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The aim of the present research is to propose a hybrid model for human error probability analysis based on Cognitive Reliability and Error Analysis Method (CREAM) and Systematic Human Error Reduction and Prediction Approach (SHERPA) Method. The model aims to provide a theoretical framework to understand human behavior and to predict error probability. This model is based on a fundamental distinction between competence and control that offers a way of describing how performance depends on context. The most important result of this work is to provide a model that can be used in the preventive phase of an accident in order to mitigate the damages

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Over the years, many techniques have been developed for human reliability analysis (HRA). The main weakness of traditional HRA approaches is the use of a simple classification scheme without a link to a model of cognition in terms of mental processes. The present work is an attempt in this direction through a particular hybrid probabilistic model. The human error in industrial emergency model aims to develop an integrated methodological approach useful in critical infrastructures during an emergency condition. The proposed method, starting from the integration of existing techniques, develops a very flexible tool, able to take into account the main external and internal factors responsible of human error in emergency conditions. The model is able to estimate the evolution of human behavior and error following the evolution of the emergency scenario. The final result is a simulation model that calculates the contextualized human error probability, through which it is possible to estimate a realistic and detailed scenario of the conditions during the emergency management

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Hospitals play a critical role in providing communities with essential medical care during all types of disasters. Any accident that damages systems or people often requires a multifunctional response and recovery effort. Without an appropriate emergency planning, it is impossible to provide good care during a critical event. In fact, during a disaster condition, the same "critical" severity could occur for patients. Thus, it is essential to categorize and to prioritize patients with the aim to provide the best care to as many patients as possible with the available resources. Triage assesses the severity of patients to give an order of medical visit. The purpose of the present research is to develop a hybrid algorithm, called triage algorithm for emergency management (TAEM). The goal is twofold: First, to assess the priority of treatment; second, to assess in which hospital it is preferable to conduct patients. The triage models proposed in the literature are qualitative. The proposed algorithm aims to cover this gap. The model presented exceeds the limits of literature by developing a quantitative algorithm, which performs a numerical index. The hybrid model is implemented in a real scenario concerning the accident management in a petrochemical plant.