sensor
Authors: Xing Hu , Qiao Dong ,*, Bin Shi , and Kang Yao
Affiliation: Department of Road Engineering, Southeast University, Nanjing 210000, China;
Abstract: As the interface between frozen and unfrozen soil, the ice front is not only a spatial location concept, but also a potentially dangerous interface where the mechanical properties of soil could change abruptly. Accurately identifying its spatial position is essential for the safe and efficient execution of large-scale frozen soil engineering projects. Electrical capacitance tomography (ECT) is a promising method for the visualization of frozen soil due to its non-invasive nature, low cast and rapid re-sponse. This paper presents the design of a mobile circular capacitance sensor (MCCS), which was optimized based on its capacitance dynamic range and sensitivity uniformity. The MCCS was used to measure frozen soil samples along the depth direction to obtain three-dimensional images of ice front. Finally, the experimental results were compared with the simulation results from COMSOL Multiphysics to analyze the deviations. It is found that the fuzzy optimization design based on multi-criteria orthogonal experiments makes the MCCS meet various performance requirements. The sum of capacitance values and the average permittivity distribution were proposed to reflect variations in the medium along the depth direction. Simulation results indicate that the MCCS has a low error margin in identifying the position of the ice front.
Keywords: ice front; frozen soil; electrical capacitance tomography; three-dimensional recon-struction