TY - JOUR
T1 - Damping in buildings
T2 - Its neural network model and AR model
AU - Li, Q. S.
AU - Liu, D. K.
AU - Fang, J. Q.
AU - Jeary, A. P.
AU - Wong, C. K.
PY - 2000/9
Y1 - 2000/9
N2 - The results of full scale measurements of damping as well as other researches on damping show that damping in buildings exhibits randomness and amplitude dependent behaviour in the case of tall buildings subjected to dynamic loading. In this paper, based on full scale measurements of damping in a tall building, a time series analysis method (TSA) is employed to obtain the relationship between damping and vibration amplitude. Then, two models of damping in a tall building, the artificial neural network (ANN) model and the auto-regressive (AR) model, are established by employing ANN and AR methods, and used to predict the damping values at high amplitude level, which are difficult to obtain from field measurements. In order to get high accuracy, a genetic algorithm strategy is employed to aid in training the ANN. Comparison analysis of the neural network model and the AR model of damping is made, and the results are presented and discussed. (C) 2000 Elsevier Science Ltd. All rights reserved.
AB - The results of full scale measurements of damping as well as other researches on damping show that damping in buildings exhibits randomness and amplitude dependent behaviour in the case of tall buildings subjected to dynamic loading. In this paper, based on full scale measurements of damping in a tall building, a time series analysis method (TSA) is employed to obtain the relationship between damping and vibration amplitude. Then, two models of damping in a tall building, the artificial neural network (ANN) model and the auto-regressive (AR) model, are established by employing ANN and AR methods, and used to predict the damping values at high amplitude level, which are difficult to obtain from field measurements. In order to get high accuracy, a genetic algorithm strategy is employed to aid in training the ANN. Comparison analysis of the neural network model and the AR model of damping is made, and the results are presented and discussed. (C) 2000 Elsevier Science Ltd. All rights reserved.
KW - AR model
KW - Artificial neural networks
KW - Damping
KW - Full-scale measurements
KW - Genetic algorithm
KW - Tall buildings
UR - http://www.scopus.com/inward/record.url?scp=0034075622&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0034075622&origin=recordpage
U2 - 10.1016/S0141-0296(99)00050-4
DO - 10.1016/S0141-0296(99)00050-4
M3 - RGC 21 - Publication in refereed journal
SN - 0141-0296
VL - 22
SP - 1216
EP - 1223
JO - Engineering Structures
JF - Engineering Structures
IS - 9
ER -