%0 Journal Article
%A CAO Yan-ming
%A JING De-quan
%A LIU Chun-gao
%T Predicting Arch Dam Displacement Using Twin Support Vector Machine Optimized by Artificial Immune Algorithm
%D 2019
%R 10.11988/ckyyb.20180639
%J Journal of Yangtze River Scientific Research Institute
%P 54-58
%V 36
%N 12
%X In this paper the twin support vector machine optimized by immune algorithm is proposed to analyze the displacement of arch dam and predict the deformation of the dam. Compared with support vector machine, twin support vector machine greatly improves the calculation speed and the calculation efficiency in batch repetitive calculation. Considering the effect of parameters on the fitting results, the artificial immune algorithm is incorporated in the twin support vector machine to optimize the parameters. Artificial immune algorithm retains a certain number of better solutions based on the genetic algorithm and improves the search efficiency of the algorithm. Engineering example analysis shows that parameters have great influence on the results of the twin support vector machine. After searching the optimal parameters by using artificial immune algorithm, the twin support vector machine can better fit the deformation data of the dam. The prediction result meets requirements, and the maximum error is merely 1 mm.
%U http://ckyyb.crsri.cn/EN/10.11988/ckyyb.20180639