عنوان مقاله :
Use of Artificial Neural Networks to Estimate Installation Damage of Nonwoven Geotextiles
پديد آورندگان :
Ehsan , Amjadi Sardehaei Department of Civil Engineering - Faculty of Engineering - Kharazmi University - Tehran, Iran , Gholamhosein , Tavakoli Mehrjardi Department of Civil Engineering - Faculty of Engineering - Kharazmi University - Tehran, Iran
كليدواژه :
Nonwoven geotextiles , Artificial neural networks (ANNs) , Regression model , Retained tensile strength , strength reduction factor
چكيده فارسي :
اين مقاله فاقد چكيده فارسي است.
چكيده لاتين :
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
عنوان نشريه :
زمين شناسي مهندسي- دانشگاه خوارزمي
