Assessment of Wave-induced Fatigue Damage to Marine Pipelines in Intermediate Seas

Yeganeh‐Bakhtiary, Abbas; Shabakhty, Naser; Valipour, Ali; Gol-Zaroudi, Hamzeh

doi:10.17736/ijope.2016.bn11

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, with advancements in data science and computational resources, Artificial Intelligence (AI) in the form of Machine Learning (ML) has been successfully employed to address a wide range of coastal engineering problems. For example, significant research relating to the development of AI based decision-support algorithms for the prediction of wave characteristics (Yeganeh-Bakhtiary et al, 2023) and wave overtopping at coastal defences has been undertaken (see, for example, den Bieman et al, 2021aBieman et al, , 2021bden Bieman et al, 2020;Elbisy, 2023;Elbisy and Elbisy, 2021;Habib et al, 2022b;Habib et al, 2023a;Habib et al, 2023b). Habib et al (2022a) has provided an overview of recent studies on the applications of ML approaches in coastal engineering problems.…”

Section: S Tmax H Smentioning

confidence: 99%

“…The final DT is constructed using the input features that distinctly divide these rectangular sections and yield the output variable with the smallest variance. DTs are commonly used in prediction tasks due to their ability to handle noise and non-linearity in input data independently (Pedregosa et al, 2011;Kotu and Deshpande, 2015;Yeganeh-Bakhtiary et al, 2023).…”

Section: Rf and Gbdtmentioning

confidence: 99%

See 1 more Smart Citation

Efficient data-driven machine learning models for scour depth predictions at sloping sea defences

Habib,

Abolfathi,

O’Sullivan

et al. 2024

Front. Built Environ.

View full text Add to dashboard Cite

Seawalls are critical defence infrastructures in coastal zones that protect hinterland areas from storm surges, wave overtopping and soil erosion hazards. Scouring at the toe of sea defences, caused by wave-induced accretion and erosion of bed material imposes a significant threat to the structural integrity of coastal infrastructures. Accurate prediction of scour depths is essential for appropriate and efficient design and maintenance of coastal structures, which serve to mitigate risks of structural failure through toe scouring. However, limited guidance and predictive tools are available for estimating toe scouring at sloping structures. In recent years, Artificial Intelligence and Machine Learning (ML) algorithms have gained interest, and although they underpin robust predictive models for many coastal engineering applications, such models have yet to be applied to scour prediction. Here we develop and present ML-based models for predicting toe scour depths at sloping seawall. Four ML algorithms, namely, Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Artificial Neural Networks (ANNs), and Support Vector Machine Regression (SVMR) are utilised. Comprehensive physical modelling measurement data is utilised to develop and validate the predictive models. A Novel framework for feature selection, feature importance, and hyperparameter tuning algorithms are adopted for pre- and post-processing steps of ML-based models. In-depth statistical analyses are proposed to evaluate the predictive performance of the proposed models. The results indicate a minimum of 80% prediction accuracy across all the algorithms tested in this study and overall, the SVMR produced the most accurate predictions with a Coefficient of Determination (r2) of 0.74 and a Mean Absolute Error (MAE) value of 0.17. The SVMR algorithm also offered most computationally efficient performance among the algorithms tested. The methodological framework proposed in this study can be applied to scouring datasets for rapid assessment of scour at coastal defence structures, facilitating model-informed decision-making.

show abstract

Section: S Tmax H Smentioning

confidence: 99%

Section: Rf and Gbdtmentioning

confidence: 99%

Efficient data-driven machine learning models for scour depth predictions at sloping sea defences

Habib,

Abolfathi,

O’Sullivan

et al. 2024

Front. Built Environ.

View full text Add to dashboard Cite

show abstract

“…In this study JONSWAP spectrum was employed, which is normally suitable for fetch limited waves:

S ((), f) = \frac{1.4}{γ} \frac{5}{16} H_{s}^{2} f_{p}^{4} f^{- 5} exp ([], - 1.25 {(f_{p} / f)}^{4}) γ^{exp ((), - \frac{{((), f - f_{p})}^{2}}{2 σ_{f}^{2} f_{p}^{2}})},

where

\begin{matrix} centertrue \\ σ_{f} = 0.10 & f \leq f_{p}, & σ_{f} = 0.50 & f > f_{p} \end{matrix}

and γ = 3.3. Then in order to generate the water surface elevation time series (Figure ) from the spectrum, we can use linear wave superposition method …”

Section: Excitation Loadsmentioning

confidence: 99%

Vibration control in wind turbines to achieve desired system-level performance under single and multiple hazard loadings

Rezaee

Aly

2018

Struct Control Health Monit

View full text Add to dashboard Cite

The increased demand in energy and the need for sustainable and renewable sources of electricity in hazardous environments with significantly growing population yields the installation of more wind turbines in these areas. In addition, the technological development in material and construction methods has led to the building of taller and more flexible turbines, with inherent low structural damping. Installing modern wind turbines in offshore harsh environments or seismic prone areas can cause an increment in the probability of failure due to excessive vibrations. The current study evaluates the performance of onshore and offshore wind turbines under multihazard loads, including wind, wave, earthquake, and mass and aerodynamic imbalances for both parked and operating conditions. The Lagrangian approach is employed to model the wind turbine considering the blade/tower coupling. In order to lessen the vibrations induced by multihazard loads, external smart dampers are used and a novel energy-based probabilistic approach is employed to tune the semiactive controllers. The results show the effectiveness of employing an analytical approach for the design of semiactive controllers in vibration mitigation of a wind turbine subjected to multiple hazards.

show abstract

A comparative assessment of the impact of tilting on structural safety and behavior of fixed offshore platforms

Karimi

Shabakhty

2023

Applied Ocean Research

View full text Add to dashboard Cite

Assessment of Wave-induced Fatigue Damage to Marine Pipelines in Intermediate Seas

Cited by 3 publications

References 0 publications

Efficient data-driven machine learning models for scour depth predictions at sloping sea defences

Efficient data-driven machine learning models for scour depth predictions at sloping sea defences

Vibration control in wind turbines to achieve desired system-level performance under single and multiple hazard loadings

A comparative assessment of the impact of tilting on structural safety and behavior of fixed offshore platforms

Contact Info

Product

Resources

About