Design of neural networks by using genetic algorithm for the prediction of immersed CBR index

Publish Year: 1396
نوع سند: مقاله کنفرانسی
زبان: English
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LTBD04_047

تاریخ نمایه سازی: 25 آذر 1396

Abstract:

The most important parameter of soil for the conception of flexible pavements is the California Bearing Ratio after immersion (CBRimm). This parameter is determined from laboratory testing, which requires skilled workforce and time. Based on parameters simply measured like Maximum Dry Density (MDD), Optimum Moisture Content (OMC), Liquid Limit (LL), Plastic Limit (PL) and the fine fraction passing at 0.08 mm and 2 mm (F 0.08 mm, F 2mm) we proposed a neuro-genetic model to predict the index CBRimm The aim to use the genetic algorithm is to evolve at the same time: The determination of the artificial neural network architecture, transfer function and the optimization of synaptic weights. Using a neuro-genetic approach helps to increase neural network performance and it gave us a minimal average absolute error.

Authors

Mohammed el Amin Bourouis

Aboubekr Belkaid University, BP ۲۳۰ - ۱۳۰۰۰ Chetouane Tlemcen, Algeria

Abdeldjalil Zadjaoui

Aboubekr Belkaid University, BP ۲۳۰ - ۱۳۰۰۰ Chetouane Tlemcen, Algeria

Abdelkader Djedid

Aboubekr Belkaid University, BP ۲۳۰ - ۱۳۰۰۰ Chetouane Tlemcen, Algeria

Abderrahmen Bensenouci

Laboratory of public works of the west, BP ۱۶۴ Abou Tachefine Tlemcen, Algeria