An optimized combination prediction model for concrete dam deformation considering quantitative evaluation and hysteresis correction

Certain degree of deformation is natural while dam operates and evolves. Due to the impact of internal and external environment, dam deformation is highly nonlinear by nature. For dam safety, it is of great significance to analyze timely deformation monitoring data and be able to predict reliably deformation. A comprehensive review of existing deformation prediction models reveals two issues that deserves further attention: (1) each environmental influencing factor contributes differently to deformation, and (2) deformation lags behind environmental factors (e.g., water level and air temperature). In response, this study presents a combination deformation prediction model considering both quantitative evaluation of influencing factors and hysteresis correction in order to further improve estimation accuracy. In this study, the complex relationship in deformation prediction is effectively captured through support vector machine (SVM) modeling. Furthermore, a modified fruit fly optimization algorithm (MFOA) is presented for SVM hyper-parameter optimization. Also, a synthetic evaluation method and a hysteresis quantification algorithm are introduced to further enhance the MFOA-SVM-based model in regards to contribution quantification and phase correction respectively. The accuracy and validity of the proposed model is evaluated in a concrete dam case, where its performance is compared with other existing models. The simulated results indicated that the proposed nonlinear MFOA-SVM model considering both quantitative evaluation and hysteresis correction, abbreviated as SEV-MFOA-SVM, is more accurate and robust than conventional models. This novel model also provides an alternative method for predicting and analyzing dam deformation and evolution behavior of other similar hydraulic structures.