Particle relaxation method for structural parameters identification based on Monte Carlo Filter

Sato, Tadanobu,Tanaka, Youhei Techno-Press 2013 Smart Structures and Systems, An International Jou Vol.11 No.1

In this paper we apply Monte Carlo Filter to identifying dynamic parameters of structural systems and improve the efficiency of this algorithm. The algorithms using Monte Carlo Filter so far has not been practical to apply to structural identification for large scale structural systems because computation time increases exponentially as the degrees of freedom of the system increase. To overcome this problem, we developed a method being able to reduce number of particles which express possible structural response state vector. In MCF there are two steps which are the prediction and filtering processes. The idea is very simple. The prediction process remains intact but the filtering process is conducted at each node of structural system in the proposed method. We named this algorithm as relaxation Monte Carlo Filter (RMCF) and demonstrate its efficiency to identify large degree of freedom systems. Moreover to increase searching field and speed up convergence time of structural parameters we proposed an algorithm combining the Genetic Algorithm with RMCF and named GARMCF. Using shaking table test data of a model structure we also demonstrate the efficiency of proposed algorithm.

Particle relaxation method for structural parameters identification based on Monte Carlo Filter

Tadanobu Sato,Youhei Tanaka 국제구조공학회 2013 Smart Structures and Systems, An International Jou Vol.11 No.1

In this paper we apply Monte Carlo Filter to identifying dynamic parameters of structural systems and improve the efficiency of this algorithm. The algorithms using Monte Carlo Filter so far has not been practical to apply to structural identification for large scale structural systems because computation time increases exponentially as the degrees of freedom of the system increase. To overcome this problem, we developed a method being able to reduce number of particles which express possible structural response state vector. In MCF there are two steps which are the prediction and filtering processes. The idea is very simple. The prediction process remains intact but the filtering process is conducted at each node of structural system in the proposed method. We named this algorithm as relaxation Monte Carlo Filter (RMCF) and demonstrate its efficiency to identify large degree of freedom systems. Moreover to increase searching field and speed up convergence time of structural parameters we proposed an algorithm combining the Genetic Algorithm with RMCF and named GARMCF. Using shaking table test data of a model structure we also demonstrate the efficiency of proposed algorithm.

A Phase Model of Earthquake Motions based on Stochastic Differential Equation

Cong Zhang,Tadanobu Sato,Lingyi Lu 대한토목학회 2011 KSCE JOURNAL OF CIVIL ENGINEERING Vol.15 No.1

In this paper, a method is proposed to simulate Group Delay Time (GDT) of earthquake ground motion by using Stochastic Differential Equation (SDE). The random characteristic of GDT is expressed by a stochastic differential equation whose mean and variance processes are defined by ordinary differential equations. An algorithm is developed to identify the coefficients of the ordinary differential equations. Regression surfaces of the coefficients are developed as functions of earthquake magnitude,epicentral distance and frequency. The Milstein approximation scheme is used to solve the stochastic differential equation of GDT. The efficiency of the developed method is demonstrated by comparing the simulated result with the original one.

Damage identification using chaotic excitation

Chunfeng Wan,Tadanobu Sato,Zhishen Wu,Jian Zhang 국제구조공학회 2013 Smart Structures and Systems, An International Jou Vol.11 No.1

Vibration-based damage detection methods are popular for structural health monitoring. However, they can only detect fairly large damages. Usually impact pulse, ambient vibrations and sine-wave forces are applied as the excitations. In this paper, we propose the method to use the chaotic excitation to vibrate structures. The attractors built from the output responses are used for the minor damage detection. After the damage is detected, it is further quantified using the Kalman Filter. Simulations are conducted. A 5-story building is subjected to chaotic excitation. The structural responses and related attractors are analyzed. The results show that the attractor distances increase monotonously with the increase of the damage degree. Therefore, damages, including minor damages, can be effectively detected using the proposed approach. With the Kalman Filter, damage which has the stiffness decrease of about 5% or lower can be quantified. The proposed approach will be helpful for detecting and evaluating minor damages at the early stage.

Structural Identification using Stochastic Filtering Techniques Based on Measurements from Wireless Data Acquisition System

Myungjin Chung,Tadanobu Sato 한국강구조학회 2006 International Journal of Steel Structures Vol.6 No.5

identification using stochastic filtering techniques. The wireless data acquisition system is possible to transmit the digital signalsof the observed structural responses. The validity of the developed structural identification instrument is verified by conductingshaking table test of a model structure. The wireles data acquisition system is set up to a five stories model building and theabsolute acceleration at each story is measured. The Kalman filter and Monte Carlo filter techniques are applied to identifyidentifications of system parameters obtained from both techniques for the model structure, the convergences of identificationresults from Monte Carlo filter are beter than those from Kalman filter.

Damage identification using chaotic excitation

Wan, Chunfeng,Sato, Tadanobu,Wu, Zhishen,Zhang, Jian Techno-Press 2013 Smart Structures and Systems, An International Jou Vol.11 No.1

Vibration-based damage detection methods are popular for structural health monitoring. However, they can only detect fairly large damages. Usually impact pulse, ambient vibrations and sine-wave forces are applied as the excitations. In this paper, we propose the method to use the chaotic excitation to vibrate structures. The attractors built from the output responses are used for the minor damage detection. After the damage is detected, it is further quantified using the Kalman Filter. Simulations are conducted. A 5-story building is subjected to chaotic excitation. The structural responses and related attractors are analyzed. The results show that the attractor distances increase monotonously with the increase of the damage degree. Therefore, damages, including minor damages, can be effectively detected using the proposed approach. With the Kalman Filter, damage which has the stiffness decrease of about 5% or lower can be quantified. The proposed approach will be helpful for detecting and evaluating minor damages at the early stage.

Simulation of Earthquake Motion Phase considering Its Fractal and Auto-covariance Features

Adam A. Abdelrahman,Tadanobu Sato,Chunfeng Wan,Lei Zhao 대한토목학회 2019 KSCE Journal of Civil Engineering Vol.23 No.9

The earthquake motion phase (EMP) is decomposed into linear delay and fluctuation parts. In this paper, the peculiar stochastic characteristics of the fluctuation part of the phase (FPP) are discussed. First, we show that the FPP has self-affine similarity and should be expressed as a fractal stochastic process by using several observed earthquake motion time histories, as well as the FPP has a long term memory in the frequency domain. Moreover, the possibility of simulating FPP using the simple fractional Brownian motion (fBm) is discussed and conclude that this is not possible. To overcome this problem, we develop a new stochastic process, the modified fBm that is able to simulate a stochastically rigorous sample FPP. This newly developed algorithm represents the phase characteristics of the observed EMP well.