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A Comprehensive Experimental and Computational Investigation on Estimation of Scour Depth at Bridge Abutment: Emerging Ensemble Intelligent Systems

Author

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  • Manish Pandey

    (National Institute of Technology Warangal)

  • Masoud Karbasi

    (University of Zanjan)

  • Mehdi Jamei

    (Shahid Chamran University of Ahvaz
    Al-Ayen University)

  • Anurag Malik

    (Regional Research Station)

  • Jaan H. Pu

    (University of Bradford)

Abstract

Several bridges failed because of scouring and erosion around the bridge elements. Hence, precise prediction of abutment scour is necessary for the safe design of bridges. In this research, experimental and computational investigations have been devoted based on 45 flume experiments carried out at the NIT Warangal, India. Three innovative ensemble-based data intelligence paradigms, namely categorical boosting (CatBoost) in conjunction with extra tree regression (ETR) and K-nearest neighbor (KNN), are used to accurately predict the scour depth around the bridge abutment. A total of 308 series of laboratory data (a wide range of existing abutment scour depth datasets (263 datasets) and 45 flume data) in various sediment and hydraulic conditions were used to develop the models. Four dimensionless variables were used to calculate scour depth: approach densimetric Froude number (Fd50), the upstream depth (y) to abutment transverse length ratio (y/L), the abutment transverse length to the sediment mean diameter (L/d50), and the mean velocity to the critical velocity ratio (V/Vcr). The Gradient boosting decision tree (GBDT) method selected features with higher importance. Based on the feature selection results, two combinations of input variables (comb1 (all variables as model input) and comb2 (all variables except Fd50)) were used. The CatBoost model with Comb1 data input (RMSE = 0.1784, R = 0.9685, MAPE = 10.4724) provided better accuracy when compared to other machine learning models.

Suggested Citation

  • Manish Pandey & Masoud Karbasi & Mehdi Jamei & Anurag Malik & Jaan H. Pu, 2023. "A Comprehensive Experimental and Computational Investigation on Estimation of Scour Depth at Bridge Abutment: Emerging Ensemble Intelligent Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3745-3767, July.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:9:d:10.1007_s11269-023-03525-w
    DOI: 10.1007/s11269-023-03525-w
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    References listed on IDEAS

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    1. Reza Mohammadpour & Aminuddin Ab. Ghani & Mohammadtaghi Vakili & Tooraj Sabzevari, 2016. "Prediction of temporal scour hazard at bridge abutment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1891-1911, February.
    2. Manish Pandey & P. K. Sharma & Z. Ahmad & Nilav Karna, 2018. "Maximum scour depth around bridge pier in gravel bed streams," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(2), pages 819-836, March.
    3. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    4. Reza Mohammadpour & Aminuddin Ghani & Mohammadtaghi Vakili & Tooraj Sabzevari, 2016. "Prediction of temporal scour hazard at bridge abutment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(3), pages 1891-1911, February.
    5. Manish Goyal & C. Ojha, 2011. "Estimation of Scour Downstream of a Ski-Jump Bucket Using Support Vector and M5 Model Tree," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2177-2195, July.
    6. Elnaz Eghlidi & Gholam-Abbas Barani & Kourosh Qaderi, 2020. "Laboratory Investigation of Stilling Basin Slope Effect on Bed Scour at Downstream of Stepped Spillway: Physical Modeling of Javeh RCC Dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 87-100, January.
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    1. Jamei, Mehdi & Sharma, Prabhakar & Ali, Mumtaz & Bora, Bhaskor J. & Malik, Anurag & Paramasivam, Prabhu & Farooque, Aitazaz A. & Abdulla, Shahab, 2024. "Application of an explainable glass-box machine learning approach for prognostic analysis of a biogas-powered small agriculture engine," Energy, Elsevier, vol. 288(C).
    2. Enes Gul & Efthymia Staiou & Mir Jafar Sadegh Safari & Babak Vaheddoost, 2023. "Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Türkiye," Sustainability, MDPI, vol. 15(15), pages 1-17, July.
    3. Anubhav Baranwal & Bhabani Shankar Das, 2024. "Live-Bed Scour Depth Modelling Around the Bridge Pier Using ANN-PSO, ANFIS, MARS, and M5Tree," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4555-4587, September.

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