A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers

By D. Tien Bui, Ataollah Shirzadi, A. Amini, Himan Shahabi, N. Al‐Ansari, Shahriar Hamidi, Sushant Kumar Singh, Binh Thai Pham, Baharin bin Ahmad, P. T. Ghazvinei. 2020

84 citations recorded by the source corpus.

Local scour depth at complex piers (LSCP) cause expensive costs when constructing bridges. In this study, a hybrid artificial intelligence approach of random subspace (RS) meta classifier, based on the reduced error pruning tree (REPTree) base classifier, namely RS-REPTree, was proposed to predict the LSCP. A total of 122 laboratory datasets were used and portioned into training (70%: 85 cases) and validation (30%: 37 cases) datasets for modeling and validation processes, respectively. The statistical metrics such as mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), and Taylor diagram were used to check the goodness-of-fit and performance of the proposed model. The capability of this model was assessed and compared with four state-of-the-art soft-computing benchmark algorithms, including artificial neural network (ANN), support vector machine (SVM), M5P, and REPTree, along with two empirical models, including the Florida Department of Transportation (FDOT) and Hydraulic Engineering Circular No. 18 (HEC-18). The findings showed that machine learning algorithms had the highest goodness-of-fit and prediction accuracy (0.885 < R < 0.945) in comparison to the other models. The results of sensitivity analysis by the proposed model indicated that pile cap location (Y) was a more sensitive factor for LSCP among other factors. The result also depicted that the RS-REPTree ensemble model (R = 0.945) could well enhance the prediction power of the REPTree base classifier (R = 0.885). Therefore, the proposed model can be useful as a promising technique to predict the LSCP.

Subfields: Civil and Structural Engineering, Ecology

View Paper: https://www.semanticscholar.org/paper/277ebf2afc08fcfad78a67c87c26385efffae361

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  1. Ataollah Shirzadi
  2. Himan Shahabi
  3. Viet-Ha Nhu
  4. N. Al‐Ansari
  5. Kamran Chapi
  6. Danesh Zandi
  7. Jie Dou
  8. Sushant Kumar Singh

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