In this study, the ability of WCA-ANN hybrid algorithm to model the pore water pressure coefficient in the body of Kabudwal dam at the time of construction was investigated and the effective features were identified. Therefore, five features including fill level, time, reservoir level, dewatering rate and fill speed during the 4-year statistical period were selected as the input of the model. By running the hybrid algorithm and feature selection method, the two features of fill level and time at points RU19.1 and RU19.2 have the greatest impact on modeling the pore water pressure coefficient. In addition to the above two features, in the points of the middle axis , the features of fill speed and reservoir level with error value (MSE) equal to 0.00006 and in points close to the dam reservoir, dewatering level and dewatering rate with error value equal to 0.00004 are effective in modeling the pore water pressure coefficient. The results showed that at points close to the dam axis, the fill level and at points farther from the middle axis construction time (with high sensitivity coefficient) was recognized as the most important features in modeling the pore water pressure coefficient with artificial intelligence models.
- حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران میشود.
- پرداخت حق اشتراک و دانلود مقالات اجازه بازنشر آن در سایر رسانههای چاپی و دیجیتال را به کاربر نمیدهد.