基于灰狼优化算法的混凝土坝变形监控模型

Monitoring Model of Concrete Dam Deformation Based on Gray Wolf Optimization Algorithm

大坝变形是能够体现混凝土坝实际状况最为可靠的指标之一,通过对大坝变形监测资料进行建模分析是反映混凝土坝运行性态、寻求变形内在规律建立的有效途径,并可运用数学模型对变形进行预测预报。为提高模型建立和变形预测的准确性,提出一种基于灰狼优化算法的混凝土坝变形监控模型,并通过工程实例分析,与常规逐步回归方法建立统计模型和BP神经网络结果进行对比,基于灰狼优化算法建立的监控模型精度更高、预测效果更佳,体现出灰狼算法在建立变形统计模型中求解精度和稳定性上优越性,为混凝土大坝变形分析和预测提供了新借鉴方法。

Dam deformation is one of the most reliable indicators that can reflect the actual condition of concrete dam.Modeling and analysis of dam deformation monitoring data is an effective way to reflect the operation behavior of concrete dam and seek for the internal law of deformation.In addition,the mathematical model can be applied to predict the deformation.In order to improve the accuracy of modeling and deformation prediction,this paper proposes a deformation monitoring model of concrete dam based on gray wolf optimization algorithm,applies the model to the engineering examples and compares the results with that from the statistical model established by the conventional stepwise regression method and the BP-neural network.The results show that the monitoring model based on the gray wolf optimization algorithm has higher accuracy and better prediction effect.Therefore,the gray wolf algorithm is superior in establishing deformation model,which provides a new reference for deformation analysis and prediction of concrete dam.