TY - JOUR
JF - jeg
JO - JEG
VL - 13
IS - 5
PY - 2020
Y1 - 2020/2/01
TI - Use of Artificial Neural Networks to Estimate Installation Damage of Nonwoven Geotextiles
TT - استفاده از شبکه عصبی مصنوعی بهمنظور برآورد (تخمین) خرابی حین نصب ژئوتکستایلهای بافته نشده
N2 - This paper presents a feed-forward back-propagation neural network model to predict the retained tensile strength and design chart to estimate the strength reduction factors of nonwoven geotextiles due to the installation process. A database of 34 full-scale field tests was utilized to train, validate and test the developed neural network and regression model. The results show that the predicted retained tensile strength using the trained neural network is in good agreement with the results of the test. The predictions obtained from the neural network are much better than the regression model as the maximum percentage of error for training data is less than 0.87% and 18.92%, for neural network and regression model, respectively. Based on the developed neural network, a design chart has been established. As a whole, installation damage reduction factors of the geotextile increases in the aftermath of the compaction process under lower as-received grab tensile strength, higher imposed stress over the geotextiles, larger particle size of the backfill, higher relative density of the backfill and weaker subgrades.
SP - 1
EP - 22
AU - Amjadi Sardehaei, Ehsan
AU - Tavakoli Mehrjardi, Gholamhosein
AD -
KW - Artificial neural networks (ANNs)
KW - Regression model
KW - Nonwoven geotextiles
KW - Retained tensile strength
KW - strength reduction factor.
UR - http://jeg.khu.ac.ir/article-1-2655-en.html
ER -