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Applied Sciences
Special Issues
Seismic Structural Health Monitoring
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Seismic Structural Health Monitoring

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 7781

Special Issue Editors

Prof. Dr. Wen-I Liao

E-Mail Website
Guest Editor
National Taipei University of Technology, Taipei, Taiwan
Interests: structural health monitoring and damage detection; seismic evaluation and retrofit of RC structures; smart materials and structures
Prof. Dr. Chien-Kuo Chiu

E-Mail Website
Guest Editor
National Taiwan University of Science and Technology, Taipei, Taiwan
Interests: deterioration analysis of RC buildings; deterioration risk management; durability design and maintenance of RC buildings

Special Issue Information

Dear Colleagues,

An earthquake can damage structures, whose safety or serviceability must then be evaluated in a limited period. The results of such evaluations are very important, as they can be used to determine what should be done to maintain or rehabilitate the damaged structure. Various methods for detecting damage to structures have been developed. The most common practical method involves determining residual crack widths or areas of concrete spalling by visual inspection. However, since visual inspection cannot be effectively carried out within a limited period after an earthquake and it involves substantial uncertainty, the quantification of damage to a structure using smart sensor technologies and/or intelligent traditional sensors has become a very important topic in recent years.

The last few decades have seen the rapid development of sensors, including smart sensors, which are based on smart materials, such as piezoceramics, fiber Bragg gratings (FBGs), and electrostrictive materials. Traditional sensors, such as strain gages, thermal couples, pressure transducers, and displacement sensors, are being made intelligent by integrating a microprocessor, communication module, and energy-harvesting system. Recently, various nanotechnologies have enabled the development of an increasing number of smart sensors. These smart sensors and smart devices are often combined with various structures to form so-called smart structures, which can sense environmental or structural changes during an earthquake. With developments in microprocessor technology, wireless communications, and sensor networks, smart sensors and smart structures are finding a wider range of applications in structural health monitoring, damage detection, and localization of damaged places in earthquake engineering and other disciplines. These advances are essential towards building an infrastructure that is able to automatically handle the threats that are posed by earthquake phenomena to the integrity of structures and increase the safety of occupants. This Special Issue aims to archive some of the latest developments in seismic damage detection and structural health monitoring in the hope of inspiring potential readers and researchers in the field and of bringing us closer to a world in which intelligent structures are commonplace.

Prof. Dr. Wen-I Liao
Prof. Dr. Chien-Kuo Chiu
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. 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 detection and localization
  • Earthquake engineering
  • Smart sensors
  • Smart actuators
  • Smart transducer
  • Smart structures
  • Piezoceramic materials
  • Fiber optic sensors
  • Ultrasonic transducers
  • Sensor networks
  • Traditional sensors

Published Papers (3 papers)

