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Abstract
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Studies have shown that the major cause of the bridge failures is the local scour around the pier foundations or their abutments.
The local scour around the bridge pier is occurred by changing the flow pattern and creating secondary vortices in
the front and rear of the bridge piers. Until now, many researchers have proposed empirical equations to estimate the bridge
pier scour based on laboratory and field datasets. However, scale impact, laboratory simplification, natural complexity of
rivers and the personal judgement are among the main causes of inaccuracy in the empirical equations. Therefore, due to the
deficiencies and disadvantages of existing equations and the complex nature of the local scour phenomenon, in this study,
the adaptive network-based fuzzy inference system (ANFIS) and teaching–learning-based optimization (TLBO) method
were combined and used. The parameters of the ANFIS were optimized by using TLBO optimization method. To develop
the model and validate its performance, two datasets were used including laboratory dataset that consisted of experimental
results from the current study and previous ones and the field dataset. In total, 27 scaled experiments of different types of
pier groups with different cross sections and side slopes were carried out. To evaluate the model ability in prediction of scour
depth, results were compared to the standard ANFIS and empirical equations using evaluation functions including Hec-18,
Froehlich and Laursen and Toch equations. The results showed that adding TLBO to the standard ANFIS was efficient and can
increase the model capability and reliability. Proposed model achieved better results than Laursen and Toch equation which
had the best results among empirical relationships. For instance, proposed model in comparison with the Laursen and Toch
equation, based on the RMSE function, yielded 50.4% and 71.8% better results in laboratory and field datasets, respectively.
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