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Remote Sensing
Special Issues
Land Deformation and Engineering Structural Health Monitoring Using Geo-Spatial Technologies
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Land Deformation and Engineering Structural Health Monitoring Using Geo-Spatial Technologies

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing and Geo-Spatial Science".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 424

Special Issue Editors

Dr. Teng-To Yu

E-Mail Website
Guest Editor
Department of Resources Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 70101, Taiwan
Interests: geodesy; geophysics; GIS and digital simulation; remote sensing; seismology
Special Issues, Collections and Topics in MDPI journals
Dr. Wen Liu

E-Mail Website
Guest Editor
Department of Urban Environmental System, Chiba University, Chiba, Japan
Interests: urban disaster prevention; remote sensing; geospatial information systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land deformation could be result from a geo-hazard event or can serve as an early warning sign for an upcoming catastrophic landslide or subsidence. It is a location-based phenomenon that possesses temporal variation as well. The deformed land causes damage to engineering structures, and the examination of their health condition is also a challenging task after any major hazard event. GIS, free satellite images, and radar data, as well as drone deployment, make the spatial technology not just easy to access but also popular. Deformation patterns or trends could be established by machine learning, and thus the failing engineering structure can be precisely located in a very short time after the event. Any studies on methods or technology that are related to this topic are highly welcome to be submitted to this Special Issue, and case reports are also welcome.

Dr. Teng-To Yu
Dr. Wen Liu
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. Remote Sensing 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 2700 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

  • GNSS
  • remote sensing
  • GIS
  • spatial analysis
  • machine learning
  • SAR/In-SAR/GBSAR

Published Papers (1 paper)

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Research

23 pages, 13384 KiB  
Article
Deformation Analysis and Prediction of a High-Speed Railway Suspension Bridge under Multi-Load Coupling
by Simin Liu, Weiping Jiang, Qusen Chen, Jian Wang, Xuyan Tan, Ruiqi Liu and Zhongtao Ye
Remote Sens. 2024, 16(10), 1687; https://doi.org/10.3390/rs16101687 - 9 May 2024
Viewed by 258
Abstract
High-speed railway suspension bridges (HSRSBs) have been constructed with the new advancements in technology. The deformation prediction for HSRSBs is essential to their safety and maintenance. The conventional prediction methods are developed for bridges without high-speed railway. Different factors, including temperature (TEMP), time [...] Read more.
High-speed railway suspension bridges (HSRSBs) have been constructed with the new advancements in technology. The deformation prediction for HSRSBs is essential to their safety and maintenance. The conventional prediction methods are developed for bridges without high-speed railway. Different factors, including temperature (TEMP), time delay compensation (TDC), train live load (TLL), are considered in these methods. However, the train side (TS) and train instantaneous position (TIP) have a significant impact on deformation for HSRSBs, and they are not used in the prediction. More importantly, the coupling issue among different factors is so significant that it cannot be neglected. In this study, we propose a deformation prediction model based on a backpropagation (BP) neural network. This model uses different factors as model input, including TEMP, TDC, TLL, TS, and TIP. The coupling issue is addressed by using the new model. The new model was evaluated using a dataset of 10-day field measurements. It achieves a mean absolute error (MAE) of 8.81 mm, a mean relative error (MRE) of 9.82%, and coefficient of determination (R2) of 0.94. The new model will provide high-precision prediction for deformation and will be used in the development of an early warning system. Full article
(This article belongs to the Special Issue Land Deformation and Engineering Structural Health Monitoring Using Geo-Spatial Technologies)
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