重庆大学学报2025，Vol.48Issue(1)：66-75,10.DOI:10.11835/j.issn.1000-582X.2024.051
基于卷积神经网络的高层建筑智能控制算法研究
Research on intelligent structural control algorithm for high-rise buildings based on one-dimensional convolution neural network
摘要
Abstract
Traditional shallow neural networks exhibit low prediction accuracy and poor generalization when handling high-dimensional data.To solve these problems,this study proposes an intelligent control algorithm for high-rise buildings based on one-dimensional convolutional neural networks(1D-CNN)and the deep dream visualization algorithm.The proposed method enables high-precision network model training and visualizes data features through 1D-CNN.Using a 20-story benchmark model as a case study,the damping performance of the 1D-CNN-based intelligent control algorithm was analyzed under different conditions and compared with back propagation(BP)and radial basis function(RBF)algorithms.Results show that 1D-CNN can effectively extract deep data features and reduce the dimensionality of massive datasets by virtue of one-dimensional convolution and pooling operations.Under external excitation,the maximum damping rates for acceleration and displacement achieved by the 1D CNN controller were 69.0％and 55.6％respectively,significantly outperforming BP and RBF.Although the control performance of all algorithms decreased under modified excitation conditions,the 1D-CNN consistently exhibited superior performance and the best generalization capability.关键词
深度学习/一维卷积神经网络/智能控制/数据特征可视化/泛化性能Key words
deep learning/1D-CNN/intelligent control/data feature visualization/generalization performance引用本文复制引用
刘康生,涂建维,张家瑞,李召..基于卷积神经网络的高层建筑智能控制算法研究[J].重庆大学学报,2025,48(1):66-75,10.基金项目
国家自然科学基金资助项目(51978550) （51978550）
国家重点研发计划资助项目(2018YFC0705601) （2018YFC0705601）
中央高校基本科研业务费专项资金资助项目(2019-YB-024).Supported by National Natural Science Foundation of China(51978550),Key Research Plan of Ministry of Science and Technology(2018YFC0705601),Fundamental Research Funds for the Central Universities(2019-YB-024). （2019-YB-024）
