RT - Journal Article T1 - Optimization of ANFIS Model using Genetic Algorithm for Estimation of Scour Depth around Bridge Abutments JF - JSTNAR YR - 2021 JO - JSTNAR VO - 25 IS - 1 UR - http://jstnar.iut.ac.ir/article-1-3938-en.html SP - 75 EP - 89 K1 - ANFIS K1 - Algorithm genetic K1 - Optimization K1 - Scour K1 - Bridge abutment K1 - Sensitivity analysis AB - Optimization of artificial intelligence (AI) models is a significant issue because it enhances the performance and flexibility of the numerical models. In this study, scour depth around bridge abutments with different shapes was estimated by means of ANFIS and ANFIS-Genetic Algorithm. In other words, the membership functions of the ANFIS model were optimized using the genetic algorithm, finding that the performance of ANFIS model was increased. Firstly, effective input parameters on the scour depth around bridge abutments were defined. Then, by using the input parameters, eleven ANFIS and ANFIS-GA models were produced. Next, the superior ANFIS and ANFIS-GA models were introduced by analyzing the numerical results. For example, the correlation coefficient and scatter index for ANFIS model were calculated to be 0.979 and 0.070; for ANFIS-GA, these were 0.986 and 0.056, respectively. In addition, the average discrepancy ratio (DRave) for ANFIS and ANFIS-GA models was 0.984 and 0.988, respectively. Also, it was shown that the ANFIS-GA models had more accuracy, as compared to the ANFIS models. Moreover, a sensitivity analysis showed that Froude number (Fr) and ratio of flow depth to radius of scour hole (h/L) were the most influential input parameters for simulating the scour depth around bridge abutments. LA eng UL http://jstnar.iut.ac.ir/article-1-3938-en.html M3 10.47176/jwss.25.1.13931 ER -