基于混沌理论的混凝土裂缝开合度改进混合预测模型

针对传统混凝土裂缝开合度统计模型对温度非线性因素和残差特性考虑不足导致预测效果不佳的问题，首先考虑温度非线性因素，建立裂缝开合度改进统计模型，进而借助混沌理论与相空间重构理论对改进统计模型的残差时间序列进行混沌特性分析与相空间重构，采用遗传算法优化的BP人工神经网络对残差进行预测，最后集成获得改进混合预测模型对混凝土裂缝开合度进行预测。结合某泄水闸检修门库裂缝实测开合度，对比分析了传统统计模型、改进统计模型和改进混合预测模型的预测效果。结果表明：改进混合预测模型的预测误差更小，能有效改善裂缝开合度的预测效果。

An improved hybrid prediction model for concrete crack opening based on chaos theory

In view of the poor prediction performance of conventional statistical models of concrete crack opening due to the insufficient consideration of temperature nonlinear factors and residual errors, an improved statistical model for the prediction of concrete crack opening was established incorporated with the temperature nonlinear factors. Then the residual error time series of the model was analyzed using chaos theory and its phase-space was reconstructed, meanwhile the residual error was predicted using BP artificial neural network optimized by genetic algorithm. Finally, an improved hybrid prediction model was established to predict concrete crack opening. Based on the monitored crack opening data of a drainage gate reservoir, the prediction results of the conventional statistical model, the improved statistical model and the improved hybrid prediction model were compared and analyzed. The result shows that the improved hybrid prediction model has smaller prediction error and can effectively improve the prediction accuracy.