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Sensors in Structural Health Monitoring and Seismic Protection

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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 55821

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Dr. Eric M. Lui

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Department of Civil & Environmental Engineering, Syracuse University, Syracuse, NY 13244-1240, USA
Interests: steel structures; structural stability; structural dynamics; earthquake engineering; numerical modeling; damage identification and quantification; computer-aided analysis and design of structures; composite structures
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Special Issue Information

Dear Colleagues,

The use of sensors in civil infrastructure is becoming increasingly important and is growing at a fast pace. Being able to monitor the built environment on a continuous basis and in a reliable manner has allowed designers and engineers to conceive and construct safer as well as more robust and resilient structures. Over the years, the development in new sensing technology and the use of innovative approaches to assess the state of the built environment both before and after a natural or man-made disaster have created a new paradigm for performing structural analysis, design, retrofit, and repair.

Using real time data collected autonomously by a new generation of sensors (e.g., laser-based optical sensors, fiber optic sensors, and discrete diode position sensors) and processed through special algorithms, data analytics, and/or deep learning techniques, engineers are able to gain a better understanding of how structures behave under the applied loads, identify potential problems, detect faults, quantify the damage, recommend more effective rehabilitation methods, and devise more cost-effective maintenance schedule.

Structural health monitoring and seismic protection are multidisciplinary fields where experts from different disciplines, such as electrical and electronic engineering, civil and structural engineering, mechanical engineering, materials engineering, computer engineering, and computer and information science, can collaborate and work toward advancing the state-of-the-art.

Potential topics for this Special Issue of Sensors include but are not limited to:

Structural sensing and monitoring
Smart sensing technology
Wireless sensor networks
Machine and deep learning
Seismic monitoring systems
Seismic protection
Active and passive structural control
Earthquake hazard mitigation
Remote sensing for earthquake engineering
Failure detection, diagnostics and prognostics

Dr. Eric M. Lui
Guest Editor

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Keywords

Structural health monitoring
Earthquake engineering
Resilient infrastructure
Smart structures
Data analytics
Deep learning

Published Papers (10 papers)

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Research

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35 pages, 21129 KiB

Open AccessArticle

De-Bonding Numerical Characterization and Detection in Aeronautic Multi-Element Spars

by Antonio Concilio, Monica Ciminello, Bernardino Galasso, Lorenzo Pellone, Umberto Mercurio, Gianvito Apuleo, Aniello Cozzolino, Iddo Kressel, Shay Shoham and David Bardenstein

Sensors 2022, 22(11), 4152; https://0-doi-org.brum.beds.ac.uk/10.3390/s22114152 - 30 May 2022

Cited by 6 | Viewed by 1903

Abstract

Structural health monitoring has multifold aims. Concerning composite structures, the main objectives are perhaps reducing costs by shifting from scheduled to on-demand maintenance and reducing weight by removing redundant precautions as the insertion of chicken fasteners to for ensuring joint safety in cases of bonding layer fail. Adhesion defects may be classified along different types, for instance distinguishing between glue deficiency or de-bonding. This paper deals with a preliminary numerical characterization of adhesive layer imperfections on a representative aircraft component. The multipart composite spar is made of two plates and two corresponding C-beams, bonded together to form an almost squared boxed section beam. A numerical test campaign was devoted to extract relevant information from different defect layouts and to try to assess some parameters that could describe their peculiarities. A focus was then given to macroscopic evidence of fault effects behavior, as localization, reciprocal interference, impact on structural response, and so on. A proprietary code was finally used to retrieve the presence and size of the imperfections, correlating numerical outcomes with estimations. Activities were performed along OPTICOMS, a European project funded within the Clean Sky 2 Joint Technology Initiative (JTI). Full article

(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Seismic Protection)

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15 pages, 3177 KiB

Open AccessEditor’s ChoiceArticle

Structural Health Monitoring: An IoT Sensor System for Structural Damage Indicator Evaluation

by Mirco Muttillo, Vincenzo Stornelli, Rocco Alaggio, Romina Paolucci, Luca Di Battista, Tullio de Rubeis and Giuseppe Ferri

Sensors 2020, 20(17), 4908; https://0-doi-org.brum.beds.ac.uk/10.3390/s20174908 - 31 Aug 2020

Cited by 36 | Viewed by 6237

Abstract

In the last decades, the applications of structural monitoring are moving toward the field of civil engineering and infrastructures. Nevertheless, if the structures have damages, it does not mean that they have a complete loss of functionality, but rather that the system is no longer in an optimal condition so that, if the damage increases, the structure can collapse. Structural Health Monitoring (SHM), a process for the identification of damage, periodically collects data from suitable sensors that allow to characterize the damage and establishes the health status of the structure. Therefore, this monitoring will provide information on the structure condition, mostly about its integrity, in a short time, and, for infrastructures and civil structures, it is necessary to assess performance and health status. The aim of this work is to design an Internet of Things (IoT) system for Structural Health Monitoring to find possible damages and to see how the structure behaves over time. For this purpose, a customized datalogger and nodes have been designed. The datalogger is able to acquire the data coming from the nodes through RS485 communication and synchronize acquisitions. Furthermore, it has an internal memory to allow for the post-processing of the collected data. The nodes are composed of a digital triaxial accelerometer, a general-purpose microcontroller, and an external memory for storage measures. The microcontroller communicates with an accelerometer, acquires values, and then saves them in the memory. The system has been characterized and the damage indicator has been evaluated on a testing structure. Experimental results show that the estimated damage indicator increases when the structure is perturbed. In the present work, the damage indicator increased by a maximum value of 24.65 when the structure is perturbed by a 2.5 mm engraving. Full article

