Articles written in Sadhana

    • A pareto design of evolutionary hybrid optimization of ANFIS model in prediction abutment scour depth


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      In this paper, a novel pareto evolutionary structure of adaptive neuro-fuzzy inference system (ANFIS) network is presented for abutment scour depth predicting. The genetic algorithm (GA) and singular value decomposition (SVD) is utilized in optimizing design of nonlinear antecedent parts and linear consequentparts of TSK-type of fuzzy rules simultaneously in ANFIS design for the first time. To this end, first the parameters affecting the scour in the vicinity of abutments are detected. After that, 11 ANFIS-GA/SVD models are introduced through the combination of the parameters affecting the scour. Based on the modeling results, the ANFIS-GA/SVD models predict the scour around abutments with a reasonable accuracy. The superior model forecasts more than 63% of scours with an error of less than 8%. The correlation coefficient (R) for the model is computed roughly 0.978. The value of the average discrepancy ratio for the model is obtained 0.981. In addition, the results of the sensitivity analysis demonstrate that the Froude number (Fr) and the ratio of the flow depth to the radius of the scour hole (h/L) are the most noticeable parameters affecting the scour depth in the vicinity ofthe abutments. Ultimately, a comparison between the superior model and the previous studies are presented which reveal that the current study has better performance to predict scour depth around abutments.

    • Estimation of scour depth around cross-vane structures using a novel non-tuned high-accuracy machine learning approach


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      Due to the vital role of rivers and canals, the protection of their banks and beds is critically important. There are various methods for protecting beds and banks of rivers and canals in which ‘‘cross-vane structures’’ is one of them. In this paper, the scour hole depth at the downstream of cross-vane structures withdifferent shapes (i.e., J, I, U, and W) is simulated utilizing a modern artificial intelligence method entitled ‘‘Outlier Robust Extreme Learning Machine (ORELM)’’. The observational data are divided into two groups: training (70%) and test (30%). After that, the most optimal activation function for simulating the scour depth at the downstream of cross-vane structures is selected. Then, using the input parameters including the ratio of the structure length to the channel width (b/B), the densimetric Froude number (Fd), the ratio of the differencebetween the downstream and upstream depths to the structure height (Dy/hst) and the structure shape factor f eleven different ORELM models are developed for estimating the scour depth. Subsequently, the suitable modeland also the most effective input parameters are identified through the conduction of an uncertainty analysis. The suitable model simulates the scour values by the dimensionless parameters b/B, Fd, Dy/hst. For this model, thevalues of the correlation coefficient (R), Variance accounted for (VAF) and the Nash-Sutcliffe efficiency (NSC) for the suitable model in the test mode are obtained 0.956, 91.378 and 0.908, respectively. Also, the dimensionlessparameters b/B, Dy/hst. are detected as the most effective input parameters. Furthermore, the results of the suitable model are compared with the extreme learning machine model and it is concluded that the ORELM model is more accurate. Moreover, an uncertainty analysis exhibits that the ORELM model has an overestimated performance. Besides, a partial derivative sensitivity analysis (PDSA) model is performed for the suitable model.

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