Prediction of temporal scour hazard at bridge abutment

Mohammadpour, Reza; Ghani, Aminuddin Ab.; Vakili, Mohammadtaghi; Sabzevari, T.

doi:10.1007/s11069-015-2044-8

Cited by 23 publications

(2 citation statements)

References 40 publications

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“…by ensuring pad footings are located below the maximum scour depth anticipated and by ensuring sufficient length and lateral stability of piles for these conditions (Hughes et al, 2007). Some element of risk will always persist due to the inaccuracies in the underlying assumptions of the equations used (Johnson et al 1998) and difficulties in measuring accurate hydraulic data (Mohammadpour et al 2016). The vulnerability of a bridge to scour is a function of its capacity under scoured conditions and its importance on a network.…”

Section: Scour-risk Related Indicators (Hazard Exposure Vulnerability)mentioning

confidence: 99%

Risk-based bridge scour management: A survey

Pregnolato

Vardanega

Limongelli³

et al. 2021

Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations

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Section: Scour-risk Related Indicators (Hazard Exposure Vulnerability)mentioning

confidence: 99%

Risk-based bridge scour management: A survey

Pregnolato

Vardanega

Limongelli³

et al. 2021

Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations

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“…However, based on the previous works, in spite of the significance of awareness about scour around abutments in designing bridges and piers, limited AI studies have focused on the issue. According to the authors' knowledge, other studies applied various SC models such as genetic algorithm-support vector regression [31], ANN and ANFIS [32], GMDH [27] and ANFIS [33], extreme learning machine [34] to model the scour in the vicinity of abutments and piers. Therefore, there is no study to simulate the scour around abutments using a meta-heuristic multi-objective AI model.…”

Section: Study Subjectmentioning

confidence: 99%

A pareto design of evolutionary hybrid optimization of ANFIS model in prediction abutment scour depth

et al. 2019

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In this paper, a novel pareto evolutionary structure of adaptive neuro-fuzzy inference system (ANFIS) network is presented for abutment scour depth predicting. The genetic algorithm (GA) and singular value decomposition (SVD) is utilized in optimizing design of nonlinear antecedent parts and linear consequent parts of TSK-type of fuzzy rules simultaneously in ANFIS design for the first time. To this end, first the parameters affecting the scour in the vicinity of abutments are detected. After that, 11 ANFIS-GA/SVD models are introduced through the combination of the parameters affecting the scour. Based on the modeling results, the ANFIS-GA/SVD models predict the scour around abutments with a reasonable accuracy. The superior model forecasts more than 63% of scours with an error of less than 8%. The correlation coefficient (R) for the model is computed roughly 0.978. The value of the average discrepancy ratio for the model is obtained 0.981. In addition, the results of the sensitivity analysis demonstrate that the Froude number (Fr) and the ratio of the flow depth to the radius of the scour hole (h/L) are the most noticeable parameters affecting the scour depth in the vicinity of the abutments. Ultimately, a comparison between the superior model and the previous studies are presented which reveal that the current study has better performance to predict scour depth around abutments.

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Scour Analysis around Bridge Piers Using Machine Learning: A Review

Rahman,

Chavan

2024

Lecture Notes in Civil Engineering

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Prediction of temporal scour hazard at bridge abutment

Cited by 23 publications

References 40 publications

Risk-based bridge scour management: A survey

Risk-based bridge scour management: A survey

A pareto design of evolutionary hybrid optimization of ANFIS model in prediction abutment scour depth

Scour Analysis around Bridge Piers Using Machine Learning: A Review

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