大坝健康监测(Dam Health Monitoring,DHM)模型提供了大坝响应的估计值,如坝顶位移、混凝土应力等,能够得到大坝安全的性态并进行监控和预警,对大坝智慧化管理十分重要.文章详细介绍了 DHM模型的实现流程和基本框架,深入阐述了确定性模型3种实现方法,即有限元法、有限差分法和离散元法的发展现状以及主要存在的问题,以期为大坝管理人员提供最新的基础性研究成果.

Aiming at the low accuracy and over fitting phenomenon of traditional statistical models in dam stress monitoring, random forest algorithm was introduced into dam stress prediction. The dam stress prediction model based random forest algorithm was established to process, analyze and predict the stress monitoring data of a concrete gravity dam. The absolute error, the sum of mean square error and the sum of the relative error square were used as indexes to compare with multivariate linear regression model and neural network model. The results show that when the prediction range is within the sample range of the training set, the prediction accuracy of the dam stress prediction model based random forest algorithm is higher and the stability is better, which provides a new way for dam stress prediction.