Physical Mechanism for Seabed Scouring around a Breakwater—A Case Study in Mailiao Port

Tseng, I-Fan; Hsu, Chih‐Hung; Yeh, Ping Hung; Lin, Ting-Chieh

doi:10.3390/jmse10101386

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…In addition to the hydrodynamic forces from tides and waves, which can be compounded by climate change influences, critical infrastructures including underwater pipelines, coastal defence structures, and coastal zone management processes such as dredging can contribute to conditions that are favourable to increased seabed scouring through the disruption of natural sediment transport processes and the alteration of the prevailing hydrodynamic environment in the nearshore region. Scouring at the toes of critical coastal defence structures (e.g., sloping and vertical seawalls) can result in the loss of structural integrity (Salauddin and Pearson, 2019a;Salauddin and Pearson, 2019b;Tseng et al, 2022) and ultimate failure, and is particularly critical in the management of coastal flood risks. Toe scouring can elevate wave overtopping discharge at defences, by increasing water depth at the defence and causing the formation of larger waves at the structure (Peng et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Efficient data-driven machine learning models for scour depth predictions at sloping sea defences

Habib,

Abolfathi,

O’Sullivan

et al. 2024

Front. Built Environ.

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Seawalls are critical defence infrastructures in coastal zones that protect hinterland areas from storm surges, wave overtopping and soil erosion hazards. Scouring at the toe of sea defences, caused by wave-induced accretion and erosion of bed material imposes a significant threat to the structural integrity of coastal infrastructures. Accurate prediction of scour depths is essential for appropriate and efficient design and maintenance of coastal structures, which serve to mitigate risks of structural failure through toe scouring. However, limited guidance and predictive tools are available for estimating toe scouring at sloping structures. In recent years, Artificial Intelligence and Machine Learning (ML) algorithms have gained interest, and although they underpin robust predictive models for many coastal engineering applications, such models have yet to be applied to scour prediction. Here we develop and present ML-based models for predicting toe scour depths at sloping seawall. Four ML algorithms, namely, Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Artificial Neural Networks (ANNs), and Support Vector Machine Regression (SVMR) are utilised. Comprehensive physical modelling measurement data is utilised to develop and validate the predictive models. A Novel framework for feature selection, feature importance, and hyperparameter tuning algorithms are adopted for pre- and post-processing steps of ML-based models. In-depth statistical analyses are proposed to evaluate the predictive performance of the proposed models. The results indicate a minimum of 80% prediction accuracy across all the algorithms tested in this study and overall, the SVMR produced the most accurate predictions with a Coefficient of Determination (r2) of 0.74 and a Mean Absolute Error (MAE) value of 0.17. The SVMR algorithm also offered most computationally efficient performance among the algorithms tested. The methodological framework proposed in this study can be applied to scouring datasets for rapid assessment of scour at coastal defence structures, facilitating model-informed decision-making.

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Section: Introductionmentioning

confidence: 99%