Reliability Analysis of Retaining Wall Using Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)

Abstract

Reliability of geotechnical structures is the main concern of geotechnical engineers as is clear from previous studies and evaluations. It also helps us to determine the probability of failure. First-order second-moment method (FOSM) helps us to determine the reliability index of geo-structure. This study employs an artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for determination of reliability index of retaining wall based on sliding criterion. ANN has played a vital role in the field of geotechnical engineering as it has reduced cumbersome calculations and has increased the precision of result. The strong nonlinear relationship between the known random variables and unknown output or result is mapped easily by using ANN. ANN also ascertains the result by removing the uncertainties involved in the problem. ANFIS is an ANN system which uses fuzzy logic in contemplating the data. It works on removing the fuzziness of the values entered (random variables) and gives more realistic values of the output as compared to other approaches. This study adopts ANN and ANFIS as regression techniques. The performance of ANN and ANFIS has been assessed based on different parameters such as coefficient of correlation, root mean square error, and mean absolute error. A comparative study has been presented between the FOSM, ANN-based FOSM, and ANFIS FOSM models. Therefore, this study concludes that ANN and ANFIS is a better alternative to solve for the reliability of the retaining wall.