A feasibility study on the use of bayesian model updating and vibration prediction for structural diagnostic

This paper puts forward a feasibility study on the use of Bayesian model updating and vibration prediction for structural diagnostic when the level of modeling error is relatively high. The proposed method consists of two parts. In the first part, the Markov chain Monte Carlo (MCMC)-based model updating is employed to calculate the posterior PDF of uncertain model parameters conditional a set of measurement and a given model class. Modeling error is the key problem to be addressed in the practical implementation of structural model updating or damage detection. Apart from very simple structures, model updating of real structures is usually not globally and locally identifiable. Therefore, MCMC simulation is employed in the proposed method in generating samples in the important region(s) for the approximation of the posterior PDF. In the second part, the probabilities for the vibrations of the structure to exceed a list of threshold limits (i.e., the failure probabilities) were calculated using the MCMC samples. It is believed that the failure probabilities for the damaged structure are higher than those for the undamaged one. A 3-dimensional scaled transmission tower model was tested under laboratory conditions for verifying the proposed method. To test the robustness in the detection of damage existence, artificial modelling error was introduced to the model class in the numerical case study. The numerical case study results were positive implying the feasibility of the proposed method

Multicrack detection on semirigidly connected beams utilizing dynamic data

The problem of crack detection has been studied by many researchers, and many methods of approaching the problem have been developed. To quantify the crack extent, most methods follow the model updating approach. This approach treats the crack location and extent as model parameters, which are then identified by minimizing the discrepancy between the modeled and the measured dynamic responses. Most methods following this approach focus on the detection of a single crack or multicracks in situations in which the number of cracks is known. The main objective of this paper is to address the crack detection problem in a general situation in which the number of cracks is not known in advance. The crack detection methodology proposed in this paper consists of two phases. In the first phase, different classes of models are employed to model the beam with different numbers of cracks, and the Bayesian model class selection method is then employed to identify the most plausible class of models based on the set of measured dynamic data in order to identify the number of cracks on the beam. In the second phase, the posterior (updated) probability density function of the crack locations and the corresponding extents is calculated using the Bayesian statistical framework. As a result, the uncertainties that may have been introduced by measurement noise and modeling error can be explicitly dealt with. The methodology proposed herein has been verified by and demonstrated through a comprehensive series of numerical case studies, in which noisy data were generated by a Bernoulli-Euler beam with semirigid connections. The results of these studies show that the proposed methodology can correctly identify the number of cracks even when the crack extent is small. The effects of measurement noise, modeling error, and the complexity of the class of identification model on the crack detection results have also been studied and are discussed in this paper. © 2008 ASCE.Heung Fai Lam, Ching Tai Ng and Andrew Yee Tak Leun

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

Structural model updating and health monitoring in the presence of modeling uncertainties

This thesis addresses the problem of updating a structural model and its associated uncertainties by utilizing measured dynamic data following a Bayesian probabilistic framework. The nonuniqueness problem arising from model updating, which has been worrying researchers in this area for a long time, is directly addressed in this thesis. Model updating problems can be classified into two categories: "Identifiable" or "Unidentifiable" problems. In an identifiable case, the posterior probability density function (PDF) of the uncertain model parameters for given measured data can be approximated by a weighted sum of Gaussian distributions centered at a number of discrete optimal values of the parameters at which some positive measure-of-fit function is minimized. The focus of this thesis is on the treatment of the general unidentifiable case where the earlier approximations are not applicable. In this case the posterior PDF of the parameters is found to be concentrated in the neighborhood of an extended and extremely complex manifold in the parameter space. The computational difficulties associated with calculating the posterior PDF in such a case are discussed and a number of algorithms for efficient approximate representation of the above manifold and the posterior PDF are presented. A structural health monitoring method is developed based on the proposed model up-dating methodology. Both the proposed model updating and health monitoring methods are demonstrated and verified by numerically simulated and experimental dynamic data

PHASE Ile OF THE IASC-ASCE BENCHMARK STUDY ON STRUCTURAL HEALTH MONITORING

This paper describes details of the extended version of Phase II, denoted by Phase Ile, of the joint IASC-ASCE benchmark study on structural health monitoring (SHM). A brief review on Phases I and II of the benchmark study is first given. It is then followed by the rationale and definition of Phase Ile. One of the main objectives of this paper is to present the simulated damage patterns for the two blind tests in Phase Ile. This paper also details the modeling of the benchmark structure, the data generation program (datagen2e.p), and the program downloading website (http://www.ce.ust.hk/asce.shm)