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Structural Health Monitoring for Smart Structures

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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 August 2021) | Viewed by 14818

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Special Issue Editor

Dr. Kong Fah Tee

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Guest Editor

Faculty of Engineering and Science, University of Greenwich, Kent ME4 4TB, UK
Interests: structural reliability and risk-cost optimization; structural health monitoring and management; structural system identification and life prediction; forensic engineering; wave propagation and signal processing; experimental stress analysis; fatigue and fracture mechanics; structural control and smart structures; structural behavior and design under extreme loading: fire, wind, and explosion
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Special Issue Information

Dear Colleagues,

The science of monitoring (continuous or periodic) of the condition of a structure using built-in or autonomous sensory systems is called structural health monitoring (SHM). The acquired data in combination with advanced signal processing techniques can indicate the need for monitoring, inspection, damage assessment, and maintenance actions upon demand. This Special Issue will focus on all the opportunities of SHM for smart structures. This Special Issue focuses on recent developments in theoretical, computational, experimental, and practical aspects in the field. Topics include, but are not limited to, the following:

Autonomous systems for SHM and maintenance intervention
Big data, artificial intelligence, and deep learning in infrastructures
Damage detection and assessment
Damage in composite and smart materials
Damage modelling
Fatigue damage
Fracture and damage mechanisms
Innovative sensing solutions for SHM
Intelligent health management
Modal analysis
Model verification and validation
Modelling and simulation
Multifunctional materials and structures
Multiscale dynamics and modelling
Non-contact dynamics measurement
Non-destructive testing and evaluation
Non-linear guided waves
Real-world SHM applications
Sensors and actuators
Sensor network and system integration
Signal processing
Smart repairs
Structural health monitoring
Structural integrity and reliability
System identification and assessment
Structural model updating
Structural rehabilitation and maintenance
Vibration-based SHM
Vibration isolation and mitigation

Dr. Kong Fah Tee
Guest Editor

Manuscript Submission Information

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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

damage detection
sensors
signal precessing
structural health monitoring
smart structures
system identification

Published Papers (6 papers)

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Research

18 pages, 4413 KiB

Open AccessArticle

A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique

by Hui Chen, Bin Huang, Kong Fah Tee and Bo Lu

Sensors 2021, 21(9), 3290; https://doi.org/10.3390/s21093290 - 10 May 2021

Cited by 3 | Viewed by 1976

Abstract

This paper proposes a new stochastic model updating method to update structural models based on the improved cross-model cross-mode (ICMCM) technique. This new method combines the stochastic hybrid perturbation-Galerkin method with the ICMCM method to solve the model updating problems with limited measurement data and uncertain measurement errors. First, using the ICMCM technique, a new stochastic model updating equation with an updated coefficient vector is established by considering the uncertain measured modal data. Then, the stochastic model updating equation is solved by the stochastic hybrid perturbation-Galerkin method so as to obtain the random updated coefficient vector. Following that, the statistical characteristics of the updated coefficients can be determined. Numerical results of a continuous beam show that the proposed method can effectively cope with relatively large uncertainty in measured data, and the computational efficiency of this new method is several orders of magnitude higher than that of the Monte Carlo simulation method. When considering the rank deficiency, the proposed stochastic ICMCM method can achieve more accurate updating results compared with the cross-model cross-mode (CMCM) method. An experimental example shows that the new method can effectively update the structural stiffness and mass, and the statistics of the frequencies of the updated model are consistent with the measured results, which ensures that the updated coefficients are of practical significance. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Smart Structures)

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32 pages, 5780 KiB

Open AccessArticle

A Novel Runtime Algorithm for the Real-Time Analysis and Detection of Unexpected Changes in a Real-Size SHM Network with Quasi-Distributed FBG Sensors

by Felipe Isamu H. Sakiyama, Frank Lehmann and Harald Garrecht

Sensors 2021, 21(8), 2871; https://doi.org/10.3390/s21082871 - 19 Apr 2021

Cited by 7 | Viewed by 2753

Abstract

The ability to track the structural condition of existing structures is one of the main concerns of bridge owners and operators. In the context of bridge maintenance programs, visual inspection predominates nowadays as the primary source of information. Yet, visual inspections alone are insufficient to satisfy the current needs for safety assessment. From this perspective, extensive research on structural health monitoring has been developed in recent decades. However, the transfer rate from laboratory experiments to real-case applications is still unsatisfactory. This paper addresses the main limitations that slow the deployment and the acceptance of real-size structural health monitoring systems (SHM) and presents a novel real-time analysis algorithm based on random variable correlation for condition monitoring. The proposed algorithm was designed to respond automatically to detect unexpected events, such as local structural failure, within a multitude of random dynamic loads. The results are part of a project on SHM, where a high sensor-count monitoring system based on long-gauge fiber Bragg grating sensors (LGFBG) was installed on a prestressed concrete bridge in Neckarsulm, Germany. The authors also present the data management system developed to handle a large amount of data, and demonstrate the results from one of the implemented post-processing methods, the principal component analysis (PCA). The results showed that the deployed SHM system successfully translates the massive raw data into meaningful information. The proposed real-time analysis algorithm delivers a reliable notification system that allows bridge managers to track unexpected events as a basis for decision-making. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Smart Structures)

