Optimal sensor placement in structural health monitoring (SHM) with a field application on a RC bridge

Structural health monitoring (SHM) is a research field that targets detecting and locating damage in structures. The main objective of SHM is to detect damage at its onset and inform authorities about the type, nature and location of the damage in the structure. Successful SHM requires deploying optimal sensor networks. We present a probabilistic approach to identify optimal location of sensors based on a priori knowledge on damage locations while considering the need for redundancy in sensor networks. The optimal number of sensors is identified using a multi-objective optimization approach incorporating information entropy and cost of the sensor network. As the size of the structure grows, the advantage of the optimal sensor network in damage detection becomes obvious. We also present an innovative field application of SHM using Field Programmable Gate Array (FPGA) and wireless communication technologies. The new SHM system was installed to monitor a reinforced concrete (RC) bridge on interstate I-40 in Tucumcari, New Mexico. The new monitoring system is powered with renewable solar energy. The integration of FPGA and photovoltaic technologies make it possible to remotely monitor infrastructure with limited access to power. Using calibrated finite element (FE) model of the bridge with real data collected from the sensors installed on the bridge, we establish fuzzy sets describing different damage states of the bridge. Unknown states of the bridge performance are then identified using degree of similarity between these fuzzy sets. The proposed SHM system will reduce human intervention significantly and can save millions of dollars currently spent on prescheduled inspection by enabling performance based monitoring

Optimal Number and Location of Sensors for Structural Damage Detection using the Theory of Geometrical Viewpoint and Parameter Subset Selection Method

The recorded responses at predefined sensor placements are used as input to solve an inverse structural damage detection problem. The error rate that noise causes from the recorded responses of the sensors is a significant issue in damage detection methods. Therefore, an optimal number and location of sensors is a goal to achieve the lowest error rate in structural damage detection. To overcome this problem, an algorithm (GVPSS) based on a Geometrical Viewpoint (GV) of optimal sensor placement and Parameter Subset Selection (PSS) method is proposed. The goal of the GVPSS algorithm is to minimize the effect of noise on damage detection problem. Therefore, the fitness function based on error in damage detection is minimized by GVPSS. In this method, the degrees of freedom are arranged to place sensors using a fitness function based on GV theory. Then, the optimal number and location of sensors are found on these arranged the degrees of freedom using the objective function. The efficiency of the proposed method is studied in a 52-bar dome structure under static and dynamic loadings. In the examples, damages are detected in two states: 1) using responses recorded at all DOFs, 2) using responses recorded at the optimal number and location of sensors obtained by GVPSS. The results showed that the damage detection error in state 2 is approximately equal to the error in state 1. Therefore, the GVPSS have the high performance to find the optimal number and location of sensors for structural damage detection

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

Bayesian structural identification of a long suspension bridge considering temperature and traffic load effects

© The Author(s) 2018. This article presents a probabilistic structural identification of the Tamar bridge using a detailed finite element model. Parameters of the bridge cables initial strain and bearings friction were identified. Effects of temperature and traffic were jointly considered as a driving excitation of the bridge’s displacement and natural frequency response. Structural identification is performed with a modular Bayesian framework, which uses multiple response Gaussian processes to emulate the model response surface and its inadequacy, that is, model discrepancy. In addition, the Metropolis–Hastings algorithm was used as an expansion for multiple parameter identification. The novelty of the approach stems from its ability to obtain unbiased parameter identifications and model discrepancy trends and correlations. Results demonstrate the applicability of the proposed method for complex civil infrastructure. A close agreement between identified parameters and test data was observed. Estimated discrepancy functions indicate that the model predicted the bridge mid-span displacements more accurately than its natural frequencies and that the adopted traffic model was less able to simulate the bridge behaviour during traffic congestion periods

Adaptive data analysis for damage detection and system identification in civil infrastructure

