Prediction of scour in plunge pools below outlet bucket using artificial intelligence

Lashkar-Ara, Babak; Ghotbi, Samira; Najafi, Leila Haj

doi:10.1007/s12205-016-1523-0

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…However, edge scour still continues to occur despite the foundation protective devices installed [33]. With the advancement of artificial intelligence (AI) technology, machine learning (ML) and deep learning (DL) will have a better contribution to offshore wind turbine condition monitoring [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. Artificial intelligence (AI) is basically an algorithm for regression analysis of existing big data rules which include machine learning, deep learning, genetic algorithm, neural network, and fuzzy.…”

Section: Introductionmentioning

confidence: 99%

Visible Light Communication System for Offshore Wind Turbine Foundation Scour Early Warning Monitoring

Lin

Chang

et al. 2019

Water

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Offshore wind farms have a superior wind source to terrestrial wind farms, but they also face more severe environmental conditions such as severe storms, typhoons, and sea waves. Scour leads to the excavation of sediments around the foundations of structures, reducing the safe capacity of the structures. The phenomenon of pier scour is extremely complex because of the combined effects of the vortex system involving time-dependent flow patterns and sediment transport mechanisms. A real-time scour monitoring system can improve the safety of structures and afford cost-effective operations by preventing premature or unnecessary maintenance. This paper proposes an on-site scour monitoring system using visible light communication (VLC) modules for offshore wind turbine installations. A flume experiment revealed that the system was highly sensitive and accurate in monitoring seabed scour processes. This arrayed-VLC sensory system, proposed in this paper, has considerable potential for safety monitoring and also can contribute to improving the accuracy of empirical scour formulas for sustainable maintenance in the life cycle of offshore structures.

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Section: Introductionmentioning

confidence: 99%

Visible Light Communication System for Offshore Wind Turbine Foundation Scour Early Warning Monitoring

Lin

Chang

et al. 2019

Water

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Scour Depth Evaluation of a Bridge with a Complex Pier Foundation

Liao

Muto

Lin³

2017

KSCE J Civ Eng

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Prediction of scour hole characteristics caused by water jets using metaheuristic artificial bee colony-optimized neural network and pre-processing techniques

Kartal,

Emiroglu,

Katipoglu

et al. 2023

Journal of Hydroinformatics

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Preventing plunge pool scouring in hydraulic structures is crucial in hydraulic engineering. Although many studies have been conducted experimentally to determine relationship between the scour depth and water jets in several fields, available equations have deficiencies in calculating the exact scour due to complexity of the scour process. This study investigated local scour depth in plunge pool using metaheuristic artificial bee colony-optimized feed-forward neural network (ABC-FFNN), variational mode decomposition (VMD), and ensemble empirical mode decomposition (EEMD) techniques. To set modeling, the input parameters are impact angle, densimetric Froude number, impingement length, and nozzle diameter. The models' training and testing were conducted using data available in the literature. The models' performances were compared with experiments. The results demonstrate that scour depth, length, width, and ridge height can be calculated more accurately than the available equations. A rank analysis was also applied to obtain the most critical parameter in predicting scour parameters in water jet scouring. ABC-FFNN, VMD-ABC-FFNN, and EEMD-VMD-FFNN hybrid models were performed to obtain scour parameters. As a result, ABC-FFNN algorithms produced the best solution to predict the scour due to circular water jets, with the values for training (R2: 0.331–0.778) and testing (R2: 0.495–0.863).

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Prediction of scour in plunge pools below outlet bucket using artificial intelligence

Cited by 3 publications

References 12 publications

Visible Light Communication System for Offshore Wind Turbine Foundation Scour Early Warning Monitoring

Visible Light Communication System for Offshore Wind Turbine Foundation Scour Early Warning Monitoring

Scour Depth Evaluation of a Bridge with a Complex Pier Foundation

Prediction of scour hole characteristics caused by water jets using metaheuristic artificial bee colony-optimized neural network and pre-processing techniques

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