Prediction of long-term temperature effect in structural health monitoring of concrete dams using support vector machines with Jaya optimizer and salp swarm algorithms

This paper presents innovative algorithms combining Jaya optimizer, salp swarm algorithms and (least-squares) support vector machines for simulating the temperature effect accurately in dam health monitoring modeling. The temperature effect is simulated by different variable sets of air temperature to get a reasonable choice. The proposed long-term air temperature based support vector machines algorithms are verified on monitoring data of a concrete gravity dam. Results confirm the ability of the proposed hybrid strategies to efficiently mine the effect of air temperature on dam behavior. The proposed approach based on direct air temperature observation can result in significant reduction in prediction errors of the dam displacements. The proposed method is tested on a concrete gravity dam and it is expected to be further tested on concrete arch dams in the future.