A hybrid model of bald eagle search and relevance vector machine for dam safety monitoring using long-term temperature

Abstract

Accurate simulation of temperature effect is a major challenge for computational (intelligent) prediction models used for monitoring health of high concrete dams, especially in regions with long freezing periods and distinct seasons, occasional extreme weather. A Hydrostatic-Temperature long-term-Time (HTLT) model was proposed for better temperature effect simulation using long-term measured environment temperatures and explored the influence of temperatures data sets of different time lengths on dam displacement prediction accuracy with the traditional Hydrostatic-Season-Time model as control. The Bald Eagle Search algorithm was coupled with the Relevance Vector Machine to form a novel hybrid model (BES-RVM) for predicting concrete gravity dam displacement response and comparison of nonlinear mapping capability between different kernel functions was conducted. Further optimized by Successive Projections Algorithm (SPA) for feature selection, sensitive features were selected from the proposed HTLT variable sets to improve the prediction model’s accuracy. The prediction model was experimented on a real concrete dam with results showing that the BES-RVM model gave excellent prediction performance. Using the HTLT variable sets significantly reduced the prediction errors and the best performed result came from variables of the two-year long temperatures data. The SPA optimized BES-RVM model largely brought down the dimension and the collinearity of the input variables and further improved the prediction performance.