One of the greatest societal challenges is represented by Critical Infrastructures (CIs) protection. To minimize the impacts of man-made and natural threats, a series of risk assessment techniques have been developed. This work aims to critically compare stateof-the-art risk assessment methodologies for CIs protection, to find the pros and cons of each of them. The paper firstly defines the main challenges in performing the risk assessment of CIs, which have been identified in data availability and in modelling multiple hazard interactions. Afterwards, twelve different risk evaluation methodologies, including mathematical and statistical methods, machine learning techniques, graph and network methods, are analyzed and compared. Every method is described and its strengths and weaknesses are summarized in a suitable Table . Results show that statistical and mathematical methods provide the most accurate results, but need a large amount of data and execution time, while machine learning and complex network approaches work well even if the data are scarce and have a lower computational cost. In addition, the graph and network approaches tend to be the most flexible, able to adapt to every data availability condition and to deal with multiple hazards contemporarily.

Risk management has become an important concern in the light of current developments in the home energy management sector as well as within the broader considerations regarding the building sector’s energy production and consumption paradigm. The current multi-parameter energy ecosystem structure raises a number of new challenges that require a reliable and robust risk management framework to assist in building management decision making. This paper presents a multi asset risk assessment algorithm, which is part of a risk management application developed for residential buildings within the framework of energy communities and digital energy markets. It describes the logic, principles, and operation of the algorithm, as well as the functionalities related to risk analysis and result visualization. This underpins the necessary means to monitor elements of a home energy system as well as tools for risk prevention and mitigation. The proposed application provides accurate, detailed, and easy to use information to assist decision makers and stakeholders in the context of smart home energy management systems.

Scope of this work was the development of a model able to simulate the flows and behaviours of heterogeneous crowds in a large transport hub, both in normal conditions and during an emergency, like a terrorist attack. These places are indeed also so-called “soft targets”, public spaces which are preferred targets of terrorists because they provide them with the opportunity to maximize casualties and publicity. Different modelling approaches were investigated and finally agent-based modelling and the BDI (belief-desire-intention) architecture were selected. Several scenarios were also identified to simulate the crowd behaviour. Flowcharts were developed to model users’ actions and interactions; while statecharts to model emergency conditions and behavioural changes. Simulations were then used to identify weak points in the infrastructure and to analyse the evacuation times for each user category investigated. Finally, various solutions were proposed and simulated, to improve crowd flows and reduce evacuation times.