Prediction of time-varying maximum scour depth around short abutments using soft computing methodologies - A comparative study

Hosseini, Khosrow; Karami, Hojat; Hosseinjanzadeh, Hossein; Ardeshir, Abdollah

doi:10.1007/s12205-015-0115-8

Cited by 13 publications

(5 citation statements)

References 29 publications

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“…Besides, unstable shear layers produced according to the separation of flow upstream and downstream of bridge abutments rotate in the form of vortex structures known as wake vortices. Wake vortices act similarly to small eddies and sediment rising from the bed [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Bonakdari

Moradi

Ebtehaj

et al. 2020

Water

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Abutment scour is a complex three-dimensional phenomenon, which is one of the leading causes of marine structure damage. Structural integrity is potentially attainable through the precise estimation of local scour depth. Due to the high complexity of scouring hydrodynamics, existing regression-based relations cannot make accurate predictions. Therefore, this study presented a novel expansion of extreme learning machines (ELM) to predict abutment scour depth (ds) in clear water conditions. The model was built using the relative flow depth (h/L), excess abutment Froude number (Fe), abutment shape factor (Ks), and relative sediment size (d50/L). A wide range of experimental samples was collected from the literature, and data was utilized to develop the ELM model. The ELM model reliability was evaluated based on the estimation results and several statistical indices. According to the results, the sigmoid activation function (correlation coefficient, R = 0.97; root mean square error, RMSE = 0.162; mean absolute percentage error, MAPE = 7.69; and scatter index, SI = 0.088) performed the best compared with the hard limit, triangular bias, radial basis, and sine activation functions. Eleven input combinations were considered to investigate the impact of each dimensionless variable on the abutment scour depth. It was found that ds/L = f (Fe, h/L, d50/L, Ks) was the best ELM model, indicating that the dimensional analysis of the original data properly reflected the underlying physics of the problem. Also, the absence of one variable from this input combination resulted in a significant accuracy reduction. The results also demonstrated that the proposed ELM model significantly outperformed the regression-based equations derived from the literature. The ELM model presented a fundamental equation for abutment scours depth prediction. Based on the simulation results, it appeared the ELM model could be used effectively in practical engineering applications of predicting abutment scour depth. The estimated uncertainty of the developed ELM model was calculated and compared with the conventional and artificial intelligence-based models. The lowest uncertainty with a value of ±0.026 was found in the proposed model in comparison with ±0.50 as the best uncertainty of the other models.

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Section: Introductionmentioning

confidence: 99%

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Bonakdari

Moradi

Ebtehaj

et al. 2020

Water

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“…Finally, the scour topography was measured with a point gaugea + 1 accuracy on a grid with a 5 Â 5 cm mesh. A test was conducted using the solid abutment to determine the equilibrium scour time, which was taken as the time at which scour depth was 95% of the maximum (Hosseini et al 2016). The graph of the time-scour relation (Figure 4) was used, and 4 hours was chosen as the experiment duration.…”

Section: Methodsmentioning

confidence: 99%

Effect of L-shaped slots on scour around a bridge abutment

Saad

Fattouh

Mokhtar

2021

Water Practice and Technology

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Local scour is the most significant cause of bridge failure. Providing a short abutment with a straight slot has proved to be an effective method for reducing scour at this abutment. In this study, laboratory experiments have been conducted to investigate the effectiveness of using L-shaped slots in comparison to the commonly used straight slot, on the scour reduction at short vertical-wall abutment under clear-water flow conditions and uniform bed materials. The slots were just above the bed and their diameters equal to half the abutment's length. The results illustrated that it is essential to have a straight slot in any combination of slots, as any configuration without one is inefficient. Also, a combination of a straight slot with one side slot in the middle of the abutment's width gives better performance than an individual straight slot, as it reduces the depth, area, and volume of the scour hole by about 32.6, 26.8, and 43.6% respectively, in comparison to 23.2, 20.7, and 35.3% for the straight slot alone.

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“…They are pivotal in flood control, irrigation, and urban drainage systems. Scholars have become interested in scouring around such buildings throughout the years because of their vital role in guaranteeing the structural safety of these constructs (Hosseini et al 2016). Prolonged scouring, especially when underestimated or unchecked, can expose the foundation of these hydraulic bodies, jeopardizing their stability (Mutlu Sumer 2007, Pizarro et al 2020.…”

Section: Introductionmentioning

confidence: 99%

Predicting maximum scour depth at sluice outlet: a comparative study of machine learning models and empirical equations

Le,

Thu Hien

2024

Environ. Res. Commun.

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Estimating the maximum scour depth of sluice outlets is pivotal in hydrological engineering, directly influencing the safety and efficiency of water infrastructure. This research compared traditional empirical formulas with advanced machine learning (ML) algorithms, including RID, SVM, CAT, and XGB, utilizing experimental datasets from prior studies. Performance statistics highlighted the efficacy of the ML algorithms over empirical formulas, with CAT and XGB leading the way. Specifically, XGB demonstrated superiority with a correlation coefficient (CORR) of 0.944 and a root mean square error (RMSE) of 0.439. Following closely, the CAT model achieved a CORR of 0.940, and SVM achieved 0.898. For empirical formulas, although CORR values up to 0.816 and RMSE values of 0.799 can be obtained, these numbers are still lower than most ML algorithms. Furthermore, a sensitivity analysis underscored the densimetric Froude number (Fd) as the most crucial factor in ML models, with influences ranging from 0.839 in RID to 0.627 in SVM. Uncertainty in ML model estimates was further quantified using the Monte Carlo technique with 1,000 simulations on testing datasets. CAT and XGB have shown more stability than the other models in providing estimates with mean CORRs of 0.937 and 0.946, respectively. Their 95% confidence intervals (CIs) are [0.929–0.944] for CAT and [0.933–0.954] for XGB. These results demonstrated the potential of ML algorithms, particularly CAT and XGB, in predicting the maximum scour depth. Although these models offer high accuracy and higher 95% CI than others, the empirical formulas retain their relevance due to their simplicity and quick computation, which may still make them favored in certain scenarios.

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Prediction of time-varying maximum scour depth around short abutments using soft computing methodologies - A comparative study

Cited by 13 publications

References 29 publications

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Effect of L-shaped slots on scour around a bridge abutment

Predicting maximum scour depth at sluice outlet: a comparative study of machine learning models and empirical equations

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