The Selection of Pattern Features for Structural Damage Detection Using an Extended Bayesian ANN Algorithm

Pattern recognition is a promising approach for the detection of structural damage using measured dynamic data. Much research of pattern recognition has employed artificial neural networks (ANNs) as a systematic way of matching pattern features. When such methods are used, the ANN design becomes the most fundamental factor affecting performance and effectiveness of the pattern recognition process. The Bayesian ANN design algorithm is proposed in Lam et al. [Lam HF, Yuen KV, Beck JL. Structural health monitoring via measured Ritz vectors utilizing artificial neural networks. Computer-Aided Civil and Infrastructure Engineering 2006;21:232-41] provides a mathematically rigorous way of determining the number of hidden neurons for a single-hidden-layer feedforward ANN. The first objective of this paper is to extend this Bayesian ANN design algorithm to cover the selection of activation (transfer) functions for neurons in the hidden layer. The proposed algorithm is found to be computationally efficient and is suitable for real-time design of an ANN. As most existing ANN design techniques require the ANN model class to be known before the training process, a technique that can automatically select an "optimal" ANN model class is essential. As modal parameters and Ritz vectors are commonly used pattern features in the literature, the second objective of this paper is to compare the performance of these two pattern features in structural damage detection using pattern recognition. To make a fair judgment between the features, the IASC-ASCE benchmark structure is employed in a case study. The results show that the performance of ANNs trained by modal parameters is slightly better than that of ANNs trained by Ritz vectors. © 2008 Elsevier Ltd. All rights reserved.Heung Fai Lam, Ching Tai N

Damage classification and estimation in experimental structures using time series analysis and pattern recognition

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Structural Health Monitoring of a Reinforced Concrete Building during the Severe Typhoon Vicente in 2012

The goal of this study is to investigate the structural performance of reinforced concrete building under the influence of severe typhoon. For this purpose, full-scale monitoring of a 22-story reinforced concrete building was conducted during the entire passage process of a severe typhoon “Vicente.” Vicente was the eighth tropical storm developed in the Western North Pacific Ocean and the South China Sea in 2012. Moreover, it was the strongest and most devastating typhoon that struck Macao since 1999. The overall duration of the typhoon affected period that lasted more than 70 hours and the typhoon eye region covered Macao for around one hour. The wind and structural response measurements were acquired throughout the entire typhoon affected period. The wind characteristics were analyzed using the measured wind data including the wind speed and wind direction time histories. Besides, the structural response measurements of the monitored building were utilized for modal identification using the Bayesian spectral density approach. Detailed analysis of the field data and the typhoon generated effects on the structural performance are discussed

Model selection for dynamic reduction-based structural health monitoring following the Bayesian evidence approach

© 2019 Elsevier Ltd There usually exist multitudinous finite element (FE) models with varying level of complexity which can be developed from the engineering judgment for the purpose of structural system identification and health monitoring. By following the theory of Bayesian evidence statistic, this paper proposes a methodology to investigate the issues of FE model-class selection for choosing suitable parameterized structural models utilized in dynamic reduction-based structural health monitoring (SHM). By employing the concept of information divergence, the amount of information needed to be extracted from the measured data is explicitly quantified during the procedure of FE model updating-based structural health monitoring. Then, for achieving a trade-off between the complexity of a parameterized FE model class and that of its corresponding information-theoretic interpretation, such information is utilized for penalizing the complexity of model class to ensure that a relatively simple parameterization scheme can be achieved. The proposed methodology consists of calibration and subsequent monitoring stages, and the information obtained in the former stage is utilized as pseudo-data which is learned by the latter stage to improve the model parameter estimation by implementing the delayed rejection adaptive Metropolis algorithm. Through numerical case studies conducted for a four-storey two-bay steel frame structure considering semi-rigid connections as well as laboratory experiment performed for a two-storey bolt-connected steel frame model, the feasibility and validity of proposed methodology is demonstrated

Structural Health Monitoring via Measured Ritz Vectors utilizing Artificial Neural Networks

A pattern recognition approach for structural health monitoring (SHM) is presented that uses damage-induced changes in Ritz vectors as the features to characterize the damage patterns defined by the corresponding locations and severity of damage. Unlike most other pattern recognition methods, an artificial neural network (ANN) technique is employed as a tool for systematically identifying the damage pattern corresponding to an observed feature. An important aspect of using an ANN is its design but this is usually skipped in the literature on ANN-based SHM. The design of an ANN has significant effects on both the training and performance of the ANN. As the multi-layer perceptron ANN model is adopted in this work, ANN design refers to the selection of the number of hidden layers and the number of neurons in each hidden layer. A design method based on a Bayesian probabilistic approach for model selection is proposed. The combination of the pattern recognition method and the Bayesian ANN design method forms a practical SHM methodology. A truss model is employed to demonstrate the proposed methodology

Monitoring the Health of Plates with Simultaneous Application of Lamb Waves and Surface Response to Excitation Approaches

