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

Document identifier: oai:DiVA.org:ltu-77620
Access full text here:10.3390/su12031063
Keyword: Engineering and Technology, Civil Engineering, Geotechnical Engineering, Teknik och teknologier, Samhällsbyggnadsteknik, Geoteknik, Scour depth, Complex piers, Pile cap, Machine learning algorithms, Ensemble models, Soil Mechanics
Publication year: 2020

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.


Dieu Tien Bui

Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Other publications >>

Ataollah Shirzadi

Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
Other publications >>

Ata Amini

Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
Other publications >>

Himan Shahabi

Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj , Iran. Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan
Other publications >>

Nadhir Al-Ansari

Luleå tekniska universitet; Geoteknologi
Other publications >>

Shahriar Hamidi

Department of Water Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran
Other publications >>

Sushant K. Singh

Department of Health, Insurance & Life Sciences, Data & Analytics, Virtusa Corporation, Irvington, NJ, USA
Other publications >>

Binh Thai Pham

Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
Other publications >>

Baharin Bin Ahmad

Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia
Other publications >>

Pezhman Taherei Ghazvinei

Department of Civil Engineering, Technical and Engineering College, Ale Taha University, Tehran, Iran
Other publications >>

Documents attached

Click on thumbnail to read

Record metadata

Click to view metadata