ASSESSMENT OF VARIOUS INTENSITY MEASURES ONTHE PERFORMANCE OF INTAKE TOWER STRUCTUREAND PREDICTION OF THE RESULTS UNDEREARTHQUAKE GROUND MOTIONS BASED ON THEARTIFICIAL NEURAL NETWORK

Prioritizing rehabilitation and innovation solutions for structures and infrastructure based onperformance and associated losses during earthquakes is crucial due to budget constraints. Selectingappropriate structures quickly can be beneficial, especially for crucial subsystems like the intake towerstructure in dam systems, which can lead to economic and humanitarian losses. This research utilizesan artificial neural network (ANN) method to evaluate the performance of intake tower structuresduring far-fault earthquakes and depict the trend of induced damages to the structure by establishing aconnection between earthquake intensity measures (IM) and structural engineering demand parameters(EDP). Using ۱۵۰ earthquake ground motion records, the ANN method offers a promising andefficient alternative to traditional methods, providing dependable initial vulnerability assessments forintake towers post-earthquakes in a rapid timeframe. The research indicates that the ANN methodeffectively predicts induced damages and structural responses of the Intake Tower System withsatisfactory agreement compared to finite element analyses.

کلیدواژه ها:

Intake Towers ، Finite Element Method ، Bayesian Framework ، Far-Fault Ground Motion ، Artificial Neural Network ، Nonlinear Seismic Response