Structural Health Monitoring (SHM) is a process of implementing a damage identification procedure for mechanical, aerospace and civil engineering infrastructure. Any change in the geometric properties, boundary conditions and behavior of material is defined as damage of these systems. In the past 10 years, there has been an accelerated increase in the amount of research related to SHM [1]. Hence, the increased interest in SHM to a wide range of industries and its correlated capability for significant life-safety and economic benefits has motivated the need for this thesis topic. The objective of this thesis study was to explore SHM approach to monitor and detect a change and/or damage in plates using Lamb wave propagation and surface response to excitation. First, the endurance of sensors and the adhesive used was evaluated. Next, the experimental data from the prepared samples was collected, compared, and evaluated. The obtained results indicated the severity and location of the defects

Bayesian Learning for Earthquake Engineering Applications and Structural Health Monitoring

Parallel to significant advances in sensor hardware, there have been recent developments of sophisticated methods for quantitative assessment of measured data that explicitly deal with all of the involved uncertainties, including inevitable measurement errors. The existence of these uncertainties often causes numerical instabilities in inverse problems that make them ill-conditioned. The Bayesian methodology is known to provide an efficient way to alleviate this illconditioning by incorporating the prior term for regularization of the inverse problem, and to provide probabilistic results which are meaningful for decision making. In this work, the Bayesian methodology is applied to inverse problems in earthquake engineering and especially to structural health monitoring. The proposed methodology of Bayesian learning using automatic relevance determination (ARD) prior, including its kernel version called the Relevance Vector Machine, is presented and applied to earthquake early warning, earthquake ground motion attenuation estimation, and structural health monitoring, using either a Bayesian classification or regression approach. The classification and regression are both performed in three phases: (1) Phase I (feature extraction phase): Determine which features from the data to use in a training dataset; (2) Phase II (training phase): Identify the unknown parameters defining a model by using a training dataset; and (3) Phase III (prediction phase): Predict the results based on the features from new data. This work focuses on the advantages of making probabilistic predictions obtained by Bayesian methods to deal with all uncertainties and the good characteristics of the proposed method in terms of computationally efficient training, and, especially, vi prediction that make it suitable for real-time operation. It is shown that sparseness (using only smaller number of basis function terms) is produced in the regression equations and classification separating boundary by using the ARD prior along with Bayesian model class selection to select the most probable (plausible) model class based on the data. This model class selection procedure automatically produces optimal regularization of the problem at hand, making it well-conditioned. Several applications of the proposed Bayesian learning methodology are presented. First, automatic near-source and far-source classification of incoming ground motion signals is treated and the Bayesian learning method is used to determine which ground motion features are optimal for this classification. Second, a probabilistic earthquake attenuation model for peak ground acceleration is identified using selected optimal features, especially taking a non-linearly involved parameter into consideration. It is shown that the Bayesian learning method can be utilized to estimate not only linear coefficients but also a non-linearly involved parameter to provide an estimate for an unknown parameter in the kernel basis functions for Relevance Vector Machine. Third, the proposed method is extended to a general case of regression problems with vector outputs and applied to structural health monitoring applications. It is concluded that the proposed vector output RVM shows promise for estimating damage locations and their severities from change of modal properties such as natural frequencies and mode shapes

Application of Stochastic Simulation Methods to System Identification

Reliable predictive models for the response of structures are a necessity for many branches of earthquake engineering, such as design, structural control, and structural health monitoring. However, the process of choosing an appropriate class of models to describe a system, known as model-class selection, and identifying the specific predictive model based on available data, known as system identification, is difficult. Variability in material properties, complex constitutive behavior, uncertainty in the excitations caused by earthquakes, and limited constraining information (relatively few channels of data, compared to the number of parameters needed for a useful predictive model) make system identification an ill-conditioned problem. In addition, model-class selection is not trivial, as it involves balancing predictive power with simplicity. These problems of system identification and model-class selection may be addressed using a Bayesian probabilistic framework that provides a rational, transparent method for combining prior knowledge of a system with measured data and for choosing between competing model classes. The probabilistic framework also allows for explicit quantification of the uncertainties associated with modeling a system. The essential idea is to use probability logic and Bayes' Theorem to give a measure of plausibility for a model or class of models that is updated with available data. Similar approaches have been used in the field of system identification, but many currently used methods for Bayesian updating focus on the model defined by the set of most plausible parameter values. The challenge for these approaches (referred to as asymptotic-approximation-based methods) is when one must deal with ill-conditioned problems, where there may be many models with high plausibility, rather than a single v dominant model. It is demonstrated here that ill-conditioned problems in system identification and model-class selection can be effectively addressed using stochastic simulation methods. This work focuses on the application of stochastic simulation to updating and comparing model classes in problems of: (1) development of empirical ground motion attenuation relations, (2) structural model updating using incomplete modal data for the purposes of structural health monitoring, and (3) identification of hysteretic structural models, including degrading models, from seismic structural response. The results for system identification and model-class selection in this work fall into three categories. First, in cases where the existing asymptotic approximation-based methods are appropriate (i.e., well-conditioned problems with one highest-plausibility model), the results obtained using stochastic simulation show good agreement with results from asymptotic-approximation-based methods. Second, for cases involving ill-conditioned problems based on simulated data, stochastic simulation methods are successfully applied to obtain results in a situation where the use of asymptotics is not feasible (specfically, the identification of hysteretic models). Third, preliminary studies using stochastic simulation to identify a deteriorating hysteretic model with relatively sparse real data from a structure damaged in the 1994 Northridge earthquake show that the high-plausibility models demonstrate behavior consistent with the observed damage, indicating that there is promise in applying these methods to ill-conditioned problems in the real world