Damage pattern and damage progression on breakwater roundheads under multidirectional waves

Comola, Francesco; Andersen, Thomas Lykke; Martinelli, Luca; Burcharth, Hans F.; Ruol, Piero

doi:10.1016/j.coastaleng.2013.09.004

Cited by 18 publications

(7 citation statements)

References 13 publications

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“…The sea waves parameters' prediction problems play an important role in many different ocean engineering tasks, such as the designing of marine structures like oil platforms or harbors or in the designing and management of marine energy systems, such as the proper operation of wave energy converters, among others [21][22]. The group method of data handling type neural network (GMDH-NN) was implemented in this investigation to predict the significant wave height using wind speed and fetch data.…”

Section: Discussionmentioning

confidence: 99%

Application of Group Method of Data Handling Type Neural Network for Significant Wave Height Prediction

Elbisy¹,

Osra²

2019

AJNNA

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The estimation of wave parameters is of great importance in coastal activities such as design studies for harbor, inshore and offshore structures, coastal erosion, sediment transport, and wave energy estimation. For this purpose, several models and approaches have been proposed to predict wave parameters, such as empirical, numerical-based approaches, and soft computing. In this study, the group method of data handling type neural network (GMDH-NN) was presented for significant wave height prediction in an attempt to suggest a new model with superior explanatory power and stability. The GMDH-NN results were compared with the field data and with a multilayer perceptron neural networks (MLPNN) model. The results indicate that the prediction accuracy and avoidance of over-fitting of the GMDH-NN method were superior to those of the MLPNN method. The percentage improvement in the root mean square error and the mean absolute percentage error of the GMDH-NN model over the MLPNN model were 72.92% and 81.02%, respectively. Also, according to the indices, the GMDH-NN model performs the best for predicting the Hs of all of the wave height ranges. That is, the GMDH-NN model is capable of predicting wave heights for different ranges. The results of the analysis suggest that the GMDH-NN-based modeling is effective in predicting significant wave height.

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Section: Discussionmentioning

confidence: 99%

Application of Group Method of Data Handling Type Neural Network for Significant Wave Height Prediction

Elbisy¹,

Osra²

2019

AJNNA

View full text Add to dashboard Cite

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“…The availability and accuracy of wave data play a crucial role in the better understanding of numerical (The WAMDI group, 1988;Tolman, 2009) and statistical wave models (Casas-Prat et al, 2014;Durrant et al, 2013), and in a number of marine-related activities such as the design of vessels and marine structures (oil platforms, breakwaters (Comola et al, 2014;Kim and Suh, 2014), wave overtopping volumes (Nørgaard et al, 2014), ports, etc. ), wave forecasting for safe ship navigation, or the design and operation of wave energy converters (López et al, 2013) to generate renewable electricity without * Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

A hybrid genetic algorithm—extreme learning machine approach for accurate significant wave height reconstruction

et al. 2015

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“…Concerning the measurement of the damage to the structure, a long time has passed since the pioneering work of Hudson (1959), who measured the crosssections by using a sounding rod equipped with a circular spirit level, a graduated scale and a ball-and-socket foot. Nowadays, in hydraulic laboratories the measurement of armour layer damage is generally performed through mechanical or laser profiling (Vidal et al, 2003;Cihan and Yuksel, 2013;van Gent, 2013van Gent, , 2014Guler et al, 2015) or visually by means of video cameras (Kramer et al, 2005;Vidal et al, 2006;Eslami and van Gent, 2010;Cihan and Yuksel, 2011;Comola et al, 2014;van Gent, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Such authors demonstrated that profiling techniques are more accurate for larger damages and that calibrated image analysis techniques can underestimate damage levels, particularly if the second layer is affected. It is worth noting that in order to understand the failure mechanism of the structure, it is important to consider not only damage levels but also damage progression (Comola et al, 2014;Martin-Hidalgo et al, 2014). Therefore dynamic measurements over large portions of the breakwater are necessary.…”

Section: Introductionmentioning

confidence: 99%

3-D monitoring of rubble mound breakwater damages

et al. 2018

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Breakwaters play a crucial role in the protection of coastal zones. Their maintenance is critical to safeguard the daily activities of harbours and marine areas. The evaluation of damage is a necessity for timely preservation works. Traditional monitoring methods span various techniques, ranging from mechanical profilers to optical systems. Current methods though are typically expensive, requiring remarkably sophisticated technologies which demand a high degree of expertise to be operated. In this paper, we propose an affordable yet accurate fully automated method based on 3D cameras. Our technique is non invasive, allowing hence non intrusive as well as fast measure of damage over time, simultaneously above and below sea water level. Experimental results obtained on laboratory breakwater models demonstrated that the proposed point cloud method, which does not depend on the imaging sensor and can be applied to any 3D dataset of rubble mound breakwater, can achieve accurate damage estimation, even when using a budget RGB-D camera. One of the additional advantages of using RGB-D cameras is the possibility to obtain measurements also in the presence of water.

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Damage pattern and damage progression on breakwater roundheads under multidirectional waves

Cited by 18 publications

References 13 publications

Application of Group Method of Data Handling Type Neural Network for Significant Wave Height Prediction

Application of Group Method of Data Handling Type Neural Network for Significant Wave Height Prediction

A hybrid genetic algorithm—extreme learning machine approach for accurate significant wave height reconstruction

3-D monitoring of rubble mound breakwater damages

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