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25 pages, 6100 KiB

Open AccessArticle

A Novel Stochastic Approach for Static Damage Identification of Beam Structures Using Homotopy Analysis Algorithm

by Zhifeng Wu, Bin Huang, Kong Fah Tee and Weidong Zhang

Sensors 2021, 21(7), 2366; https://doi.org/10.3390/s21072366 - 29 Mar 2021

Cited by 4 | Viewed by 1896

Abstract

This paper proposes a new damage identification approach for beam structures with stochastic parameters based on uncertain static measurement data. This new approach considers not only the static measurement errors, but also the modelling error of the initial beam structures as random quantities, and can also address static damage identification problems with relatively large uncertainties. First, the stochastic damage identification equations with respect to the damage indexes were established. On this basis, a new homotopy analysis algorithm was used to solve the stochastic damage identification equations. During the process of solution, a static condensation technique and a L1 regularization method were employed to address the limited measurement data and ill-posed problems, respectively. Furthermore, the definition of damage probability index is presented to evaluate the possibility of existing damages. The results of two numerical examples show that the accuracy and efficiency of the proposed damage identification approach are good. In comparison to the first-order perturbation method, the proposed method can ensure better accuracy in damage identification with relatively large measurement errors and modelling error. Finally, according to the static tests of a simply supported concrete beam, the proposed method successfully identified the damage of the beam. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Smart Structures)

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15 pages, 4067 KiB

Open AccessCommunication

Microchip Health Monitoring System Using the FLL Circuit

by Emmanuel Bender and Joseph B. Bernstein

Sensors 2021, 21(7), 2285; https://doi.org/10.3390/s21072285 - 24 Mar 2021

Cited by 2 | Viewed by 2095

Abstract

Here a solution for a Microchip Health Monitoring (MHM) system using MTOL (Multi-Temperature Operational Life) reliability testing assessment data is proposed. The module monitors frequency degradation over time compared to lab tested data. Since trends in performance degradation in recently developed devices have transitioned from multiple failure mechanisms to a single dominant failure mechanism, development of the monitor is greatly simplified. The monitor uses a novel circuit customized to deliver optimum accuracy by combining the concepts of ring oscillator (RO) and phase locked loop (PLL) circuits. The modified circuit proposed is a new form of the frequency locked loop (FLL) circuit. We demonstrate that the collection of frequency degradation data from the ring circuits of each test produces Weibull distributions with steep slopes. This implies that the monitor can predict accurate end-of-life (EOL) predictions at early stages of chip degradations. The design of the microchip health monitoring system projected in this work can have great benefit in all systems using FPGA and ASIC devices. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Smart Structures)

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15 pages, 5724 KiB

Open AccessArticle

An Analysis of the Reliability of a Bus Safety Structure on Carrying Out the Numerical and Experimental Tests

by Tautvydas Pravilonis, Valdas Eidukynas and Edgar Sokolovskij

Sensors 2020, 20(24), 7092; https://doi.org/10.3390/s20247092 - 11 Dec 2020

Cited by 4 | Viewed by 2611

Abstract

In the paper, the reliability of a spatial tubular structure of a bus safety frame formed of different steel profiles is discussed. A methodology for the bus safety structure modeling is presented herein by applying numerical methods that enable us to simulate virtually a test for assessing bus rollover crashworthiness according to the United Nations Economic Commission for Europe (UNECE) Regulation No. 66, and also to assess and ensure the reliability and safety of the structure under operating conditions. The simulation has been performed by applying the mixed method of kinematical analysis and finite elements. In the course of the calculations, physical and geometrical non-linearity of materials was assessed. In addition, an experimental rollover test according to UNECE Regulation No. 66 was performed in this work, striving to verify the provided methodology for modeling by applying numerical methods. For the experiment, an identical safety structure and a rollover stand (identical to the one used in modeling) were used. The rollover test was shot by a Phantom v711 high-speed camera. In the paper, the results of kinematical and dynamic analysis from applying the finite element method and the ones of the experimental test, as well as their comparisons, are provided. It is assessed whether the developed safety structure model is reliable and suitable for use. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Smart Structures)

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23 pages, 10785 KiB

Open AccessArticle

Distributed Optical Fiber Sensing Bonding Techniques Performance for Embedment inside Reinforced Concrete Structures

by Mattia Francesco Bado, Joan R. Casas, Alinda Dey and Carlos Gil Berrocal

Sensors 2020, 20(20), 5788; https://doi.org/10.3390/s20205788 - 13 Oct 2020

Cited by 31 | Viewed by 2460

Abstract

Distributed optical fiber sensors (DOFS) are modern-day cutting-edge monitoring tools that are quickly acquiring relevance in structural health monitoring engineering. Their most ambitious use is embedded inside plain or reinforced concrete (RC) structures with the scope of comprehending their inner-workings and the functioning of the concrete-reinforcement interaction. Yet, multiple studies have shown that the bonding technique with which the DOFS are bonded to the reinforcement bars has a significant role on the quality of the extracted strain data. Whilst this influence has been studied for externally bonded DOFS, it has not been done for embedded ones. The present article is set on performing such study by monitoring the strain measurement quality as sampled by DOFS bonded to multiple rebars with different techniques and adhesives. These instrumented rebars are used to produce differently sized RC ties later tested in tension. The discussion of the test outputs highlights the quasi-optimal performance of a DOFS/rebar bonding technique consisting of incising a groove in the rebar, positioning the DOFS inside it, bonding it with cyanoacrylate and later adding a protective layer of silicone. The resulting data is mostly noise-free and anomalies-free, yet still presents a newly diagnosed hitch that needs addressing in future research. Full article

(This article belongs to the Special Issue Structural Health Monitoring for Smart Structures)

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