Displacement is the most intuitive reflection of the structural behaviour of concrete dams, and it is of great significance to predict future displacement for dam health diagnosis. The mathematical models used to predict displacement are mostly established with the only regression objective of minimizing the mean square error between the measured and fitted displacements, whereas the spatial associations between the displacements of multiple monitoring points of an arch dam are ignored. To increase the prediction accuracy of machine learning technique-based mathematical models, a spatial association-coupled support vector machine model is proposed in this article to predict the displacement of high arch dams. This approach is conducted by performing an incremental distance-based spatial clustering for dam displacement field in the first step. The displacement spatial association is quantified by the integrated shape similarity index between the measured time series of multiple monitoring points and is then coupled with the fitting mean square error to optimize the parameters of the support vector machine model. A case study of an engineering example indicates that the prediction accuracy and generalization ability of the proposed double objective support vector machine model have been greatly improved compared to the traditional single objective support vector machine model. For the total 34 plumb line monitoring points on the dam body of the Jinping-I arch dam, when using the hydraulic, seasonal and time- and hydraulic, hysteretic, seasonal and time-based double objective support vector machine models, the prediction accuracy of 25 and 21 monitoring points increases with an average rate of 50.8% and 47.4%, and the degrees of overfitting are evenly reduced by 44.3% and 70.9%, respectively.

位移是混凝土大坝结构行为最直观的反映，预测未来位移对大坝健康诊断具有重要意义。用于预测位移的数学模型大多以最小化测量位移和拟合位移之间的均方误差为唯一回归目标，而忽略了拱坝多个监测点位移之间的空间关联。为了提高基于机器学习技术的数学模型的预测精度，本文提出了一种空间关联耦合的支持向量机模型来预测高拱坝的位移。这种方法是通过在第一步中对大坝位移场执行基于增量距离的空间聚类来进行的。位移空间关联通过多个监测点测量时间序列之间的综合形状相似指数量化，然后结合拟合均方误差优化支持向量机模型的参数。一个工程实例的案例研究表明，与传统的单目标支持向量机模型相比，所提出的双目标支持向量机模型的预测精度和泛化能力有了很大的提高。对于锦屏一级拱坝坝体上共34个铅垂线监测点，当采用水力、季节和时间和水力、滞后、季节和时间双目标支持向量机模型时，