Prediction of irregular wave (current)-induced pore water pressure around monopile using machine learning methods

Cheng, Haiyang; Cheng, Yan; Zheng, Yuwei; Zhang, Jing; Lyu, Xing

doi:10.1016/j.coastaleng.2023.104291

Cited by 6 publications

(4 citation statements)

References 55 publications

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“…Content Analysis optimization fluidstructure interaction free surface flows (Das & Meher, 2019) x (Ramos, Giannini, Calheiros-Cabral, Rosa-Santos, & Taveira-Pinto, 2022) x x (Didier, Neves, Martins, & Neves, 2014) x (Qiu et al, 2017) x (Haiyang Cheng, Cheng, Zheng, Zhang, & Lyu, 2023) x (Chen, 2017) x (Wengang Zhang et al, 2024) x This Paper…”

Section: Author and Titlementioning

confidence: 99%

Systematic Literature Review on Smoothed Particle Hydrodynamic Method for Coastal Protection Optimizing

Javier Purba,

Ramadhani,

Hamdani

2024

SLJIL

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The Smoothed Particle Hydrodynamic (SPH) method has been extensively used in coastal engineering applications, including coastal protection optimization. This paper presents a systematic literature review of research studies that have utilized SPH method for coastal protection optimization. The review covers various applications of SPH method, such as optimizing breakwaters, studying wave-structure interactions, modelling wave-induced erosion, simulating tsunami run-up and inundation, and optimizing artificial reefs. The review demonstrates the versatility of SPH method in coastal engineering applications and highlights the importance of its capabilities in simulating complex coastal processes. The limitations and challenges of using SPH method are also discussed in the context of coastal protection optimization. The review provides a comprehensive overview of the current state of research on SPH method for coastal protection optimization, which can serve as a valuable reference for researchers and practitioners in the field

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Section: Author and Titlementioning

confidence: 99%

Systematic Literature Review on Smoothed Particle Hydrodynamic Method for Coastal Protection Optimizing

Javier Purba,

Ramadhani,

Hamdani

2024

SLJIL

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“…Many researches give evidence that liquefaction of cargo is accompanied by an increase in PWP [2]. So, the PWP in granular bulk cargo is often used to predict the cargo liquefaction [3]. Therefore, an accurate prediction of shaking-induced PWP is significant for navigation safety.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Pore Water Pressure of Iron Concentrate Using Phase Space Reconstruction Long Short-Term Memory

Li,

Zhou,

Zhang

et al. 2024

Frontiers in Artificial Intelligence and Applications

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Ship shaking can cause cargo PWP (pore water pressure) increasing, the prediction of PWP is helpful for navigation safety. This paper presents a machine learning method for PWP time series prediction learning. Source data of iron concentrate are collected from scaled model test, they are used to train a time series prediction model and test the accuracy and effective of our method. The proposed method is based on PSR (phase space reconstruction) and LSTM (Long Short-Term Memory) Network. A input matrix is constructed by phase space reconstruction technology and the prediction model can be learned by a specific Long Short-Term Memory Network. The single-step pore water pressure prediction model is achieved, when MSE loss function value is minimum, the R value 0.98 is maximum is better than the baseline R value 0.93. This result suggests that this PSR-LSTM is more effective than LSTM, it can be a complement for physical test and numerical simulation.

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“…Because of the strong effect of waves in shallow water, waves are one of the key processes of causing sediment resuspension (Booth et al., 2000), which may lead to the release of buried contaminants into the water (Chatelain & Guizien, 2010; G. Xu et al., 2018). Storm waves may even cause a series of coastal hazards, such as coastal erosion (S. Zhang et al., 2022b), seabed liquefaction (Cheng et al., 2023; S. Zhang et al., 2018), and even submarine landslides (Z. Wang et al., 2020). On the other hand, waves are also good providers of energy.…”

Section: Introductionmentioning

confidence: 99%

“…Xu et al, 2018). Storm waves may even cause a series of coastal hazards, such as coastal erosion (S. Zhang et al, 2022b), seabed liquefaction (Cheng et al, 2023;, and even submarine landslides (Z. Wang et al, 2020).…”

mentioning

confidence: 99%

Improved Prediction of Local Significant Wave Height by Considering the Memory of Past Winds

Zhang

Yang

Zhang

et al. 2023

Water Resources Research

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Wave and water depth were measured with an instrumented tripod in the Yellow River Delta from 9 December 2014 to 29 April 2015. Concurrent wind data were also collected from a nearby wind station. A high‐precision model for predicting local significant wave height (Hs) with wind speed (vw) is constructed using an improved data‐driven approach. The proposed model realized high accuracy as it solves the problem that the Hs falls too fast during the wind‐decreasing periods. It was tackled by considering the remaining influence of historical vw on the present Hs via incorporating a memory curve of the past wind effect. This innovative approach significantly improves the prediction (R2 from 0.60 to 0.83). The winds in the past 24 hr still left an influence on the waves at the observation site although the influence decreases with time. Physically, it is an implicit but simpler consideration of wind fetch/duration. Further data modeling experiments indicated that the decisive factor for the Hs at the site is the wind speed. Wind directions slightly improve the prediction, indicating that waves are slightly affected by the underwater seabed slope along different wind directions, and northwest winds cause the strongest waves at the site. Adding atmospheric pressure or water depth even reduces the accuracy, which indicated that storm surges and wave deformations under different tide levels have a weak impact on Hs. The proposed local wave model can be easily constructed with available wind and wave data, making it expandable to other regions dominated by wind waves.

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Prediction of irregular wave (current)-induced pore water pressure around monopile using machine learning methods

Cited by 6 publications

References 55 publications

Systematic Literature Review on Smoothed Particle Hydrodynamic Method for Coastal Protection Optimizing

Systematic Literature Review on Smoothed Particle Hydrodynamic Method for Coastal Protection Optimizing

Prediction of Pore Water Pressure of Iron Concentrate Using Phase Space Reconstruction Long Short-Term Memory

Improved Prediction of Local Significant Wave Height by Considering the Memory of Past Winds

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