(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Seismic Protection)

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25 pages, 15093 KiB

Open AccessArticle

Automated and Model-Free Bridge Damage Indicators with Simultaneous Multiparameter Modal Anomaly Detection

by Thanh T. X. Tran and Ekin Ozer

Sensors 2020, 20(17), 4752; https://0-doi-org.brum.beds.ac.uk/10.3390/s20174752 - 22 Aug 2020

Cited by 14 | Viewed by 2779

Abstract

This paper pursues a simultaneous modal parameter anomaly detection paradigm to structural damage identification inferred from vibration-based structural health monitoring (SHM) sensors, e.g., accelerometers. System Realization Using Information Matrix (SRIM) method is performed in short duration sweeping time windows for identification of state matrices, and then, modal parameters with enhanced automation. Stable modal poles collected from stability diagrams are clustered and fed into the Gaussian distribution-based anomaly detection platform. Different anomaly thresholds are examined both on frequency and damping ratio terms taking two testbed bridge structures as application means, and simplistic Boolean Operators are performed to merge univariate anomalies. The first bridge is a reinforced concrete bridge subjected to incremental damage through a series of seismic shake table experiments conducted at the University of Nevada, Reno. The second bridge is a steel arch structure at Columbia University Morningside Campus, which reflects no damage throughout the measurements, unlike the first one. Two large-scale implementations indicate the realistic performance of automated modal analysis and anomaly recognition with minimal human intervention in terms of parameter extraction and learning supervision. Anomaly detection performance, presented in this paper, shows variation according to the designated thresholds, and hence, the information retrieval metrics being considered. The methodology is well-fitted to SHM problems which require sole data-driven, scalable, and fully autonomous perspectives. Full article

(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Seismic Protection)

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32 pages, 14279 KiB

Open AccessArticle

Sparse and Random Sampling Techniques for High-Resolution, Full-Field, BSS-Based Structural Dynamics Identification from Video

by Bridget Martinez, Andre Green, Moises Felipe Silva, Yongchao Yang and David Mascareñas

Sensors 2020, 20(12), 3526; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123526 - 22 Jun 2020

Cited by 14 | Viewed by 2885

Abstract

Video-based techniques for identification of structural dynamics have the advantage that they are very inexpensive to deploy compared to conventional accelerometer or strain gauge techniques. When structural dynamics from video is accomplished using full-field, high-resolution analysis techniques utilizing algorithms on the pixel time series such as principal components analysis and solutions to blind source separation the added benefit of high-resolution, full-field modal identification is achieved. An important property of video of vibrating structures is that it is particularly sparse. Typically video of vibrating structures has a dimensionality consisting of many thousands or even millions of pixels and hundreds to thousands of frames. However the motion of the vibrating structure can be described using only a few mode shapes and their associated time series. As a result, emerging techniques for sparse and random sampling such as compressive sensing should be applicable to performing modal identification on video. This work presents how full-field, high-resolution, structural dynamics identification frameworks can be coupled with compressive sampling. The techniques described in this work are demonstrated to be able to recover mode shapes from experimental video of vibrating structures when 70% to 90% of the frames from a video captured in the conventional manner are removed. Full article

(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Seismic Protection)

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19 pages, 7967 KiB

Open AccessArticle

Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model

by Hai Chien Pham, Quoc-Bao Ta, Jeong-Tae Kim, Duc-Duy Ho, Xuan-Linh Tran and Thanh-Canh Huynh

Sensors 2020, 20(12), 3382; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123382 - 15 Jun 2020

Cited by 52 | Viewed by 4998 | Correction

Abstract

In this study, we investigate a novel idea of using synthetic images of bolts which are generated from a graphical model to train a deep learning model for loosened bolt detection. Firstly, a framework for bolt-loosening detection using image-based deep learning and computer graphics is proposed. Next, the feasibility of the proposed framework is demonstrated through the bolt-loosening monitoring of a lab-scaled bolted joint model. For practicality, the proposed idea is evaluated on the real-scale bolted connections of a historical truss bridge in Danang, Vietnam. The results show that the deep learning model trained by the synthesized images can achieve accurate bolt recognitions and looseness detections. The proposed methodology could help to reduce the time and cost associated with the collection of high-quality training data and further accelerate the applicability of vision-based deep learning models trained on synthetic data in practice. Full article

(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Seismic Protection)

