Shaking table tests on a masonry building monitored using smart bricks: Damage detection and localization

The seismic behavior of unreinforced masonry buildings is typically characterized by premature brittle collapse mechanisms that can cause serious consequences for the protection of human lives and for the preservation of historical and cultural heritage. Structural health monitoring can be a powerful tool enabling a quick post-earthquake assessment of the structure's performance, but its applications are still scarce as a consequence of the severe limitations affecting off-the-shelf sensing technologies, in terms of local nature of the measurements, costs, as well as long-term behavior, installation, and maintenance. To overcome some of these limitations, the authors have recently proposed a new sensing technology, called “smart brick,” that is a durable clay brick doped with stainless steel microfibers, working as a smart strain sensor for masonry buildings. This paper presents the first full-scale application of smart bricks, used for detecting and localizing progressive earthquake-induced damage in an unreinforced masonry building subjected to shaking table tests. Smart bricks are employed to detect changes in load paths on masonry walls, comparing strain measurements acquired after each step of the seismic sequence with those referring to the undamaged structure. Experimental results are interpreted using a 3D finite element model built to reproduce the shaking table tests. Overall, the results demonstrate that the smart bricks can effectively reveal local permanent changes in structural conditions following a progressive damage, therefore being apt for earthquake-induced damage detection and localization.     

桥梁健康监测及诊断研究综述

本文分析了桥梁结构健康监测和诊断的必要性和紧迫性，对桥梁结构损伤探测的方法进行了详细的分类及论述。提出了桥梁健康监测和诊断的研究新领域——无线监测系统。最后指出了桥梁健康监测和诊断中存在的问题及发展趋势。     

A frequency response function change method for damage localization and quantification in a shear building under ground excitation

A frequency response function change (FRFC) method to detect damage location and extent based on the change in the frequency response functions of a shear building under the effects of ground excitation was proposed in this paper. The damage identification equation was derived from the motion equations of the system before and after the occurrence of the damage. Efforts to make the FRFC method less model‐dependent were made. Intact system matrices, which could be estimated using the measured data without the need for an analytical model, and the frequency response functions were required for the FRFC method. The effects of measurement noise and model parameter error in the FRFC method were studied numerically. The proposed FRFC method was validated by experimental studies of a six‐story steel building structure with single and multiple damage cases. Copyright © 2012 John Wiley & Sons, Ltd.     

The Interpolation Evolution Method for damage localization in structures under seismic excitation

In the aftermath of an earthquake, data acquired by a monitoring system can be used to identify possible damage that occurred in the structure by using algorithms to estimate proper damage features. In this paper, a new method is proposed for damage localization in beam‐like structures under seismic excitation. The proposed algorithm, named the Interpolation Evolution Method (IEM), is based on the combination of two existing methods: the Interpolation Method and the Curvature Evolution Method. Only responses recorded in story accelerations are required to estimate the damage feature with the combined IEM approach. This method does not require a priori knowledge of a “signature” of the structure because it exploits responses recorded during a single strong motion event. Herein, the IEM is applied to case studies of 2 reinforced concrete frames excited by several different ground motions, simulated using nonlinear finite element models and recorded during experimental tests carried out on a shaking table at the University of California, San Diego (USA) and at the University of Basilicata (Italy).     

A method for non-parametric damage detection through the use of neural networks

A neural network-based approach is presented for the detection of changes in the characteristics of structure-unknown systems. The approach relies on the use of vibration measurements from a ‘healthy’ system to train a neural network for identification purposes. Subsequently, the trained network is fed comparable vibration measurements from the same structure under different episodes of response in order to monitor the health of the structure. The methodology is applied to actual data obtained from ambient vibration measurements on a steel building structure that was damaged under strong seismic motion during the Hyogo-Ken Nanbu Earthquake of 17 January 1995. The measurements were done before and after repairs to the damaged frame were made. A neural network is trained with data after the repairs, which represents ‘healthy’ condition of the building. The trained network, which is subsequently fed data before the repairs, successfully identified the difference between the damaged storey and the undamaged storey. Through this study, it is shown that the proposed approach has the potential of being a practical tool for a damage detection methodology applied to smart civil structures. © 1998 John Wiley & Sons, Ltd.     

