Development of Stability Formulas for Rock Armor and Tetrapods Using Multigene Genetic Programming

Lee, Jae-Sung; Suh, Kyung-Duck

doi:10.1061/(asce)ww.1943-5460.0000540

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…The advantage of MGGP in comparison with nonlinear regression models is that the structure of a prediction model does not require to be assumed in advance (24,25). To be more precise, both structure and parameters of a prediction model can be achieved by MGGP.…”

Section: Discussionmentioning

confidence: 99%

“…To be more precise, both structure and parameters of a prediction model can be achieved by MGGP. As a result, this advantage enables developing a prediction model without shape limitation, while the user can decide the trade-off between the accuracy and complexity of the prediction model by controlling over the maximum genes allowed in each individual and depth of trees in MGGP (25).…”

Section: Discussionmentioning

confidence: 99%

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COVID-19 Outbreak: Application of Multi-gene Genetic Programming to Country-based Prediction Models

Niazkar

2020

Electron J Gen Med

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Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is a novel coronavirus that has infected more than 2,900,000 individuals worldwide. The widespread of coronavirus 2019 (COVID-19) brings about the need for a prediction model to adopt appropriate evidence-based strategies. In this study, multi-gene genetic programming (MGGP), as one of the artificial intelligence models, has been proposed for the first time for predicting the COVID-19 outbreak. Although this is a challenging task due to significant fluctuations of daily confirmed cases, the results achieved by MGGP are promising. To be more specific, the predicted confirmed cases are acceptably close to the observed values for seven countries considered in this study. Thus, MGGP is suggested for developing estimation models of COVID-19. Furthermore, similarities and differences between the proposed prediction models are presented. Finally, it is discussed why a country-based prediction model is recommended.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

COVID-19 Outbreak: Application of Multi-gene Genetic Programming to Country-based Prediction Models

Niazkar

2020

Electron J Gen Med

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“…The stability coefficient K D is a parameter associated with the weight of the armor stone; it corresponds to the wave height initiating the damage and serves as a measure of stability under varying wave conditions. Because it is possible to simply examine the weight and stability of the armor stones, they have been widely used in the analysis of many hydrodynamic experiments (e.g., [23,24]). Hudson's equation for calculating the stability factor (K D ) is as follows:…”

Section: Breakwater Armor Stone Damagementioning

confidence: 99%

Experimental Study on the Effect of Wind on Armor Stone Stability

Kim,

Lim,

Yoon

2024

JMSE

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Wind is a significant factor influencing the stability of breakwater armor stones. However, few existing studies have considered the effects of wind on these structures. In this study, two-dimensional laboratory experiments were conducted to examine the effect of wind on the stability of breakwater armor stones. The stability factor (KD) of the armor stone, fluid velocity, runup, and rundown were observed under the action of waves and winds. A wind turbine was installed in front of the physical model of the breakwater to generate extreme wind conditions of 5.5 and 12 m/s. The results showed that KD decreased by 42.18% at 5.5 m/s and 57.82% at 12 m/s compared with that without wind. The maximum runup and rundown heights increased with wind velocity, following a Rayleigh distribution. The fluid velocity distribution conformed to a normal distribution, with the mean velocity directed offshore. Many studies have suggested that runup, rundown, and fluid velocity are the main factors affecting the stability of breakwater armor stones. The analysis revealed that wind affects these factors and lowers the stability coefficient. These wind-induced hydrodynamic changes suggest the need for a detailed hydrodynamic review of wind-wave conditions.

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“…MGGP is a relatively new machine learning technology that evolved from genetic programming (GP). GP is derived from Darwin's evolutionary theory and imitates the natural selection and genetic mechanism of the biological world [62]. It is an extension of the genetic algorithm (GA).…”

Section: Multigene Genetic Programming (Mggp)mentioning

confidence: 99%

Fast Prediction of Solute Concentration Field in Rotationally Influenced Fluids Using a Parameter-Based Field Reconstruction Convolutional Neural Network

Yan

Mohammadian

Yu³

et al. 2023

Water

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Many high-performance fluid dynamic models do not consider fluids in a rotating environment and often require a significant amount of computational time. The current study proposes a novel parameter-based field reconstruction convolutional neural network (PFR-CNN) approach to model the solute concentration field in rotationally influenced fluids. A new three-dimensional (3D) numerical solver, TwoLiquidMixingCoriolisFoam, was implemented within the framework of OpenFOAM to simulate effluents subjected to the influence of rotation. Subsequently, the developed numerical solver was employed to conduct numerical experiments to generate numerical data. A PFR-CNN was designed to predict the concentration fields of neutrally buoyant effluents in rotating water bodies based on the Froude number (Fr) and Rossby number (Ro). The proposed PFR-CNN was trained and validated with a train-validation dataset. The predicted concentration fields for two additional tests demonstrated the good performance of the proposed approach, and the algorithm performed better than traditional approaches. This study offers a new 3D numerical solver, and a novel PFR-CNN approach can predict solute transport subjected to the effects of rotation in few seconds, and the PFR-CNN can significantly reduce the computational costs. The study can significantly advance the ability to model flow and solute transport processes, and the proposed CNN-based approach can potentially be employed to predict the spatial distribution of any physical variable in the lentic, ocean, and earth system.

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Development of Stability Formulas for Rock Armor and Tetrapods Using Multigene Genetic Programming

Cited by 9 publications

References 26 publications

COVID-19 Outbreak: Application of Multi-gene Genetic Programming to Country-based Prediction Models

COVID-19 Outbreak: Application of Multi-gene Genetic Programming to Country-based Prediction Models

Experimental Study on the Effect of Wind on Armor Stone Stability

Fast Prediction of Solute Concentration Field in Rotationally Influenced Fluids Using a Parameter-Based Field Reconstruction Convolutional Neural Network

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