8 research outputs found
System theory and human factors hazard identification approach for marine survey operation
Get PDFMarine survey operation is one of the most frequent and essential activities in enhancing ocean knowledge. Some dangerous tasks and activities involve scientists, marine crews, survey equipment, and sensors, such as deploying equipment near the seabed, collecting the sediment sample, and towing the equipment with the ship’s movement. Since it consists of several controllers and components, the comprehensive system theory must be applied to analyse the risk, and the effect of human error must be incorporated as the equipment’s controller within the system. This study provides a holistic hazard identiMication of marine survey operation by using System Theory Process and Analysis (STPA) and integrating it with the Human Factors ClassiMication System (HFACS) to deMine the unsafe control actions (UCA) and failure scenarios. According to the STPA, 194 UCAs could occur. The main causal factors of the UCAs are human and followed by technical errors. The STPA-HFACS analysis indicated that additional human and equipment actions would have a detrimental effect on the failures of the operation. This study will beneMit stakeholders in marine survey operations with an alternative method for risk analysis
Developed Methodology for Ship Retrofitting (Case Study: RV Baruna Jaya I)
Get PDFShip retrofitting is one alternative solution to extend a ship’s life. Several component replacements were performed in the retrofitting process, remodelings to the ship’s main dimension, and state-of-the-art equipment and technology were added. As one of the ships that require regular renewal, especially in research equipment, retrofit processes are often carried out on research vessels. The current problem in the retrofit process for research vessels, especially in Indonesia, is that retrofits are not carried out with established methodology and planning. Thus, some retrofit projects are limited to major repairs and do not extend the ship’s life or performance. To solve that problem, a developed methodology for ship retrofitting was proposed, which consists of selecting the ship and the type of retrofit and its components, determining retrofit requirements, and designing the ship based on the retrofit project. In the design process, a modified spiral design is explicitly used for the retrofit process with some alterations compared with the conventional one, which is focused on analyzing the suitability and availability of space, consumable and tank requirements, analysis of power requirements, risk analysis, and project execution plan. A case study of this developed methodology has been undertaken in the concept design phase of RV Baruna Jaya 1’s retrofit, and the results show that the methodology is considered helpful as an approach for ship retrofitting. Moreover, several considerations were also obtained from the concept design stage and had to be analyzed at the following design stage to meet the retrofit design requirements
Assessing the risk of transporting battery electric vehicles through water transportation modes by integrating system theory and Bayesian network approach
Get PDFThe growing use of Battery Electric Vehicles (BEVs) has increased the need for their transportation via sea routes. This study conducts a comprehensive safety assessment of BEV transport on ferries using the System Theoretic Process Analysis (STPA) to identify Unsafe Control Actions (UCAs) and potential loss scenarios. A Bayesian Network (BN) model is developed to evaluate the causal relationships between contributing factors, while fuzzy set theory is applied to quantify failure and conditional probabilities based on expert input. Four key subsystems influence the safety of BEV transports, including marine crew, fire safety, port-related activities, and EV's batteries. The analysis highlights component failures, process model flaws, and inadequate system feedback as key contributors to UCAs. Sensitivity analysis reveals that lack of knowledge is the most influential factor leading to human, asset, and environmental losses. Furthermore, management factors, specifically, lack of procedures and rules, were found to be significant causal contributors to potential losses and accidents. These findings provide crucial insights to support the development of safety standards and regulations for the maritime transportation of BEVs
Assessing the accident severity level of passenger vessels in Indonesia using Bayesian Network model
Get PDFThe growing demand for passenger vessels has been paralleled by increased accidents, resulting in significant economic, human, and environmental losses. Accidents on passenger ships often stem from complex factors, including technical, operational, and human elements. Therefore, a detailed analysis is essential for understanding these factors and improving safety management. While various traditional risk analysis methods exist, the Bayesian Network (BN) offers unique advantages in modelling the probabilistic relationships between risk factors and accident outcomes. This study aims to analyse the accident severity level of passenger vessels in Indonesia by employing a Tree Augmented Naïve Bayesian Network (TAN-BN) to assess 46 passenger ship accidents in Indonesia using 17 identified Risk Influencing Factors (RIFs) focused on ship internal factors. Sensitivity analysis using mutual information and True Risk Influence (TRI) methods identified “Ship Operation” and “Accident Type” as the most significant RIFs, where the ship during passage is the most severe ship operation, and the ship sinking accident is the most catastrophic accident type. Scenario analysis revealed that very serious accidents often occur in transit, with human factors, particularly violation errors, playing a critical role. This study can leverage the decision-making process for stakeholders to reduce the severity of accidents in passenger vessels
