Applied machine learning model comparison: Predicting offshore platform integrity with gradient boosting algorithms and neural networks

Dyer, Alec S.; Zaengle, Dakota; Nelson, Jake R.; Duran, Rodrigo; Wenzlick, Madison; Patrick, Wingo,; Bauer, Jennifer; Rose, Kelly; Romeo, Lucy

doi:10.1016/j.marstruc.2021.103152

Cited by 23 publications

(7 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the last proposed machine learning method, we employed the Gradient Boosting Algorithm (GBA) for digitalizing cancer based on the dataset [ 33 , 34 ]. The GBA is a powerful supervised machine learning technique that has been proven to be effective in analyzing and addressing complex problems.…”

Section: Resultsmentioning

confidence: 99%

Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer

et al. 2023

View full text Add to dashboard Cite

Medical digital twins, which represent medical assets, play a crucial role in connecting the physical world to the metaverse, enabling patients to access virtual medical services and experience immersive interactions with the real world. One serious disease that can be diagnosed and treated using this technology is cancer. However, the digitalization of such diseases for use in the metaverse is a highly complex process. To address this, this study aims to use machine learning (ML) techniques to create real-time and reliable digital twins of cancer for diagnostic and therapeutic purposes. The study focuses on four classical ML techniques that are simple and fast for medical specialists without extensive Artificial Intelligence (AI) knowledge, and meet the requirements of the Internet of Medical Things (IoMT) in terms of latency and cost. The case study focuses on breast cancer (BC), the second most prevalent form of cancer worldwide. The study also presents a comprehensive conceptual framework to illustrate the process of creating digital twins of cancer, and demonstrates the feasibility and reliability of these digital twins in monitoring, diagnosing, and predicting medical parameters.

show abstract

Section: Resultsmentioning

confidence: 99%

Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer

et al. 2023

View full text Add to dashboard Cite

show abstract

“…where ∆w is the difference of densities between water and oil equal to 0.06341; t represents the time of oil spill with a value of 2400 s and U is equal to 30 m/s. Deferred production cost is estimated as follows [60]: 20) where DL is equal to 30 years [28]; C C is the price of crude oil, equal to 92 USD/barrel; U C represents the economic benefit of selling, equal to 12%; T RP is the time to recover the product, with a value of four years. The annual discount rate, γ m , is equal to 6% [21].…”

Section: Optimal Time Instantmentioning

confidence: 99%

“…Ref. [20] develop maintenance strategies for offshore systems using artificial neural networks to identify life extension opportunities. Ref.…”

Section: Introductionmentioning

confidence: 99%

Cost–Benefit Assessment of Offshore Structures Considering Structural Deterioration

Tolentino

2023

JMSE

View full text Add to dashboard Cite

Offshore facilities are essential infrastructure systems for many nations because their partial or total interruption causes diverse consequences in the economic, political, environmental, and social sectors. With the aim to preserve such structures at acceptable reliability levels, an approach is proposed to calculate the optimal instant of time in which inspection and maintenance works can be performed. The optimal time instant is estimated following the cost benefit criterion (CB) considering the cost of inspection, repair and failure. The inspection cost is given by an inspection quality, while fatigue crack size at different critical joints is calculated to estimate repair costs. In this paper, the concept of demand exceedance rates is introduced to evaluate the failure cost. Uncertainties related to both storm and operational waves are considered. The optimal time instant is associated with the lowest cost of inspection, repair and failure. For this purpose, the approach is exemplified in an offshore jacket structure situated in the Gulf of Mexico. The optimal instant of time corresponds to 6 years after the offshore jacket installation. If maintenance actions are implemented every six years during the lifespan of the system, an economic reduction of 58% is achieved, compared to the case in which no inspection and maintenance works are performed over time. The approach helps decision-makers ensure the best use of economic resources.

show abstract

“…However, during earthquakes and ocean waves, [17] employed a particular inertial damper to mitigate vibration on jacket offshore platforms. Furthermore, the method has been able to classify and predict the damage to the structure via the service of gathering neuron networks in vibration-based harm assessment for construction structures [21][22][23]. The algorithm of a one-dimensional CNN automatically extracts damage-sensitive features from raw strain response data of a structure under specific excitation conditions without the need for manually generated feature extraction [24].…”

Section: Introductionmentioning

confidence: 99%

Early State Prediction Model for Offshore Jacket Platform Structural Using EfficientNet-B0 Neural Network

Ho,

Do,

Dang

et al. 2024

EAI Endorsed Trans Ind Net Intel Syst

View full text Add to dashboard Cite

Offshore Jacket Platforms (OJPs) are often affected by environmental components that lead to damage, and the early detection system can help prevent serious failures, ensuring safe operations and mining conditions, and reducing maintenance costs. In this study, we proposed a prediction model based on Convolutional Neural Networks (CNNs) aimed at determining the early stage of the OJP structure’s abnormal status. Additionally, the EfficientNet-B0 Deep Neural Network classifies normal and abnormal states, which may cause problems, by using displacement signal analysis at specific areas taken into account throughout the test. Displacement data is transferred to a 2D scalogram image by applying a continuous Wavelet converter that shows the state of the work. Finally, the scalogram image data set is used as the input of the neural network, and feasibility experimental results compared with other typical neural networks such as GoogLeNet and ResNet-50 have verified the effectiveness of the approach.

show abstract

Applied machine learning model comparison: Predicting offshore platform integrity with gradient boosting algorithms and neural networks

Cited by 23 publications

References 58 publications

Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer

Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer

Cost–Benefit Assessment of Offshore Structures Considering Structural Deterioration

Early State Prediction Model for Offshore Jacket Platform Structural Using EfficientNet-B0 Neural Network

Contact Info

Product

Resources

About