Browsing by Author "Wen, CM"

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A neural network approach for structural identification and diagnosis of a building from seismic response data
(2003-02-01)
Huang, CS
;
Hung, SL
;
Wen, CM
;
Tu, TT
;
土木工程學系
This work presents a novel procedure for identifying the dynamic characteristics of a building and diagnosing whether the building has been damaged by earthquakes, using a back-propagation neural network approach. The dynamic characteristics are directly evaluated from the freighting matrices of the neural network trained by observed acceleration responses and input base excitations. Whether the building is damaged under a large earthquake is assessed by comparing the modal parameters and responses for this large earthquake with those for a small earthquake that has not Caused this building any damage. The feasibility of the approach is demonstrated through processing the dynamic responses of a five-storey steel frame. subjected to different strengths of the Kobe earthquake, in shaking table tests. Copyright (C) 2002 John Wiley Sons, Ltd.
Damage detection of structures using unsupervised fuzzy neural network
(2005)
Wen, CM
;
Hung, SL
;
Huang, CS
;
Jan, JC
;
土木工程學系
This work presents an artificial neural network (ANN) approach for detecting structural damage. An unsupervised neural network which incorporates the fuzzy concept (named the Unsupervised Fuzzy Neural Network, UFN) is adopted to detect localized damage. The structural damage is assumed to take the form of reduced elemental stiffness. The damage site is demonstrated to correlate with the changes in the modal parameters of the structure. Therefore, a feature representing the damage location termed the Damage Localization Feature (DLF) is presented. When the structure experiences damage or change in the structural member, the measured DLF is obtained by analyzing the recorded dynamic responses of the structure. The location of the structural damage then can be identified using the UFN according to the measured DLF information. This study verifies the proposed model using an example involving a five-storey frame building. Both single and multiple damaged sites are considered. The effects of measured noise and the use of incomplete modal data are introduced to inspect the capability of the proposed detection approach.
Nonparametric identification of a building structure from experimental data using wavelet neural network
(2003-09-01)
Hung, SL
;
Huang, CS
;
Wen, CM
;
Hsu, YC
;
土木工程學系
This study presents a wavelet neural network-based approach to dynamically identifying and modeling a building structure. By combining wavelet decomposition and artificial neural networks (ANN), wavelet neural networks (WNN) are used for solving chaotic signal processing. The basic operations and training method of wavelet neural networks are briefly introduced, since these networks can approximate universal functions. The feasibility of structural behavior modeling and the possibility of structural health monitoring using wavelet neural networks are investigated. The practical application of a wavelet neural network to the structural dynamic modeling of a building frame in shaking tests is considered in an example. Structural acceleration responses under various levels of the strength of the Kobe earthquake were used to train and then test the WNNs. The results reveal that the WNNs not only identify the structural dynamic model, but also can be applied to monitor the health condition of a building structure under strong external excitation.
Purification, characterization and cloning of a chitinase from Bacillus sp NCTU2
(2002-06-01)
Wen, CM
;
Tseng, CS
;
Cheng, CY
;
Li, YK
;
應用化學系
A chitin-degrading Bacillus strain, designated as NCTU2, was screened from soil and identified. An extracellular chitinase was purified to > 90% homogeneity from the culture filtrate. The purification involved hydrophobic-interaction and gel-filtration chromatographic separations with a yield of 58%. The purified enzyme (ChiNCTU2) is a monomeric protein with an estimated molecular mass of 36.5 kDa and a pI of 6.3. It is thermally stable at 60 degreesC and pH 6-8 for more than 3 h. The optimal activity is in the range of 50-60 degreesC at pH 7.0. Chitobiose is the predominant product throughout the enzymic hydrolysis of the colloidal chitin, indicating that the purified chitinase is an exo-chitinase. Chito-oligosaccharides [with degree of polymerization (DP) values of 4-6] are good substrates of the purified enzyme, whereas a DP3 oligomer was slowly hydrolysed to form DP1 and DP2 sugars. The first 15 N-terminal amino acids of the enzyme were determined to be ANNLGSKLLVGYWHN, which is highly homologous to that of ChiA from Bacillus cereus. A PCR cloning technique was employed to obtain the corresponding gene from Bacillus NCTU2. The gene sequence was determined to be 1080 bp, encoding a polypeptide of 360 amino acids with the first 27 amino acids as the signal peptide.