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In today's interconnected global economy, maritime trade is a pillar of prosperity, yet maritime accidents loom as a formidable challenge. The intricate nature of these accidents, coupled with rapid technological advancements, necessitates the evolution of systematic analysis methods. Conventional systemic approaches, while valuable, struggle to encapsulate the intricate web of mutual and dynamic dependencies inherent in these incidents. Furthermore, the call for more quantitative support in decision‐making and the ability to account for emergent factors has become increasingly imperative. This study aims to analyze maritime accidents by introducing a quantitative and dynamic model. The endeavour begins with establishing an extended Accident Map‐based model, a robust framework that unveils a sophisticated accident causation model. This preliminary action establishes the groundwork for integrating an innovative Spherical Fuzzy Set, navigating the complex landscape of knowledge acquisition. The subsequent phase charts a transformative course by mapping the model onto a dynamic Bayesian Network to conduct a forward and backward analysis. The essence of the model lies in its dynamic nature, allowing for real‐time updates that reflect the evolving maritime accidents risk factors. The approach is validated through a partial benchmark exercise, a reality check, an independent peer review, and a sensitivity analysis. The model can explore emerging contributing factors, reduce uncertainty, and consider relationships between factors that yield designing more effective safety measures. [ABSTRACT FROM AUTHOR]

Human factors analysis (HFA) has been explored from various aspects (e.g., engineering, psychology, physiology, and ergonomics). Numerous conventional techniques have been developed and applied to improve system safety from the human perspective. However, emerging sociotechnical systems, industry 4.0, and the use of artificial intelligence-driven systems reveal these methods' incapability. This necessity is developing intelligent approaches that account for integrating artificial intelligence (AI) into human factors. This work reviewed the integration of artificial intelligence and expert systems into HFA. It primarily focused on using machine and deep learning and knowledge/data-driven modeling approaches to HFA. Accordingly, this systematic review investigated the applications, contributions, challenges, and research gaps of HFA in complex systems. We analyzed seven vital elements of HFA to illustrate these concerns. This work also highlighted important myths, misapplications, and critical concerns that need to be addressed using advanced approaches. [ABSTRACT FROM AUTHOR]

The performance of sociotechnical elements varies owing to a wide range of endogenous and exogenous influencing factors. These are called uncoupled variability as per Safety-II. The uncoupled variability has drawn rare attention, despite its vital importance in major accidents analysis as per Safety-I and Safety-II paradigms. Accordingly, as the first attempt, this study proposes a systematic model to analyze performance variability in human, organizational, and technology-oriented functions caused by various variability shaping factors (VSFs). The model contains three main phases. First, a FRAM (Functional Resonance Analysis Method) - driven Human-Organization-Technology Taxonomy is developed. Subsequently, Dempster - Shafer Evidence theory is employed to elicit knowledge under epistemic uncertainty. The proposed causation model is integrated into Dynamic Bayesian Networks to support decision-making under aleatory uncertainty. Finally, a criticality matrix is developed to evaluate the performance of the system functions to support decision-making. The proposed model is built considering the advanced canonical probabilistic approaches (e.g., Noisy Max and Leaky models) that address the critical challenges of incomplete and imprecise data. The proposed dynamic model would help better understand, analyze, and improve the safety performance of complex sociotechnical systems. [ABSTRACT FROM AUTHOR]

• A hybrid system theory-based model is proposed by integrating STAMP into Fuzzy logic and DEMATEL. • The proposed model was applied to analyze real accidents in the process industry to indicate its application and effectiveness. • It provides quantitative support for the decision-making process and establishes a rigorous cause-effect model. • It also reveals safety investment control actions and handles uncertainty in accident analysis in complex systems. This paper presents a novel framework that combines the Systems-Theoretic Accident Model and Processes (STAMP) with Decision Making Trial and Evaluation Laboratory (DEMATEL) and Fuzzy logic to achieve a quantitative non-linear accident analysis methodology. The proposed methodology consists of two primary stages. Firstly, a qualitative analysis of a fatal accident is conducted using STAMP. The accident's control structure is constructed, and unsafe control actions (UCAs) are pinpointed. Secondly, the Fuzzy-DEMATEL approach is employed to quantify and determine the relationships between different control levels. A diverse pool of experts' opinions is leveraged to assess the impact of each control level on others and accident scenarios. The findings reveal that various interactions between Operation, HSE, Business, Contracting, Firefighting, and Management levels contributed to the occurrence of the accidents. The integrated approach not only addressed qualitative nature of STAMP, but also elucidates the cause-effect relationships among control levels. The significance of this study lies in its ability to not only analyze past accidents but also proactively examine the system and identify hazardous control levels before accidents occur. By prioritizing control levels and understanding their interconnections, organizations can implement targeted safety measures to prevent accidents and enhance overall system reliability and resilience. Its effectiveness in quantifying data, identifying unsafe control actions, and establishing cause-effect relationships make it a valuable tool for enhancing safety in various complex socio-technical systems beyond the process industry. [ABSTRACT FROM AUTHOR]

• A systematic approach to identify and quantify PSFs importance on the performance of complex sociotechnical systems is proposed. • A holistic PSFs Taxonomy based on sociotechnical systems design is proposed. • The proposed model's capability is tested on maintenance operations in the chemical process plants. • This study helps to allocate safety measures to improve resilience and safety performance. The safety performance of sociotechnical systems (STSs) and their main components (e.g., human, organization, and technology) vary due to numerous performance shaping factors (PSFs). However, previous research mainly focused on studying limited PSFs related to human functions, while organization and technology functions have often been ignored. This paper aims to propose a systematic approach to identify PSFs, quantify their importance level and influence on the performance of complex sociotechnical systems' functions. To this end, we first developed a holistic PSFs Taxonomy based on sociotechnical systems design and then employed novel Interval-Valued Spherical Fuzzy Sets (IVSFS) and Best Worst Method to quantify the importance of performance. We tested the proposed model's capability on maintenance operations in the chemical process plants and compared the model with the previous research considering fourteen criteria. The findings revealed the approach's effectiveness in dealing with epistemic uncertainty, vagueness, and fuzziness in the knowledge acquisition process. It revealed the critical safety investment factors among different sociotechnical elements and contributing factors to maintenance operations. This helps to effectively allocate safety countermeasures to improve resilience and system safety performance. [ABSTRACT FROM AUTHOR]