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Based on Neural Network Cantilever Retaining Wall Modal Parameter Identification

Author: LiZuo
Tutor: ChenJianGong；LiuXinRong
School: Chongqing University
Course: Geotechnical Engineering
Keywords: modal parameters detection neural network dynamic response signal processing
CLC: TU476.4
Type: Master's thesis
Year: 2009
Downloads: 61
Quote: 0
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Abstract


Many civil infrastructures are now deteriorating due to aging, misuse, lacking proper maintenance, and, in some cases, overstressing as a result of increasing load demands and changing environments. Failure of these infrastructures often leads to a high social consequence. It is therefore critical to evaluate their current reliability, performance, and condition for the prevention of potential catastrophic events. Structural identification has become an increasingly important research topic for health monitoring, performance assessment and safety evaluation of engmeermg structures.Modal parameters can be intuitive and accurately reflect the dynamic characteristics of the system with simple, intuitive and physical advantages of the concept of a clear, which often used for Dynamic Analysis now.because of the ability to approximate arbitrary continuous functions, neural networks have drawn considerable attention in civil engineering for identification in a non-parametric manner. However, because of the nonparametric characteristics, most of the proposed methods for structural health monitoring and damage detection with neural networks can just be used to give a qualitative indication or information that damage might be present in the structure, no quantitative identification can be determined. A high accurate algorithm is presented for identification of complex modal parameters in structural system, which is based on FFT and neural network. A characteristic of the method is that adjustable parameter base function is adopted by neural network. First, the sampled signal is processed with FFT algorithm. By this algorithm, the estimate values of frequency and phase, and orders of modal are obtained. Second, applying results analyzed with FFT, the number of neural nodes is established according to the orders of modal, the initial weights of neural network and the iterative initial parameters of base function are assigned according to the estimate values of frequency and phase. Finally, by training artificial neural network, the complex modal parameters can be identified precisely. The method can get high accuracy and eliminate the influence of frequency spectrum leakage and ground noise in frequency-domain methods.Firstly, a comprehensive review on the traditional damage detection techniques, vibration-based global identification methodologies, and the application of artificial neural network in civil engineering are made. Secondly, a novel two neural networks based structural parameters identification methodology with the direct use structural acceleration time series is proposed and validated by a shaking table test a model structure. The results show that the neural network used in time-domain modal parameter identification method can reliably run and Prospects for practical applications.

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CLC: > Industrial Technology > Building Science > Soil mechanics,foundation engineering > Foundation > The basis of the special form > Retaining wall
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