混凝土局部受压强度的人工神经网络仿真

简述了混凝土局部受压区域在高应力状态下的随机性和非线性特点，尤其是预应力锚具区这一复杂应力区域的受压强度评估的不确定性．引入4个试验系列，考虑垫板刚度、受压面积比和横向钢筋等因素的作用，将这4个试验系列的结果作为网络的训练样本和验证样本，经过合理的网络选择和训练，对受压强度进行预测并获得了满意的精度．应用实例表明，人工神经网络可作为预测混凝土局部受压强度的方法之一．

Simulation of Artificial Neural Networks for Bearing Strength of Concrete Subject to Local Compression

The nonlinear behavior and stochastic features for concrete in an anchorage zone or called an end block subject to local compression loading and under complex stress state are briefly reviewed. Results of experimental study for 4 groups of specimens are introduced. Parameters, such as the stiffness of the end plates, the ratio of the compression areas, and the reinforcement effects are considered. Theses results are used as learning samples for training an artificial neural network(ANN). By selecting network architecture with sufficient training and testing, an ANN model with necessary accuracy for predicting the bearing strength of concrete is obtained. An application example shows that ANN can be used as one of approaches for predicting bearing strength of concrete subject to local compression.