An Application of Machine Learning Algorithms on the Prediction of the Damage Level of Rubble-Mound Breakwaters

Saha, Susmita; De, Soumen; Changdar, Satyasaran

doi:10.1115/1.4062475

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…Therefore, the propagation velocity of the excitation wave is only dependent on the real-time water depth of the wave. In future studies, we aim to explore the relationships between these influencing factors and other physical parameters, such as the speed of wave propagation, using the effective and accurate method of machine learning algorithms [45].…”

Section: Analyze the Changes In Propagation Velocity Of Excitation Wa...mentioning

confidence: 99%

Propagation Velocity of Excitation Waves Caused by Turbidity Currents

Xu,

Sun,

Ren

et al. 2024

JMSE

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Turbidity currents are important carriers for transporting terrestrial sediment into the deep sea, facilitating the transfer of matter and energy between land and the deep sea. Previous studies have suggested that turbidity currents can exhibit high velocities during their movement in submarine canyons. However, the maximum vertical descent velocity of high-concentration turbid water simulating turbidity currents does not exceed 1 m/s, which does not support the understanding that turbidity currents can reach speeds of over twenty meters per second in submarine canyons. During their movement, turbidity currents can compress and push the water ahead, generating propagating waves. These waves, known as excitation waves, exert a force on the seafloor, resuspending bottom sediments and potentially leading to the generation of secondary turbidity currents downstream. Therefore, the propagation distance of excitation waves is not the same as the initial journey of the turbidity currents, and the velocity of excitation waves within this journey has been mistakenly regarded as the velocity of the turbidity currents. Research on the propagation velocity of excitation waves is of great significance for understanding the sediment supply patterns of turbidity currents and the transport patterns of deep-sea sediments. In this study, numerical simulations were conducted to investigate the velocity of excitation waves induced by turbidity currents and to explore the factors that can affect their propagation velocity and amplitude. The relationship between the velocity and amplitude of excitation waves and different influencing factors was determined. The results indicate that the propagation velocity of excitation waves induced by turbidity currents is primarily determined by the water depth, and an expression (v2 = 0.63gh) for the propagation velocity of excitation waves is provided.

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Section: Analyze the Changes In Propagation Velocity Of Excitation Wa...mentioning

confidence: 99%

Propagation Velocity of Excitation Waves Caused by Turbidity Currents

Xu,

Sun,

Ren

et al. 2024

JMSE

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“…Saha et al [28] proposed a machine learning-based method to predict the damage level of rubble-mound breakwaters. In this research work, machine learning algorithms such as Random Forest, Adaboost, Support vector regression, gradient boosting, and deep artificial neural networks were utilized to compare the performance to estimate the damage level of breakwaters.…”

Section: Literature Reviewmentioning

confidence: 99%

Stability Analysis of Breakwater Armor Blocks Based on Deep Learning

Zhu,

Bai,

Liu

et al. 2024

Water

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This paper aims to use deep learning algorithms to identify and study the stability of breakwater armor blocks. It introduces a posture identification model for fender blocks using a Mask Region-based Convolutional Neural Network (R-CNN), which has been enhanced by considering factors affecting breakwater fender blocks. Furthermore, a wave prediction model for breakwaters is developed by integrating Bidirectional Encoder Representations from Transformers (BERTs) with Bidirectional Long Short-Term Memory (BiLSTM). The performance of these models is evaluated. The results show that the accuracy of the Mask R-CNN and its comparison algorithms initially increases and then decreases with higher Intersection Over Union (IOU) thresholds, peaking at 95.16% accuracy at an IOU threshold of 0.5. The BERT-BiLSTM wave prediction model maintains a loss value around 0.01 and an accuracy of approximately 90.00%. These results suggest that the proposed models offer more accurate stability assessments of breakwater armor blocks. By combining the random forest prediction model with BiLSTM, the wave characteristics and fender posture can be predicted better, offering reliable decision support for breakwater engineering.

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A guidance of solid element application in predicting the ultimate strength of flat plates in compression

Wang,

Kong,

et al. 2024

Journal of Ocean Engineering and Science

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An Application of Machine Learning Algorithms on the Prediction of the Damage Level of Rubble-Mound Breakwaters

Cited by 5 publications

References 44 publications

Propagation Velocity of Excitation Waves Caused by Turbidity Currents

Propagation Velocity of Excitation Waves Caused by Turbidity Currents

Stability Analysis of Breakwater Armor Blocks Based on Deep Learning

A guidance of solid element application in predicting the ultimate strength of flat plates in compression

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