基于改进遗传算法的粘弹性岩体力学参数反演

详细信息

Back analysis of viscoelastic rock mass parameters based on improved genetic algorithm

1. Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China;
2. Residential Development Company, Jiaozuo Construction Bureau, Jiaozuo 454000, China

摘要: 把模式搜索嵌入目前广为应用的遗传算法中,使之和神经网络有机结合,提出了搜索—遗传—神经网络算法。该方法用经过最佳预测学习算法训练的神经网络来表达粘弹性岩体力学参数和位移之间的映射关系,除具有一般遗传算法的优点外,还提高了参数反演的精度,节省了参数反演的计算时间。结合某工程实例,验证了该方法在粘弹性岩体力学参数反演中的优越性。
- 遗传算法 /
- 粘弹性 /
- 岩体参数 /
- 反分析
Abstract: A back analysis method of viscoelastic rock mass is proposed in this paper,in which,pattern search(PS),genetic algorithm(GA),and neural network(NN) are naturally combined.The method that has been trained by optimal prediction algorithm is used to describe relationship between the rock mass viscoelastic parameters and displacement and expected to improve the precision of back analysis on parameters,short the time of calculation.The practical engineering example shows feasibility and advantages of this method.
- genetic algorithm /
- viscoelastic /
- rock mass parameters /
- back analysis