Abstract
In this paper, for the first time, the scour pattern around twin bridge piers was predicted using an optimized hybrid algorithm. The hybrid algorithm (ANFIS-FA) was developed through the combination of the Adaptive Neuro-Fuzzy Inference System (ANFIS) as well as the Firefly algorithm (FA). After that, four ANFIS and ANFIS-FA models were developed by implementing the parameters influencing the scour depth around twin piers. To assess the performance of soft computing models, the Monte Carlo simulations were employed. In addition, the validation of the numerical models was carried out by the k-fold cross validation approach. It is worth noting that the value of k in the k-fold cross validation was considered as 5. Based on the modeling results, the analysis of the results indicated that ANFIS-FA models are more precise than ANFIS models. Then, the superior model was detected through the establishment of a sensitivity analysis. The superior model is a function of all input parameters. This model estimated scour values with reasonable accuracy. For example, the values of R2, MAPE and RMSE were calculated 0.991, 5.876 and 0.015, respectively. Furthermore, the results of the error distribution demonstrated that about 66% of the results obtained from the superior model have an error less than 5%. Next, the Froude number (Fr) was identified as the most effective input parameter in estimating the scour hole around twin bridge piers. The study showed that the firefly algorithm could successfully optimize the ANFIS network and the performance of the hybrid model (ANFIS-FA) was better than the simple model (ANFIS). Finally, by performing an uncertainty analysis, it was concluded that the superior model has an overestimated performance. The uncertainty analysis proved that the hybrid model had a narrower uncertainty band in comparison with the ANFIS model.
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Kohansarbaz, A., Kohansarbaz, A., Yaghoubi, B. et al. An integration of adaptive neuro-fuzzy inference system and firefly algorithm for scour estimation near bridge piers. Earth Sci Inform 14, 1399–1411 (2021). https://doi.org/10.1007/s12145-021-00652-z
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DOI: https://doi.org/10.1007/s12145-021-00652-z