Stability Assessment of Rubble Mound Breakwaters Using Extreme Learning Machine Models

Wei, Xianglong; Liu, Huaixiang; She, Xiaojian; Lu, Yongjun; Liu, Xingnian; Mo, Siping

doi:10.3390/jmse7090312

Cited by 9 publications

(3 citation statements)

References 33 publications

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“…Their SOM-ELM outperformed single ELM and SOM-SVM on real data. Wei and Liu [189] tried to evaluate the stability of rubblemound breakwaters using ELM. They trained 5000 different ELMs to select the best one based on testing performance.…”

Section: Geography Applicationmentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

229

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Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

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Section: Geography Applicationmentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

229

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“…The breakwater stability is also under the influence of wave hydraulics and care must be taken in using the correct set of assumptions and formulas (Van der Meer, 1988;Van Gent et al, 2003). The stability assessment of a rubble mound breakwater using extreme models was presented by Wei et al (2019). The aim was to use a modeling approach in addition to empirical formulas to indicate the breakwater's damage level.…”

Section: Introductionmentioning

confidence: 99%

Investigation of performance of rock and artificial rubble mound breakwater armour using physical modelling and OpenFOAM

Safa,

Mojtahedi,

Yaghin

2024

Preprint

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The widely used traditional armour units might be promoted by more detailed analysis for better performance. This research investigates the stability, run-up and overtopping performances of three traditional armours, including rock, antifer and tetrapod layer of a rubble-mound breakwater by regular and irregular waves. The comparison is made by the results obtained from several verified numerical models in OpenFOAM library and OlaFOAM solver and also utilizes the common wave flume test procedure. The calibration process has two main parts, which include mesh solution and model turbulence. In this process, the optimal mesh and the most effective turbulence model were selected. The results indicate that the lowest and highest values of relative run-up and overtopping discharge were observed for rock armour and Antifer armour units, respectively. The amount of the relative run-up for tetrapod was slightly more than the rock armour. Also, it was observed that the measured stability parameter Ns on the armour unit was controlled. The results indicate that Antifer units can lead to a more stable armour layer than other units.

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“…Starting with studies that combine traditional and modern approaches for the evaluation of breakwater stability, ref. [1] presents two novel data-driven models based on the Extreme Machine Learning algorithm for the stability assessment of rubble-mound breakwaters, and compare their results to a well-established formula and two formulae derived from machine learning and genetic programming methods. On the same topic, ref.…”

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confidence: 99%