Offline and online detection of damage using autoregressive models and artificial neural networks

Peer reviewedPostprin

Health monitoring of civil infrastructures by subspace system identification method: an overview

Structural health monitoring (SHM) is the main contributor of the future's smart city to deal with the need for safety, lower maintenance costs, and reliable condition assessment of structures. Among the algorithms used for SHM to identify the system parameters of structures, subspace system identification (SSI) is a reliable method in the time-domain that takes advantages of using extended observability matrices. Considerable numbers of studies have specifically concentrated on practical applications of SSI in recent years. To the best of author's knowledge, no study has been undertaken to review and investigate the application of SSI in the monitoring of civil engineering structures. This paper aims to review studies that have used the SSI algorithm for the damage identification and modal analysis of structures. The fundamental focus is on data-driven and covariance-driven SSI algorithms. In this review, we consider the subspace algorithm to resolve the problem of a real-world application for SHM. With regard to performance, a comparison between SSI and other methods is provided in order to investigate its advantages and disadvantages. The applied methods of SHM in civil engineering structures are categorized into three classes, from simple one-dimensional (1D) to very complex structures, and the detectability of the SSI for different damage scenarios are reported. Finally, the available software incorporating SSI as their system identification technique are investigated

Structural Health Monitoring and Damage Identification of Bridges Using Triaxial Geophones and Time Series Analysis

This study uses the vibration data of two full-scale bridges, subjected to controlled damage, along the I-40 west, near downtown Knoxville, TN, to evaluate the feasibility of time series-based damage identification techniques for structural health monitoring. The vibration data was acquired for the entrance ramp to James White Parkway from I-40 westbound, and the I-40 westbound bridge over 4th Avenue, before the bridges were demolished during I-40 expansion project called Smartfix40. The vibration data was recorded using an array of triaxial geophones, highly sensitive sensors to record vibrations, in healthy and damaged conditions of the bridges. The vibration data is evaluated using linear stationary time series models to extract damage sensitive-features (DSFs) which are used to identify the condition of bridge. Two time series-based damage identification techniques are used and developed in this study. In the first technique, the vibration data is corrected for sensor transfer function suitable for given geophone type and then convolved with random values to create input for autoregressive (AR) time series models. A two-stage prediction model, combined AR and autoregressive with exogenous input (ARX), is employed to obtain DSFs. An outlier analysis method based on DSF values is used to detect the damage. The technique is evaluated using the vertical vibration data of the two bridges subjected to three controlled amounts of known damage on the steel girders. In the second technique, ARX models and sensor clustering technique is used to obtain prediction errors in healthy and damaged conditions of the bridges. DSF is defined as the ratio of the standard deviations of the prediction errors. The proposed technique is evaluated using the triaxial vibration data of the two bridges. This study also presents finite element analysis of the I-40 westbound bridge over 4th Avenue to obtain simulated vibration data for different damage levels and locations. The simulated data are then used in the ARX models and sensor clustering damage identification technique to investigate the effects of damage location and extent, efficacy of each triaxial vibration, and effect of noise on the vibration-based damage identification techniques

Time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals

A major trust of modal parameters identification (MPI) research in recent years has been based on using artificial and natural vibrations sources because vibration measurements can reflect the true dynamic behavior of a structure while analytical prediction methods, such as finite element models, are less accurate due to the numerous structural idealizations and uncertainties involved in the simulations. This paper presents a state-of-the-art review of the time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals as well as the factors that affect the estimation accuracy. Further, the latest signal processing techniques proposed since 2012 are also reviewed. These algorithms are worth being researched for MPI of large real-life structures because they provide good time-frequency resolution and noise-immunity