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32 pages, 10838 KiB

Open AccessArticle

Output-Only Damage Detection of Shear Building Structures Using an Autoregressive Model-Enhanced Optimal Subpattern Assignment Metric

by Liu Mei, Huaguan Li, Yunlai Zhou, Dawang Li, Wujian Long and Feng Xing

Sensors 2020, 20(7), 2050; https://0-doi-org.brum.beds.ac.uk/10.3390/s20072050 - 06 Apr 2020

Cited by 7 | Viewed by 3595

Abstract

This paper proposes a novel output-only structural damage indicator by incorporating the pole-based optimal subpattern assignment distance with autoregressive models to localize and relatively assess the severity of damages for sheared structures. Autoregressive models can model dynamic systems well, while their model poles can represent the state of the dynamic systems. Structural damage generally causes changes in the dynamic characteristics (especially the natural frequency, mode shapes and damping ratio) of structures. Since the poles of the autoregressive models can solve the modal parameters of the structure, the poles have a close relationship with the modal parameters so that the changes in the poles of its autoregressive model reflect structural damages. Therefore, we can identify the damage by tracking the shifts in the dynamic system poles. The optimal subpattern assignment distance, which is the performance evaluator in multi-target tracking algorithms to measure the metric between true and estimated tracks, enables the construction of damage sensitive indicator from system poles using the Hungarian algorithm. The proposed approach has been validated with a five-story shear-building using numerical simulations and experimental verifications, which are subjected to excitations of white noise, El Centro earthquake and sinusoidal wave with frequencies sweeping, respectively; the results indicate that this approach can localize and quantify structural damages effectively in an output-only and data-driven way. Full article

(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Seismic Protection)

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19 pages, 15552 KiB

Open AccessArticle

A Method of Interstory Drift Monitoring Using a Smartphone and a Laser Device

by Jinke Li, Botao Xie and Xuefeng Zhao

Sensors 2020, 20(6), 1777; https://0-doi-org.brum.beds.ac.uk/10.3390/s20061777 - 23 Mar 2020

Cited by 5 | Viewed by 2999

Abstract

Interstory drift is an important engineering parameter in building design and building structural health monitoring. However, many problems exist in current interstory drift monitoring methods. The traditional method is imprecise—double numerical integration of acceleration data—and other direct monitoring methods need professional equipment. This paper proposes a method to solve these problems by monitoring the interstory drift with a smartphone and a laser device. In this method, a laser device is installed on the ceiling while a smartphone is fixed on a steel projection plate on the floor. Compared with a reference sensor, the method designed in this study shows that a smartphone is competent in monitoring the interstory drift. This method utilizes a smartphone application (APP) named D-Viewer to implement monitoring and data storage just in one place, which is also inexpensive. The results showed that this method has an average percent error of 3.37%, with a standard deviation of 2.67%. With the popularization of the smartphone, this method is promising in acquiring large amounts of data, which will be significant for building assessment after an earthquake. Full article

(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Seismic Protection)

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17 pages, 6202 KiB

Open AccessArticle

Strong Wind Characteristics and Buffeting Response of a Cable-Stayed Bridge under Construction

by Lei Yan, Lei Ren, Xuhui He, Siying Lu, Hui Guo and Teng Wu

Sensors 2020, 20(4), 1228; https://0-doi-org.brum.beds.ac.uk/10.3390/s20041228 - 24 Feb 2020

Cited by 8 | Viewed by 3512

Abstract

This study carries out a detailed full-scale investigation on the strong wind characteristics at a cable-stayed bridge site and associated buffeting response of the bridge structure during construction, using a field monitoring system. It is found that the wind turbulence parameters during the typhoon and monsoon conditions share a considerable amount of similarity, and they can be described as the input turbulence parameters for the current wind-induced vibration theory. While the longitudinal turbulence integral scales are consistent with those in regional structural codes, the turbulence intensities and gust factors are less than the recommended values. The wind spectra obtained via the field measurements can be well approximated by the von Karman spectra. For the buffeting response of the bridge under strong winds, its vertical acceleration responses at the extreme single-cantilever state are significantly larger than those in the horizontal direction and the increasing tendencies with mean wind velocities are also different from each other. The identified frequencies of the bridge are utilized to validate its finite element model (FEM), and these field-measurement acceleration results are compared with those from the FEM-based numerical buffeting analysis with measured turbulence parameters. Full article

(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Seismic Protection)

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Review

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34 pages, 3827 KiB

Open AccessReview

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

by Mohsen Azimi, Armin Dadras Eslamlou and Gokhan Pekcan

Sensors 2020, 20(10), 2778; https://0-doi-org.brum.beds.ac.uk/10.3390/s20102778 - 13 May 2020

Cited by 322 | Viewed by 24495

Abstract

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications. Full article

(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Seismic Protection)

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Other

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1 pages, 460 KiB

Open AccessCorrection

Correction: Pham et al. Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model. Sensors 2020, 20, 3382

by Hai Chien Pham, Quoc-Bao Ta, Jeong-Tae Kim, Duc-Duy Ho, Xuan-Linh Tran and Thanh-Canh Huynh

Sensors 2021, 21(16), 5280; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165280 - 05 Aug 2021

Viewed by 1420

Abstract

The authors wish to make the following correction to this paper [...] Full article

(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Seismic Protection)

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