A data-driven assessment model for collision responses of offshore platform structure with ship using hybrid intelligent approaches

Lin, Hong; Yang, Lei; Luan, Haochen; Han, Chang; Han, Pingping; Hao, Xu; Chen, Guo-Ming

doi:10.1016/j.psep.2022.09.008

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…Further, the evaluation result of port sustainability is an important reference by which to finish the corresponding task in short term decision-making, and an essential foundation to establish advanced facilities in long term decision-making because new investments are needed, and more technologies will be developed in the future. For sustainability, the technologies of offshore platforms [53][54][55] and offshore wind power [56][57][58][59] will possibly be useful to support the ongoing development in ports. These above technologies, respectively, involve floating structures and green energies for sustainability.…”

Section: Discussionmentioning

confidence: 99%

Extending Quality Function Deployment and Analytic Hierarchy Process under Interval-Valued Fuzzy Environment for Evaluating Port Sustainability

Wang

2023

Sustainability

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To confront the related problems of environmental protection, energy saving, and carbon reduction, sustainability has been a prominent issue for enterprises seeking to meet the requirements of the Earth Summit’ sustainable development goals (SDGs). Basically, sustainability evaluation of enterprises must be considered from environmental, social, and economic perspectives, recognized as quality requirements. Numerous enterprises, especially for international ports, must pay attention to these requirements in expressing their corporate social responsibility (CSR) for decreasing marine pollution. Practically, the three requirements may be dependent under uncertain environments, and rationally evaluated by fuzzy multi-criteria decision-making (FMCDM) with dependent evaluation criteria (DEC). In other words, evaluating port sustainability, containing location expanding, should belong to FMCDM with DEC. For DEC under uncertain environments, fuzzy extension of the analytic network process (ANP) is a feasible solution to solve the above problems. However, fuzzy computations of ANP are heavily complicated; thus, we desire to combine quality function deployment (QFD) with the analytic hierarchy process (AHP) under the interval-valued fuzzy environment (IVFE) into a hybrid method for evaluating port sustainability. In numerous multi-criteria decision-making (MCDM) efforts, AHP was often extended into FMCDM to encompass the imprecision and vagueness of data, but the extension was properly used for FMCDM with independent evaluation criteria (IDEC). Herein, QFD is utilized to express the dependent relationships between criteria, and thus transforms IDEC into DEC for the evaluation of port sustainability. Through the hybrid method, QFD is combined with AHP to replace ANP under IVFE, the complicated ties of ANP-corresponding interval-valued fuzzy numbers (IVFNs) are overcome, and the problem of evaluating port sustainability is rationally solved.

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Section: Discussionmentioning

confidence: 99%

Extending Quality Function Deployment and Analytic Hierarchy Process under Interval-Valued Fuzzy Environment for Evaluating Port Sustainability

Wang

2023

Sustainability

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“…Prior to the conduct of the GRA evaluation the original data of collision responses are transformed to benefit-type and standardized data [36]. Then, as stated in Section 2.6,…”

Section: Evaluations Of the Collision Reponsementioning

confidence: 99%

“…Prior to the conduct of the GRA evaluation the original data of collision responses are transformed to benefit-type and standardized data [36]. Then, as stated in Section 2.6, the grey relational analysis (GRA) evaluation is carried out for the preprocessed data.…”

Section: Evaluations Of the Collision Reponsementioning

confidence: 99%

“…However, due to its inherent nature as a gradient-based descent algorithm, the traditional BP-ANN model inevitably leads to some shortcomings such as local optimum and the restrictions of error function derivability. Therefore, by incorporating some global optimizing algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) into the training process of the BP algorithm, researchers have proposed a series of hybrid ANN models such as the GA-ANN model and PSO-ANN model and provided some useful results in the relevant literatures [30][31][32][33][34][35][36]. Li et al [35] used SSA-LSTM and SSA-BP ANN models to predict the maximum pitting corrosion depth of subsea oil pipelines.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [35] used SSA-LSTM and SSA-BP ANN models to predict the maximum pitting corrosion depth of subsea oil pipelines. Lin et al [36] proposed a hybrid ANN model to predict the collision responses of offshore platform structures by combining the DPSO algorithm with the BP-ANN model, which also proved that the DPSO-ANN model can be regarded as an effective alternative of the time-consuming FEA method.…”

Section: Introductionmentioning

confidence: 99%

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A Data-Driven Intelligent Prediction Approach for Collision Responses of Honeycomb Reinforced Pipe Pile of the Offshore Platform

Yang

Lin

Han

et al. 2023

JMSE

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The potential collision between the ship and the pipe piles of the jacket structure brings huge risks to the safety of an offshore platform. Due to their high energy-absorbing capacity, honeycomb structures have been widely used as impact protectors in various engineering applications. This paper proposes a data-driven intelligent approach for the prediction of the collision response of honeycomb-reinforced structures under ship collision. In the proposed model, the artificial neural network (ANN) is combined with the dynamic particle swarm optimization (DPSO) algorithm to predict the collision responses of honeycomb reinforced pipe piles, including the maximum collision depth (δmax) and maximum absorption energy (Emax). Furthermore, a data-driven evaluation method, known as grey relational analysis (GRA), is proposed to evaluate the collision responses of the honeycomb-reinforced pipe piles of offshore platforms. Results of the case study demonstrate the accuracy of the DPSO-BP-ANN model, with measured mean-square-error (MSE) of 5.06 × 10−4 and 4.35 × 10−3 and R2 of 0.9906 and 0.9963 for δmax and Emax, respectively. It is shown that the GRA method can provide a comprehensive evaluation of the performance of a honeycomb structure under impact loads. The proposed model provides a robust and efficient assessment tool for the safe design of offshore platforms under ship collisions.

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A novel method for the evaluation of ship berthing risk using AIS data

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Chu

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A data-driven assessment model for collision responses of offshore platform structure with ship using hybrid intelligent approaches

Cited by 7 publications

References 23 publications

Extending Quality Function Deployment and Analytic Hierarchy Process under Interval-Valued Fuzzy Environment for Evaluating Port Sustainability

Extending Quality Function Deployment and Analytic Hierarchy Process under Interval-Valued Fuzzy Environment for Evaluating Port Sustainability

A Data-Driven Intelligent Prediction Approach for Collision Responses of Honeycomb Reinforced Pipe Pile of the Offshore Platform

A novel method for the evaluation of ship berthing risk using AIS data

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