TY - JOUR ID - 4184 TI - Predicting potential of controlled blasting-induced liquefaction using neural networks and neuro -fuzzy system JO - Scientia Iranica JA - SCI LA - en SN - 1026-3098 AU - Asvar, Fariba AU - Shirmohammadi Faradonbeh, Arash AU - Barkhordari, Kazem AD - Department of civil engineering, Yazd University, Iran AD - Department of civil engineering, Yazd University, Iran AD - Departme n t of Civil Engineering , Yazd University , Iran Y1 - 2018 PY - 2018 VL - 25 IS - 2 SP - 617 EP - 631 KW - Soil liquefaction KW - Controlled blasting KW - pore water pressure KW - artificial neural network (ANN) KW - Neuro-fuzzy KW - sensitivity analysis DO - 10.24200/sci.2017.4184 N2 - In recent years, controlled blasting has turned into an efficient method for  evaluation of soil liquefaction in real scale and evaluation of ground improvement techniques. Predicting blast-induced soil liquefaction by using collected information can be an effective step in the[a1]  study of blast-induced liquefaction. In this study, to estimate residual pore pressure ratio, first, multi- layer perceptron neural network is used in which error (RMS) for the network was calculated as 0.105. Next, neuro-fuzzy network, ANFIS was used for modeling. Different ANFIS models  are created using Grid  partitioning (GP), Subtractive Clustering (SCM), and Fuzzy C-means Clustering (FCM). Minimum error is obtained using by FCM at about 0.081. Finally, radial basis function (RBF) network is used. Error of this method was about 0.06. Accordingly, RBF network has better performance. Variables including fine-content, relative density, effective overburden pressure and SPT value  are considered as input components and the Ru, residual[a2]  pore pressure ratio was used as the only output component for designing prediction models. In the next stage the network output is compared with the results of a regression analysis. Finally, sensitivity analysis for RBF network is tested,  its results reveal that and SPT are the most effective factors in determining Ru. UR - https://scientiairanica.sharif.edu/article_4184.html L1 - https://scientiairanica.sharif.edu/article_4184_4c92541c36dcaa3c08b53c1c9efe1d78.pdf ER -