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Applied Sciences
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Civil Structural Health Monitoring: Techniques, Systems and Applications
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Civil Structural Health Monitoring: Techniques, Systems and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 2515

Special Issue Editors

Prof. Dr. Fei Kang

E-Mail Website
Guest Editor
School of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China
Interests: dam safety monitoring; support vector machines; Gaussian processes; evolutionary computation; inverse analysis; structural health monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yonglong Li

E-Mail Website
Guest Editor
Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610000, China
Interests: dam inspection robot; AI for civil engineering; automation; control system

Special Issue Information

Dear Colleagues,

Civil structures and infrastructures, such as bridges, high-rise buildings, towers, dams, tunnels, and so forth, occupy a major position in the economy and play a vital role in facilitating daily life for the world population. However, accidental structural damage can cause significant economic and human casualties. Structures subjected to dead load, live load, natural hazards, and anthropogenic activities are referred to as the censorious elements in the construction systems. Thus, structural health monitoring is necessary for the longevity of the building and the safety of the public. Structural health monitoring is the process of implementing a damage detection and characterization strategy for engineering structures. Structural health monitoring pertains to the life of a structure and diagnoses the ‘‘state” of the constituted materials in different parts of the structure. This Special Issue aims to discuss the latest research advances in the field of civil structural health monitoring (such as the detection of damage and data processing algorithms, modelling and simulation, sensor development and experimentation, materials research, etc.) in the form of reviews and case studies. The future of this field is very bright, and will help to drive futuristic and intelligent infrastructure. 

Prof. Dr. Fei Kang
Prof. Dr. Yonglong Li
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. Applied Sciences 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 2400 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

  • structural health monitoring
  • damage identification
  • numerical simulation
  • sensor experiments
  • underwater defect detection
  • machine learning
  • dams
  • case studies

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Published Papers (2 papers)

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Research

31 pages, 50761 KiB  
Article
Intelligent Structural Health Monitoring and Noncontact Measurement Method of Small Reservoir Dams Using UAV Photogrammetry and Anomaly Detection
by Sizeng Zhao, Fei Kang, Lina He, Junjie Li, Yiqing Si and Yiping Xu
Appl. Sci. 2024, 14(20), 9156; https://doi.org/10.3390/app14209156 - 10 Oct 2024
Viewed by 656
Abstract
This study proposes a UAV-based remote measurement method for accurately locating pedestrians and other small targets within small reservoir dams. To address the imprecise coordinate information in reservoir areas after prolonged operations, a transformation method for converting UAV coordinates into the local coordinate [...] Read more.
This study proposes a UAV-based remote measurement method for accurately locating pedestrians and other small targets within small reservoir dams. To address the imprecise coordinate information in reservoir areas after prolonged operations, a transformation method for converting UAV coordinates into the local coordinate system without relying on preset parameters is introduced, accomplished by integrating the Structure from Motion (SfM) algorithm to calculate the transformation parameters. An improved YOLOv8 network is introduced for the high-precision detection of small pedestrian targets, complemented by a laser rangefinder to facilitate accurate 3D locating of targets from varying postures and positions. Furthermore, the integration of a thermal infrared camera facilitates the detection and localization of potential seepage. The experimental validation and application across two real small reservoir dams confirm the accuracy and applicability of the proposed approach, demonstrating the efficiency of the proposed routine UAV surveillance strategy and proving its potential to establish electronic fences and enhance maintenance operations. Full article
(This article belongs to the Special Issue Civil Structural Health Monitoring: Techniques, Systems and Applications)
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16 pages, 6273 KiB  
Article
Load Effect Analysis Method of Cable-Stayed Bridge for Long-Span Track Based on Adaptive Filtering Method
by Peng Ding, Xiaogang Li, Sheng Chen, Xiangsheng Huang, Xiaohu Chen and Yong Qi
Appl. Sci. 2024, 14(16), 7057; https://doi.org/10.3390/app14167057 - 12 Aug 2024
Viewed by 937
Abstract
Aiming at the problems of large capacity, narrow transverse width, large excitation, high safety level, and difficulty in accurately grasping the working state of the cable-stayed bridge for the long-span track, this research obtains the structural response data in real time by establishing [...] Read more.
Aiming at the problems of large capacity, narrow transverse width, large excitation, high safety level, and difficulty in accurately grasping the working state of the cable-stayed bridge for the long-span track, this research obtains the structural response data in real time by establishing a health monitoring system. The adaptive filtering method was employed to separate the train load response and the temperature load response. Then, a train load effect analysis method based on the influence line and a temperature load effect analysis method based on the correlation were proposed to assess the operational status of the bridge in real time and objectively. The Chongqing Nanjimen Railway Track Bridge (hereinafter Chongqing Nanjimen track bridge) project was utilized as a case study to demonstrate the application of these methods. The results show that the adaptive filtering method can effectively separate the response of train and temperature loads. The normalized cross-correlation (NCC) results of the measured train load response and the influence line’s finite element calculation show a high degree of fit between the measured values and the theory, proving that no significant anomalies are found in the bridge. There is a strong correlation between the ambient temperature difference and the Pearson correlation coefficient of structural response, which indicates that the Chongqing Nanjimen track bridge is currently in normal working condition. Full article
(This article belongs to the Special Issue Civil Structural Health Monitoring: Techniques, Systems and Applications)
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