摘要
在混凝土温度场计算中,热力学参数一般由室内试验或经验公式获得,往往与现场实际参数出入较大。为此通过测量埋设于混凝土中不同温度测点的温度,采用粒子群算法优化初始权值和阈值的小波神经网络对混凝土的热力学参数进行反演,以控温防裂。通过对某水闸工程底板混凝土的热学参数的反演分析,验证了小波神经网络反演计算的可行性。
When calculation of the temperature field of concrete, the thermodynamic parameters are generally ob-tained from experiment or empirical formula, which is often different from the actual parameters. Based on measuring thetemperature observation points buried in different parts, wavelet neural network optimized by PSO was carried out in theback analysis of thermodynamic parameters to control temperature and prevent cracks. Through the back analysis of ther-mal parameters of a sluice concrete floor, it verifies the feasibility of wavelet neural network for back analysis.
出处
《水电能源科学》
北大核心
2014年第12期107-109,113,共4页
Water Resources and Power
关键词
小波神经网络
混凝土
热力学参数
反演
wavelet neural network
concrete
thermodynamic parameter
back analysis
