Seepage Analysis Through Earthen Dam by Artificial- Neural- Networks (ANNs): Duhok Dam as a Case Study

Abstract

An earthen dam is a structure made of soil particles that are bonded together and compacted in layers using mechanical methods. depend on their weight to combat forces such as sliding & overturning. Seepage passage during earth dam is the principal cause of collapse owing to erosion, scouring, and piping. The passage of water during soil can result in the displacement of the particles. Ongoing motion induces erosion. This study depends on Artificial Neural Networks (ANN) for estimation seepage in Duhok dam, utilizing measured upstream water level and flow rate, as well as piezometric head measurements from four distinct parts of the dam structure. The findings indicated excellent model efficacy. Artificial Neural Network models necessitate less field data, rendering them advantageous for dam safety evaluations, providing insights into their relative efficacy in forecasting seepage in earthen dams under diverse scenarios. The results were derived from established statistical metrics R², MAE, MAPE and E-NASH. This research concluded that many ANNs has excellent forecasting capabilities.