The Road Safety: Utilising Machine Learning Approach for Predicting Fatality in Toll Road Accidents
Get PDFRoad safety is one of the critical government transportation concerns, especially on the toll roads. With the increasing number of toll roads as part of infrastructure planning, road traffic accidents are significantly escalating. Developing a system that predicts accidents on toll roads will benefit to reduce the harm that is caused by traffic accidents. This study will propose a method for analysing toll road accidents in Indonesia using historical toll road accident data as a dataset to become a pattern to examine the frequency of accidents. This dataset consists of various parameters from three main factors that cause accidents: human, environmental, and road infrastructure factors. Machine learning technique will be mainly used to determine the most influencing factors by employing classifiers such as Logistic Regression (LR), Decision Tree (DT), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbors (KNN) can construct the prediction model. Fourteen subfactors from the data were used to predict the future fatalities caused by accidents, which allowed the system to forecast the accident fatality. The results show accuracy performance on the test set with LR, DT, KNN, and GNB models, 85.3%, 79.4%, 87.1%, and 77.1%, respectively. The KNN Classifier model has the most minor error value of 0.6 compared to the other models. The study’s findings will help analyse the causal factors involved in toll road accidents and could be utilised by road authorities to employ risk control options to mitigate the ramifications
A Systematic Literature Review of Risk Assessment Methodologies for Battery Electric Vehicles
Get PDFThis systematic literature review investigates risk assessment methodologies for Battery Electric Vehicles (BEVs), highlighting their diversity and effectiveness in addressing emerging safety challenges. With the rapid global adoption of BEVs, there is an increasing need for robust methodologies to assess risks such as thermal runaway (TR), degradation, and operational failures. This review highlights techniques such as fuzzy failure mode and effect analysis (FMEA), hybrid neural networks, bayesian networks (BN), and entropy weight methods. These tools effectively identify and mitigate risks; however, they face challenges in providing holistic, system-level safety assessments and adapting to long-term, real-world conditions. Unlike previous works, this study integrates interdependent BEV subsystems into unified risk models and examines underexplored areas such as maritime transport safety. The transport of BEVs by vessels presents unique risks, including high humidity and confined cargo spaces, which intensify the battery safety challenges. Tools like FMEA and real-time monitoring systems are critical to mitigate these risks. The findings highlight the growing reliance on real-time diagnostics and advanced algorithms for enhancing BEV safety and reliability. By identifying gaps and proposing recommendations, this review aims to support the development of standardized frameworks to ensure BEV safety across various environments and operational scenarios, contributing to their continued global adoption
Analysing the Impact of Human Error on the Severity of Truck Accidents through HFACS and Bayesian Network Models
No full textTruck accidents are a prevalent global issue resulting in substantial economic losses and human lives. One of the principal contributing factors to these accidents is driver error. While analysing human error, it is important to thoroughly examine the truck’s condition, the drivers, external circumstances, the trucking company, and regulatory factors. Therefore, this study aimed to illustrate the application of HFACS (Human Factor Classification System) to examine the causal factors behind the unsafe behaviors of drivers and the resulting accident consequences. Bayesian Network (BN) analysis was adopted to discern the relationships between failure modes within the HFACS framework. The result showed that driver violations had the most significant influence on fatalities and multiple-vehicle accidents. Furthermore, the backward inference with BN showed that the mechanical system malfunction significantly impacts driver operating error. The result of this analysis is valuable for regulators and trucking companies striving to mitigate the occurrence of truck accidents proactively
Risk Analysis of Equipment Loss During Marine Survey Operation by Integrating Fault Tree to Bayesian Network
Get PDFThe process of deploying and towing the survey equipment for several marine survey activities is essential since it visualises the seabed and improves data accuracy. Since the equipment is deployed to an underwater level, the risk arises with the deployment. These risks include potential contact with submerged objects and the seabed, which can result in the loss of equipment and have detrimental environmental consequences. This study aims to analyse the risk-associated factors related to the loss of survey equipment using Fault Tree Analysis (FTA) and Bayesian Network (BN). The constructed FTA was converted into BN to find the relationship between Basic events and simulate the probability of updating Basic events. The sensitivity analysis results of the BN model indicate that "Procedure Failure" is the Basic contributor to the loss of survey equipment. The findings from this study will have practical implications for stakeholders, enabling them to enhance the safety of marine survey activities, particularly by mitigating the occurrence of equipment loss during operational procedures