Time-varying structural systems are often encountered in civil engineering. As extreme events occur more frequently and severely in recent years, more structures are loaded beyond their elastic conditions and may thus experience damage in the years to come. Even if structures remain elastic, energy dissipation devices installed on structures often reveal hysteretic behaviors under earthquake loads. Therefore, it is imperative to develop and implement novel technologies that enable the identification and damage detection of time-varying systems. In this dissertation, adaptive wavelet transform (AWT) and multiple analytical mode decomposition (M-AMD) are proposed and applied to identify system properties and detect damage in structures. AWT is an optimized time-frequency representation of dynamic responses for the extraction of features. It is defined as an average of overlapped short-time wavelet transforms with time-varying wavelet parameters in order to extract time-dependent frequencies. The effectiveness of AWT is demonstrated by various analytical signals, acoustic emission and impact echo responses. M-AMD is a response decomposition method for the identification of weakly to moderately nonlinear oscillators based on vibration responses. It can be used to accurately separate the low and high frequency components of time-varying stiffness and damping coefficients in dynamic systems. The efficiency and accuracy of the proposed M-AMD are evaluated with three characteristic nonlinear oscillators and a 1/4-scale 3-story building model with frictional damping under seismic excitations. Finally, AWT-based M-AMD is applied to decompose the measured dynamic responses of a 1/20-scale cable-stayed bridge model tested on four shake tables and evaluate the progression of damage under increasing earthquake loads --Abstract, page iii

Substructural condition assessment of bridge structures under moving vehicles

Bridge infrastructures are continuously subject to degradation, due to aging, their operational environment, and excess loading, which places users at risk. It has now become a major concern worldwide, where the majority of bridge infrastructures are approaching their design life, and the number of bridges in poor condition is increasing. This compels the engineering community to develop robust and reliable methods for continuous monitoring of bridge infrastructures. Most of the existing methods are time-consuming, labour-intensive, and expensive or they are not robust enough to be used in real-world applications. To address this problem, new methods need to be developed, and rather than numerical verifications laboratory and field tests should be carried out for experimental validation. In this research project, condition assessment of bridge structures under moving vehicles is investigated. The bridge subjected to a moving vehicle is subjected to one type of forced vibration test, with no need for traffic interruption and extensive experimental arrangements. Using moving vehicles as an exciter has the ability to induce structural vibration with a large enough amplitude and reasonable signal-to-noise ratio. Experimental and numerical studies on a bridge structure subject to moving loads indicate the robustness and efficiency of the proposed techniques to deal with road roughness, and vehicle speed in moving load identification as well as detecting and quantifying structural damage. The proposed techniques have the potential to reduce the number of sensors needed for bridge structural health monitoring as well as to reduce the computational effort and costs while enhancing the accuracy

Health condition monitoring of civil structures using time varying autoregressive models

In recent years, there have been an increasing interest in long-term monitoring of civil structures, as the research community has been alarmed by some tragic events and collapses of bridges and buildings that pointed out the vulnerability of some existing structures and the uncertainties in their analysis for monitoring and maintenance purposes. SHM is the measurement of the operating and loading environment; as well as the critical responses of a structure to track and evaluate the symptoms of incidents, anomalies, damage and/or deterioration which may affect operation, serviceability, safety and reliability. Although many damage detection techniques were applied to scaled models or specimen tests in controlled laboratory environments, the performance of these techniques in real operational environments is still questionable and needs to be validated. Often damage sensitive features employed in these damage detection techniques are also sensitive to changes of environmental and operation conditions of the structure. The objective of this study is to propose a new Time Varying Autoregressive (TVAR) modeling technique for SHM of large-scale structures like bridges and buildings. TVAR model, a method by virtue of its nature is applicable for modeling data whose spectral content varies with time. The research is conducted to critically understand the effective performance of the structures under various loads and health conditions, and detect their operational anomalies using the proposed data-driven technique. In this research, an attempt is made to alleviate the use of system identification method where TVAR modeling is conducted directly on the data. The proposed method does not depend on the complicated algorithms and free of any other user-defined parameters. In pursuance of applying the proposed data-driven technique, the data collected on site are essentially paramount. Data inherently used are mainly obtained from experiments, as well as the data acquired from the Harbin Institute of Technology in fulfillment of a full-scale validation. The proposed TVAR technique detects not only the occurrence of structural damage, but also the location of damage. Whereas the TVAR developed captures the changes in the time domain, for comparison, Stochastic Subspace System Identification (SSI) method is applied to the experimental data. The method is used because it is an important tool that captures the frequency changes, as the SSI tracks the changes in the frequency domain. Using both experimental and full-scale studies, it is shown that the proposed TVAR technique and the comparable SSI method applied, can therefore be considered as a useful tool for SHM

