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EN
In this study novel integrative machine learning models embedded with the firefly algorithm (FA) were developed and employed to predict energy dissipation on block ramps. The used models include multi-layer perceptron neural network (MLPNN), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), support vector regression (SVR), linear equation (LE), and nonlinear regression equation (NE). The investigation focused on the evaluation of the performance of standard and integrative models in different runs. The performances of machine learning models and the nonlinear equation are higher than the linear equation. The results also show that FA increases the performance of all applied models. Moreover, the results indicate that the ANFIS-FA is the most stable integrative model in comparison to the other embedded methods and reveal that GMDH and SVR are the most stable technique among all applied models. The results also show that the accuracy of the LE-FA technique is relatively low, RMSE=0.091. The most accurate results provide SVR-FA, RMSE=0.034.
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EN
Bridge failure, due to local scour at bridge pier foundations, has become a critical issue in river and bridge engineering, which might lead to transportation disruption, loss of lives and economic problems. A practical solution to prevent bridge collapses is the implementation of scour mitigation methods around bridge foundations. Based on an experimental perspective, this study is focused on the infuence of the size and position of circular collars from the sediment bed on scour depth at two tandem piers. To meet this end, long-lasting experiments are performed under clear-water conditions using uniform sand for bed materials. Compared to the adjacent position of the collar on the bed, placing the collars below the bed would increase the delay time of scour at the piers up to four times. However, regardless of the delay time, the observations indicate that locating the collars on the initial bed surface results in maximum reduction in scour depths around the piers. It was found that diminishing the fow intensity has a dramatic impact on the scour reduction at the piers, so that maximum reduction in scour depths at piers increased on average from 20 to 70% with the reduction in the fow intensity from 0.95 to 0.9.
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