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Sensor-Based Structural Health Monitoring of Civil Infrastructure
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Sensor-Based Structural Health Monitoring of Civil Infrastructure

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

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 1487

Special Issue Editor

Prof. Dr. Bin Xu

E-Mail Website
Guest Editor
College of Civil Engineering, Huaqiao University, Xiamen 361021, China
Interests: multiscale simulation on RC structures; multiphysics simulation; FEM/XFEM/SEM; soft computation and artificial intelligence; NDT and SHM for engineering structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Civil infrastructure, including bridges, buildings, tunnels, power plants, and dams, plays a crucial role in our lives. It is essential to properly maintain and monitor these structures to accurately assess their age, usability, and identify potential concerns. The improper functioning and negligent actions of humans have led to significant economic losses and loss of human lives. In recent years, the research community has shown a growing interest in developing effective methods for structural health monitoring (SHM). A typical SHM system consists of a network of sensors that measure various parameters related to the structure's condition and its surrounding environment, such as temperature, stress, delamination, strain, vibration, and humidity. To ensure reliable in situ structural health monitoring, it is crucial to have accurate, durable, responsive, and long-lasting sensors. Although numerous sensor types have been developed and demonstrated, there is an increasing need for innovative, high-performance in situ sensors.

We extend an invitation to researchers to submit original research articles and review articles that will contribute to the advancement of sensor technologies for structural health monitoring (SHM). Potential topics encompass, but are not restricted to, the following:

  • Advanced sensing technologies for structural damage detection;
  • Artificial intelligence, signal processing and sensor fusion for civil infrastructure monitoring;
  • Sensor materials, processing, and fabrication;
  • Wired and wireless sensor networks;
  • Vibration-based system identification and modal analysis;
  • Deep learning for structural condition assessment;
  • Other innovative technologies for sensor application in structural health monitoring.

Prof. Dr. Bin Xu
Guest Editor

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

  • sensors
  • structural health monitoring
  • civil infrastructure
  • sensor materials
  • sensor networks

Published Papers (2 papers)

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Research

21 pages, 12544 KiB  
Article
Sensor-Based Structural Health Monitoring of Asphalt Pavements with Semi-Rigid Bases Combining Accelerated Pavement Testing and a Falling Weight Deflectometer Test
by Zhen Liu, Bingyan Cui, Qifeng Yang and Xingyu Gu
Sensors 2024, 24(3), 994; https://doi.org/10.3390/s24030994 - 03 Feb 2024
Viewed by 639
Abstract
The Structural Health Monitoring (SHM) of pavement infrastructures holds paramount significance in the assessment and prognostication of the remaining service life of roadways. In response to this imperative, a methodology for surveilling the surface and internal mechanical responses of pavements was devised through [...] Read more.
The Structural Health Monitoring (SHM) of pavement infrastructures holds paramount significance in the assessment and prognostication of the remaining service life of roadways. In response to this imperative, a methodology for surveilling the surface and internal mechanical responses of pavements was devised through the amalgamation of Accelerated Pavement Testing (APT) and Falling Weight Deflectometer (FWD) examinations. An experimental road segment, characterized by a conventional asphalt pavement structure with semi-rigid bases, was meticulously established in Jiangsu, China. Considering nine distinct influencing factors, including loading speed, loading weight, and temperature, innovative buried and layout configurations for Resistive Sensors and Fiber-optic Bragg Grating (FBG) sensors were devised. These configurations facilitated the comprehensive assessment of stress and strain within the road structure across diverse APT conditions. The methodology encompassed the formulation of response baselines, the conversion of electrical signals to stress and strain signals, and the proposition of a signal processing approach involving partial filtering and noise reduction. In experimental findings, the asphalt bottom layer was observed to undergo alternate tensile strains under dynamic loads (the peak strain was ten με). Simultaneously, the horizontal transverse sensor exhibited compressive strains peaking at 66.5 με. The horizontal longitudinal strain within the base and subbase ranged between 3 and 5 με, with the base registering a higher strain value than the subbase. When subjected to FWD, the sensor indicated a diminishing peak pulse signal, with the most pronounced peak response occurring when the load plate was situated atop the sensor. In summary, a comprehensive suite of monitoring schemes for road structures has been formulated, delineating guidelines for the deployment of road sensors and facilitating sustained performance observation over extended durations. Full article
(This article belongs to the Special Issue Sensor-Based Structural Health Monitoring of Civil Infrastructure)
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19 pages, 21978 KiB  
Communication
Three-Dimensional Reconstruction and Deformation Identification of Slope Models Based on Structured Light Method
by Zhijian Chen, Changxing Zhang, Zhiyi Tang, Kun Fang and Wei Xu
Sensors 2024, 24(3), 794; https://doi.org/10.3390/s24030794 - 25 Jan 2024
Viewed by 616
Abstract
In this study, we propose a meticulous method for the three-dimensional modeling of slope models using structured light, a swift and cost-effective technique. Our approach aims to enhance the understanding of slope behavior during landslides by capturing and analyzing surface deformations. The methodology [...] Read more.
In this study, we propose a meticulous method for the three-dimensional modeling of slope models using structured light, a swift and cost-effective technique. Our approach aims to enhance the understanding of slope behavior during landslides by capturing and analyzing surface deformations. The methodology involves the initial capture of images at various stages of landslides, followed by the application of the structured light method for precise three-dimensional reconstructions at each stage. The system’s low-cost nature and operational convenience make it accessible for widespread use. Subsequently, a comparative analysis is conducted to identify regions susceptible to severe landslide disasters, providing valuable insights for risk assessment. Our findings underscore the efficacy of this system in facilitating a qualitative analysis of landslide-prone areas, offering a swift and cost-efficient solution for the three-dimensional reconstruction of slope models. Full article
(This article belongs to the Special Issue Sensor-Based Structural Health Monitoring of Civil Infrastructure)
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