Non-contact vision-based deformation monitoring on bridge structures

Information on deformation is an important metric for bridge condition and performance assessment, e.g. identifying abnormal events, calibrating bridge models and estimating load carrying capacities, etc. However, accurate measurement of bridge deformation, especially for long-span bridges remains as a challenging task. The major aim of this research is to develop practical and cost-effective techniques for accurate deformation monitoring on bridge structures. Vision-based systems are taken as the study focus due to a few reasons: low cost, easy installation, desired sample rates, remote and distributed sensing, etc. This research proposes an custom-developed vision-based system for bridge deformation monitoring. The system supports either consumer-grade or professional cameras and incorporates four advanced video tracking methods to adapt to different test situations. The sensing accuracy is firstly quantified in laboratory conditions. The working performance in field testing is evaluated on one short-span and one long-span bridge examples considering several influential factors i.e. long-range sensing, low-contrast target patterns, pattern changes and lighting changes. Through case studies, some suggestions about tracking method selection are summarised for field testing. Possible limitations of vision-based systems are illustrated as well. To overcome observed limitations of vision-based systems, this research further proposes a mixed system combining cameras with accelerometers for accurate deformation measurement. To integrate displacement with acceleration data autonomously, a novel data fusion method based on Kalman filter and maximum likelihood estimation is proposed. Through field test validation, the method is effective for improving displacement accuracy and widening frequency bandwidth. The mixed system based on data fusion is implemented on field testing of a railway bridge considering undesired test conditions (e.g. low-contrast target patterns and camera shake). Analysis results indicate that the system offers higher accuracy than using a camera alone and is viable for bridge influence line estimation. With considerable accuracy and resolution in time and frequency domains, the potential of vision-based measurement for vibration monitoring is investigated. The proposed vision-based system is applied on a cable-stayed footbridge for deck deformation and cable vibration measurement under pedestrian loading. Analysis results indicate that the measured data enables accurate estimation of modal frequencies and could be used to investigate variations of modal frequencies under varying pedestrian loads. The vision-based system in this application is used for multi-point vibration measurement and provides results comparable to those obtained using an array of accelerometers

Innovations and advances in structural engineering: Honoring the career of Yozo Fujino

This special issue of Smart Structures and Systems (SSS) is dedicated to Dr. Yozo Fujino to celebrate his outstanding and innovative contributions to structural engineering during his career. The papers in this issue present a wide range of recent results on bridge dynamics, wind and earthquake effects on structures, health monitoring, and passive/active control technology. This collection of papers also provides a glimpse into the broad nature of Dr. Fujino’s interests. Prof. Fujino is an internationally recognized leader who has been an inspiration to industrial and academic scientists and engineers for over 30 years. During his brilliant academic career, Prof. Fujino has made and continues to make fundamental contributions to dynamics, control and monitoring of bridges considering both wind actions and earthquakes loading. In addition, he has consulted on over 30 signature bridge projects including Akashi Kaikyo Bridge in Japan, Millennium Bridge (vibration control) in UK and Stonecutters Bridge in Hong Kong, demonstrating his recognition not only for his research achievements, but also for his practical knowledge and experience in bridge engineering. In addition to his numerous contributions to science and engineering, Dr. Fujino is a dedicated and passionate teacher and professor, inspiring young scientists and engineers to advance their knowledge and experiences. Dr. Fujino is currently a Distinguished Professor of Advanced Sciences at Yokohama National University (YNU) in Japan. He is also jointly appointed as a Program Director (Policy Adviser) for the Council for Science, Technology and Innovation, Cabinet Office, Japanese Government. Prior to joining YNU, he served for more than 30 years as a Professor of Civil Engineering and the head of the Bridge and Structures Laboratory at The University of Tokyo. On behalf of all the contributors to this special issue, we would like to sincerely congratulate Dr. Yozo Fujino on a truly amazing career and wish him good health, happiness, and many more contributions to structural engineering in the years to come.Ope