SSA optimized back propagation neural network model for dam displacement monitoring based on long-term temperature data

Featured with the harmonic sinusoidal function to reflect temperature effects, the hydrostatic-season-time (HST) model is often used to monitor the concrete gravity dam health, but it does not take account of the effects of environment temperatures in real-term and has flaws especially when applied in conditions of significant temperature variations. A model of Sparrow Search Algorithm optimized error Back Propagation neural network (SSA-BP) based on the hydrostatic-temperature-time (HTT) model is proposed in this paper for predicting the concrete gravity dam displacement using the long-term environment temperature variable sets to reflect temperature effects. Successive Projections Algorithm (SPA) is used for the first time for feature selection on long-term temperature variables to further optimize the model (as SPA-SSA-BP). Through a case study with the practical observed data from a reality high concrete gravity dam, the effectiveness of the new model is verified, suggesting that HTT-based SSA-BP models have better performance than HST with the best result obtained when using the 2-year long variable sets. The SSA-BP model has much lower error in predicting the concrete dam displacement than Multiple Linear Regression (MLR). The arithmetic speed and prediction accuracy of the SPA-SSA-BP model is optimized as it can minimize the collinearity among feature variables in the long-term HTT variable sets, bring down the input variable dimension close to the level of HST, and diminish the redundant data information.