In this study, a backpropagation neural network algorithm was developed in order to predict the liquefaction cyclic resistance ratio (CRR) of sand-silt mixtures. A database, consisting of sufficient published data of laboratory cyclic triaxial, torsional shear and simple shear tests results, was collected and utilized in the ANN model. Several ANN models were developed with different sets of input parameters in order to determine the model with best performance and preciseness. It has been illustrated that the proposed ANN model can predict the measured CRR of the different data set which was not incorporated in the developing phase of the model with the good degree of accuracy. The subsequent sensitivity analysis was performed to compare the effect of each parameter in the model with the laboratory test results. At the end, the participation or relative importance of each parameter in the ANN model was obtained.218 meter can be determined using cyclic tests on the undisturbed or reconstituted laboratory specimens.In recent years, the application of artificial neural networks (ANNs) for the solution of variety of geotechnical engineering problems has been the focus of many researchers. The method of artificial neural networks essentially involves the mapping of a complex input pattern with another complex output pattern using data processing models made up of extensively interconnected neurons [1]. Artificial Neural Networks (ANN), a powerful tool for statistical data manipulation, have been used in many complicated geotechnical engineering problems such as stress-strain modeling of soils, piles bearing capacity, settlement of shallow foundations, earthquake induced liquefaction and seismic lateral spreading [1] [2]. Considering of the high complexity and multiparameter dependence of soil response, relatively simple, but robust, feed-forward neural network models trained by back propagation algorithms have found wide usage in the field of geotechnical engineering [3]- [5].Recently, many researchers [6]- [9] have implemented the ANN model in the assessment of liquefaction resistance of sands. For example, a simplified method proposed by Juang et al. [10], based on cone penetration test (CPT) data, clearly illustrated the potential applicability and suitability of ANNs in the assessment of liquefaction resistance. Rahman and Wung [11] have also contributed to this area of research by developing a neural network model based on standard penetration test (SPT) data. As indicated earlier, data from laboratory element tests provides another avenue for understanding the cyclic loading response of soils in a fundamental manner [12] [13]. have attempted to use a limited number of element test results in the ANN model to explain the parameters affecting CRR. However, no comprehensive study has yet been conducted to examine a wide-range laboratory test results on liquefaction resistance ratio (CRR) using ANN as a framework.Based on these considerations, this study introduces a new ANN model, developed to predict liquefactio...
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