Mathematical modelling and hybrid strategies for risk and uncertainty management

Abstract

Risk management is a decision-making process that takes into consideration political, social, economic and engineering factors, with relevant risk assessments relating to a potential hazard. Mathematics uses reasoned theories, computational techniques, algorithms, and the latest computer technologies to solve the problems arising from various fields, such as economics, engineering, business and social sciences. Mathematics also focuses on problems coming from the industry, and creates solutions relevant to the industry, including finding the most efficient and cost-effective way to solve these problems. It allows the development, analysis and comparison of regulatory options, as well as the selection of the optimal regulatory response for safety from that hazard. Reliability engineering and risk management have been attracting increasing amounts of attention, and are of growing importance in civil, mechanical, aerospace and aeronautics, offshore and marine engineering, as well as in many other disciplines of engineering. Against this background, the aim of this Special Issue is to bring scientists all over the world together to present their research on innovative methodologies, and the practical applications of these technologies in the field of reliability engineering and risk management. Emerging concepts, as well as the state of the art and novel applications of reliability principles and risk-based decision-making in all types of structures, infrastructures and mechanical systems, will be emphasized within the scope of the issue. In this context, articles on theories, methods, algorithms and applications are all welcome. This special issue focuses on the characterization of uncertainties and the development of risk assessment models for the essential infrastructures in different areas of life. Both practical applications for reliability-based design code calibration and quantitative risk assessment, and novel methodological developments, including emerging areas, such as machine learning and artificial intelligence, are included to provide a reliable method for multifaceted risk assessment. We invite you to contribute and submit your latest research work.