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Research

17 pages, 5581 KiB  
Article
Image Analysis Applications for Building Inter-Story Drift Monitoring
by Yuan-Sen Yang, Qiang Xue, Pin-Yao Chen, Jian-Huang Weng, Chi-Hang Li, Chien-Chun Liu, Jing-Syu Chen and Chao-Tsun Chen
Appl. Sci. 2020, 10(20), 7304; https://doi.org/10.3390/app10207304 - 20 Oct 2020
Cited by 3 | Viewed by 2174
Abstract
Structural health monitoring techniques have been applied to several important structures and infrastructure facilities, such as buildings, bridges, and power plants. For buildings, accelerometers are commonly used for monitoring the accelerations induced by ambient vibration to analyze the structural natural frequencies for further [...] Read more.
Structural health monitoring techniques have been applied to several important structures and infrastructure facilities, such as buildings, bridges, and power plants. For buildings, accelerometers are commonly used for monitoring the accelerations induced by ambient vibration to analyze the structural natural frequencies for further system identification and damage detection. However, due to the relatively high cost of the accelerometers and data acquisition systems, accelerometer-based structural health monitoring systems are challenging to deploy in general buildings. This study proposed an image analysis-based building deformation monitoring method that integrates a small single-board computer, computer vision techniques, and a single-camera multiple degree-of-freedom algorithm. In contrast to other vision-based systems that use multiple expensive cameras, this method is designed for a single camera configuration to simplify the installation and maintenance procedures for practical applications. It is designed to monitor the inter-story drifts and torsional responses between the ceiling and floor of a story that is being monitored in a building, aiming to maximize the monitored structural responses. A series of 1:10 reduced scale static and dynamic structural experiments demonstrated that the proposed method and the device prototype are capable of analyzing images and structural responses with an accuracy of 0.07 and 0.3 mm from the results of the static and dynamic experiments, respectively. As digital imaging technology has been developing dramatically, the accuracy and the sampling rates of this method can be improved accordingly with the development of the required hardware, making this method practically feasible for an increasing number of applications for building structural monitoring. Full article
(This article belongs to the Special Issue Seismic Structural Health Monitoring)
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19 pages, 4803 KiB  
Article
Machine-Learning Based Optimal Seismic Control of Structure with Active Mass Damper
by Pei-Ching Chen and Kai-Yi Chien
Appl. Sci. 2020, 10(15), 5342; https://doi.org/10.3390/app10155342 - 03 Aug 2020
Cited by 11 | Viewed by 3160
Abstract
In recent years, optimal control which minimizes a cost function formulated by weighted states and control inputs has been applied to the seismic control of structures. Optimal control requires structural states which may not be available in real application; therefore, state estimation is [...] Read more.
In recent years, optimal control which minimizes a cost function formulated by weighted states and control inputs has been applied to the seismic control of structures. Optimal control requires structural states which may not be available in real application; therefore, state estimation is essential, which inevitably takes additional computation time. However, time delay and state estimate error could affect the control performance. In this study, a multilayer perceptron (MLP) model and an autoregressive with exogenous inputs (ARX) model in machine learning are applied to learn the control force generated from a linear-quadratic regulator (LQR) with weighting matrices optimized by applying symbiotic organisms search algorithm. A 10-story building is adopted as a benchmark model for training and validation of the MLP and ARX models. Numerical simulation results demonstrate that the MLP and ARX models are able to emulate the LQR control force from the acceleration response directly, indicating that state estimation is not essential for optimal control implementation in real application. Finally, the machine-learning based approach is experimentally validated by conducting shake table testing in the laboratory in which the structural model is controlled by an active mass damper. The experimental results and structural control performance of the MLP and ARX models are compared with those of the LQR with a Kalman filter. Full article
(This article belongs to the Special Issue Seismic Structural Health Monitoring)
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19 pages, 10077 KiB  
Article
Application of Post-Embedded Piezoceramic Sensors for Force Detection on RC Columns under Seismic Loading
by Chien-Kuo Chiu, Chia-Hsin Wu, Hsin-Fang Sung, Wen-I Liao and Chih-Hsien Lin
Appl. Sci. 2020, 10(15), 5061; https://doi.org/10.3390/app10155061 - 23 Jul 2020
Cited by 2 | Viewed by 1513
Abstract
To quantify damage to reinforced concrete (RC) column members after an earthquake, an engineer needs to know the maximum applied force that was generated by the earthquake. Therefore, in this work, piezoceramic transducers were used to detect the applied force on an RC [...] Read more.
To quantify damage to reinforced concrete (RC) column members after an earthquake, an engineer needs to know the maximum applied force that was generated by the earthquake. Therefore, in this work, piezoceramic transducers were used to detect the applied force on an RC column member under dynamic loading. To investigate the use of post-embedded piezoceramic sensors in detecting the force that is applied to RC columns, eight full-size RC column specimens with various failure modes were tested under specific earthquake loadings. Post-embedded piezoceramic sensors were installed at a range of depths (70–80 mm) beneath the surface of a column specimen to examine the relationship between the signals that were obtained from them and the force applied by the dynamic actuator. The signals that were generated by the post-embedded piezoceramic sensors, which correlate with the applied force, are presented. These results indicate that the post-embedded piezoceramic sensors have great potential as tools for measuring the maximum applied force on an RC column in an earthquake. In other words, signals that are obtained from post-embedded piezoceramic sensors on an RC column in an earthquake can be used to determine the applied force and corresponding damage or residual seismic capacity. Full article
(This article belongs to the Special Issue Seismic Structural Health Monitoring)
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