Output only modal identification and structural damage detection using time frequency ＆ wavelet techniques

The primary objective of this paper is to develop output only modal identifi cation and structural damage detection.Identif ication of multi-degree of freedom(MDOF) linear time invariant(LTI) and linear time variant(LTV—due to damage) systems based on Time-frequency(TF) techniques—such as short-time Fourier transform(STFT),empirical mode decomposition(EMD),and wavelets—is proposed.STFT,EMD,and wavelet methods developed to date are reviewed in detail.In addition a Hilbert transform(HT) approach to determine ...     

Active sensing using impedance‐based ARX models and extreme value statistics for damage detection

In this paper, the applicability of an auto‐regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is established. Damage sensitive features that explicitly consider non‐linear system input/output relationships are extracted from the ARX model. Furthermore, because of the non‐Gaussian nature of the extracted features, Extreme Value Statistics (EVS) is employed to develop a robust damage classifier. EVS provides superior performance to standard statistical methods because the data of interest are in the tails (extremes) of the damage sensitive feature distribution. The suitability of the ARX model, combined with EVS, to non‐linear damage detection is demonstrated using vibration data obtained from a laboratory experiment of a three‐story building model. It is found that the vibration‐based method, while able to discern when damage is present in the structure, is unable to localize the damage to a particular joint. An impedance‐based active sensing method using piezoelectric (PZT) material as both an actuator and a sensor is then investigated as an alternative solution to the problem of damage localization. Copyright © 2005 John Wiley & Sons, Ltd.     

Earthquake damage detection in the Imperial County Services Building III: Analysis of wave travel times via impulse response functions

The majority of structural health monitoring methods are based on detecting changes in the modal properties, which are global characteristics of the structure, and are not sensitive to local damage. Wave travel times between selected sections of a structure, on the other hand, are local characteristics, and are potentially more sensitive to local damage. In this paper, a structural health monitoring method based on changes in wave travel times is explored using strong motion data from the Imperial Valley Earthquake of 1979 recorded in the former Imperial County Services (ICS) Building, severely damaged by this earthquake. Wave travel times are measured from impulse response functions computed from the recorded horizontal seismic response in three time windows—before, during, and after the largest amplitude response, as determined from previous studies of this building, based on analysis of novelties in the recorded response. The results suggest initial spatial distribution of stiffness consistent with the design characteristics, and reduction of stiffness following the major damage consistent with the spatial distribution of the observed damage. The travel times were also used to estimate the fundamental fixed-base frequency of the structure f₁ (assuming the building deformed as a shear beam), and its changes during this earthquake. These estimates are consistent with previous estimates of the soil–structure system frequency, f_sys, during the earthquakes (f₁<f_sys as expected from soil–structure interaction studies), and with other estimates of frequency (f₁ from ETABS models, and f_sys from ambient vibration tests, and “instantaneous” f₁ from high-frequency pulse propagation).     

Earthquake damage detection in the Imperial County Services Building I: The data and time–frequency analysis

The former Imperial County Services Building was a six-story reinforced concrete structure in the El Centro, California, severely damaged by the 1979 Imperial Valley earthquake. It represents a rare case of an instrumented building that has been damaged, and thus can serve as a full-scale benchmark to evaluate and further develop structural health monitoring methods. This paper presents an analysis of inter-story drifts, and of changes in the first NS and EW system frequencies (estimated from the ridge of the Gabor transform of the relative roof displacement response) as indicators of the occurrence of damage. The detected initial decreases of system frequency, of about 28% for NS and 24% for EW motions, are not believed to be due to severe damage. The subsequent decreases, of about 44% for NS and 21% for EW motions, are attributed to damage. Near the end of shaking, increases of about 35% for the NS and 36% for EW motions were detected. (These percentage changes were computed with respect to the previous value for particular interval, rather than a fixed reference). During the most severe shaking, the inter-story drifts exceeded 0.5% for NS and 1.5% for EW motions.