Using Bayesian network-based TOPSIS to aid dynamic port state control detention risk control decision

Yang, Zhisen; Wan, Chengpeng; Yang, Zaili; Yu, Qing

doi:10.1016/j.ress.2021.107784

Cited by 44 publications

(14 citation statements)

References 49 publications

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“…For example, Yang et al introduced data-driven Bayesian networks into the TOPSIS method. A new ship detention risk assessment method and reasonable risk control strategy are proposed [ 22 ]. With the deepening of theoretical research on safety risk management, the research focus has gradually turned to the essence of safety risk.…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Twin-Based Risk Control during Prefabricated Building Hoisting Operations

Liu

Sun

et al. 2022

Sensors

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Prefabricated buildings have advantages when it comes to environmental protection. However, the dynamics and complexity of building hoisting operations bring significant safety risks. Existing research on hoisting safety risk lacks a real-time information interaction mechanism and lacks scientific control decision-making tools based on considering the correlation between safety risks. Digital twin (DT) has the advantage of real-time interaction. This paper presents a safety risk control framework for controlling prefabricated building hoisting operations based on DT. In the case of considering the correlation of the safety risk index of hoisting, the safety risk hierarchy model of hoisting is defined in the process of building the DT model. The authors have established a Bayesian network model into the process of the integrated analysis of the digital twin mechanism model and monitoring data to realize the visualization of the decision analysis process of hoisting safety risk control. The key degree of the indirect inducement variable to direct inducement variable was calculated according to probability. The key factor leading to the occurrence of risk was found. The effectiveness of the hoisting safety risk control method is verified by a large, prefabricated building project. This method provides decision tools for hoisting safety risk control, assists in formulating effective control schemes, and improves the efficiency of information integration and sharing.

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Section: Literature Reviewmentioning

confidence: 99%

Digital Twin-Based Risk Control during Prefabricated Building Hoisting Operations

Liu

Sun

et al. 2022

Sensors

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“…Much of the current literature on PSC overall evaluation and analysis pays particular attention to explore the causes leading to the discrepancy and inconsistency in ship selection criteria and onboard inspection process within different PSC regimes, the member states of one MoU, and even the PSCOs at the same port ( Yan et al, 2020 ; Yang et al, 2021b ). Knapp and Franses (2007) and Knapp and Van de Velden (2009) both showed that the treatment of vessels across the MoUs was inconsistent based on more than 180k inspection records in more than 50 individual port states over the world.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Such issues and challenges included but were not limited to discrepancies in the capacity, contribution, quality, and performance among the ports as well as harmonization of the member states within the Tokyo MoU. In addition to proposing measures to harmonize PSC inspection, the dependency and interdependency among influencing factors are analyzed ( Wang et al, 2021 ) and approaches to rationalizing detention decision ( Yang et al, 2021 ) are proposed in existing literature to improve PSC inspection.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A deficiency is a condition violating the regulations or conventions found onboard a ship by the inspectors who are also called PSC officers (PSCOs). If any serious/fatal deficiency is found onboard a ship by a PSCO, he/she can take a mandatory intervention action named detention to ensure that the hazard is addressed before the ship is allowed to leave the port ( Yang et al, 2021c ). After launching in 1982, nine regional agreements on PSC, which are called Memorandum of Understandings or MoUs, have been signed globally: Paris MoU, Tokyo MoU, Acuerdo de Viña del Mar, Caribbean MoU, Abuja MoU, Black Sea MoU, Mediterranean MoU, Indian Ocean MoU, and Riyadh MoU.…”

Section: Introductionmentioning

confidence: 99%

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Is port state control influenced by the COVID-19? Evidence from inspection data

Yan

Guo

et al. 2022

Transport Policy

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“…First, we use real sales data of air purifiers to measure the public's protective consumption behavior, which is an improvement over previous studies that used self-reported behavioral intentions (Wu et al, 2018 ), as there is often a gap between intentions and real behavior (ElHaffar et al, 2020 ; Park & Lin, 2020 ). Second, this paper uses a data-driven approach to obtain the network structure and relationships among protective consumption behaviors and their influencing factors, which avoids the possible subjective limitations of previous subjective diagnosis based on the expert knowledge (Yang et al, 2021 ). Third, existing studies generally focus on considering one aspect that influences protective consumption behavior, such as marketing strategies from a firm-side perspective (Stanciu et al, 2020 ; Untaru & Han, 2021 ), risk perception on the public side (Mehiriz & Gosselin, 2022 ), but the protective consumption behavior of the public in the real world is influenced by multiple factors (Awan & Raza, 2012 ), which are not independent of each other.…”

Section: Introductionmentioning

confidence: 99%

Protective consumption behavior under smog: using a data-driven dynamic Bayesian network

Yuan

Fan

2022

Environ Dev Sustain

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In the midst of the deteriorating air pollution and collective stress, people pay close attention to risk mitigation measures such as keeping indoor and purchasing anti-smog products. Through impact evaluations, factors regarding health protective behavior can be identified. However, limited research is available regarding probabilistic interdependencies between the factors and protective behavior and largely relies on subjective diagnosis. These concerns have led us to adopt a data-driven static Bayesian network (BN) and Dynamic BN model to help explore multidimensional factors that may influence the public’s health protective behavior of buying anti-smog air purifiers and examine the dependencies among network nodes. Using the city-level aggregate data from an online shopping platform, the results shed new light on relationships existing among 11 factors and protective behavior of buying air purifiers. Furthermore, taking into account the dynamic nature of protective behavior, we add time-related factors on the basis of static BN to construct the dynamic BN model. Results indicate that PM2.5 concentration and product price are the two leading factors affecting the consumption behavior for air purifiers. Additionally, media-related factors play an important role in the consumption behavior. This study contributes to the fields of impact evaluation of protective consumption behavior analysis and links environment risk with public consumption by identifying key factors.

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Using Bayesian network-based TOPSIS to aid dynamic port state control detention risk control decision

Cited by 44 publications

References 49 publications

Digital Twin-Based Risk Control during Prefabricated Building Hoisting Operations

Digital Twin-Based Risk Control during Prefabricated Building Hoisting Operations

Is port state control influenced by the COVID-19? Evidence from inspection data

Protective consumption behavior under smog: using a data-driven dynamic Bayesian network

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