Theme 1: System reliability and safety, reliability-based design and optimization.

Complex systems and critical infrastructures require appropriate methodologies to support decision-making. Operational safety and related decisions are based on qualitative and quantitative reliability approaches at different scales (structures, complex systems, domino effects, Natech). Analysis methods must integrate the industrial context as well as interactions with the natural, physical, and organizational environments. Decisions are often based on incomplete or uncertain data and generally involve a multi-criteria approach. Expected contributions concern, on the one hand, the modeling of complex systems (networks, territories exposed to natural or technological risks) and, on the other hand, decision support processes, including the representation and propagation of uncertainties using probabilistic, possibilistic, or hybrid approaches, as well as sensitivity analyses.

Risk management is based on decision-making in uncertain environments and on assessing the reliability of complex structures and facilities comprising several components or failure modes. This topic covers failure probability estimation, local and global sensitivity analyses, and robust design under reliability constraints. Contributions in single- or multi-objective optimization and industrial applications illustrating the operational nature of the proposed methods are encouraged.

Topics covered:

• Operational safety of industrial structures, facilities, and systems,
• Qualitative and quantitative approaches to decision support and multi-criteria optimization,
• Data uncertainty, variability, model error, expert elicitation,
• Modeling and analysis of complex and multi-risk systems,
• Resilience of critical systems and infrastructure,
• Optimization of the design and maintenance of complex systems,
• Performance indicators.
• Local and global sensitivity analysis,
• System reliability, optimization under uncertainty,
• Robustness and reliability of structures and civil engineering works,
• Reliable design of structures and civil engineering works,
• Industrial applications, etc.

Theme 2: Hazards and stresses in the context of climate change.

The hazards and uncertainties associated with stresses and the environment (natural, mechanical, and anthropogenic) determine the reliability of structures and engineering works. It is therefore essential to have stochastic models that represent the environment and the actions envisaged throughout the entire service life. Short- and long-term hazard prediction models are essential for ensuring the reliability of industrial structures, works, and systems during service. In this context, climate change is a major factor, altering the frequency, intensity, and nature of natural hazards (floods, storms, heat waves, ground movements, etc.). This analysis will therefore focus on uncertainties and anticipated stresses throughout the entire life cycle, including the design, construction, operation, demolition, and recycling phases. On the other hand, the vulnerability and resilience of structures and systems reflect their response and capacity to adapt and recover from damage, including damage exacerbated by climate change. Contributions should focus on original developments or case studies emphasizing the concepts of hazard, vulnerability, resilience, and adaptation to climate effects.

Topics covered:

• Modeling natural and climatic hazards: storms, earthquakes, floods, heat waves, avalanches, etc.
• Resilience of structures, buildings, infrastructure, and industrial systems in the face of climatic hazards
• Use of feedback and historical and climate records,
• Modeling of loading in real and future conditions,
• Stochastic extrapolation of stresses and hazards under climate scenarios,
• Modeling of rare data and extreme events exacerbated by climate change.

Theme 3: Degradation of materials, structures, and engineering works.

The deterioration of materials, structures, and buildings is now a major industrial concern, given the facilities built in various sectors of the economy. Having predictive models of deterioration is therefore essential for decision-making regarding maintenance and extending the service life of facilities. This topic focuses on probabilistic modeling of degradation due to mechanical operation (fatigue, wear, damage, creep, etc.) and the natural environment (corrosion, erosion, changes in material properties, etc.). Two approaches will be considered: physical, through modeling of physical behavior in uncertain contexts, and statistical, through analysis of observations on operating systems. Contributions are expected to focus on the development of models and methods for the characterization and predictive assessment of material and structural degradation. This theme includes the characterization and probabilistic modeling of different types of degradation with a view to risk prevention or life cycle optimization.

Topics covered:

• Experimental characterization of degradation,
• Analysis of in situ observations, feedback,
• Stochastic degradation models, geostatistical models,
• Probabilistic physical degradation models,
• Extrapolation of degradation data,
• Control and testing techniques: destructive testing, non-destructive testing,
• Modeling of accelerated tests, test optimization,
• Consideration of degradation in reliability models,
• Bayesian methods, etc.,
• Possibilistic methods and data fusion,
• Inverse problems of stochastic identification,
• Modeling and identification of structures, structures and soils, geostatistics.

Theme 4: Modeling, monitoring, inspection, and life cycle management.

Asset management involves modeling the life cycle, incorporating internal changes (aging and deterioration) and external changes (loads, environment, interventions, etc.). This asset management incorporates numerous sources of uncertainty, which must be taken into account in the decision-making process. Developing an optimal management strategy also requires modeling the consequences of deterioration and failure, modeling actions (inspection, repair) in the context of the temporal evolution of the system under study, and continuously monitoring the condition of structures using auscultation or monitoring devices. The use of artificial intelligence and data analytics methods makes it possible to process and interpret the large volumes of data from sensors, identify degradation trends, predict residual life, and propose optimized maintenance strategies. It is also necessary to take into account industrial, technical, and financial constraints for optimal management under the operating conditions of structures and facilities in service and their resistance to extreme events. This requires consideration of different scales (from components to entire facilities) and rigorous sampling for the most accurate assessment possible. This theme therefore focuses on optimizing asset management policy through the presentation of advances in methodological developments, auscultation/monitoring, and applications to industrial cases. Strategies may be placed within the framework of adaptations to climate change.

Topics covered:

• Methodology for managing existing structures,
• Structural health monitoring (SHM), optimization of instrumentation and exploitation of monitoring data,
• Residual life prediction, life extension,
• Accounting for inspection uncertainties in terms of time and space,
• Stochastic modeling of data from destructive testing, NDT, in-situ measurements, accelerated testing, and sensor data,
• Optimal preventive and dynamic maintenance policy,
• Single and multi-component systems,
• Regulatory and normative methods,
• Feedback,
• Use of AI and machine learning for data analysis, failure prediction, and decision optimization,
• Impact of the human factor on management policy,
• Assessment of effects and adaptation to climate change,
• Spatial variability at different scales in the context of a sampling policy.

Theme 5: Propagation of uncertainties and reliability methods.

The complexity of behavioral models, the associated computation times, the large number of random variables, the computing and human resources required, and the low probabilities to be assessed in an industrial context are real obstacles to the application of probabilistic methods. This theme aims to bring together contributions relating to methods for propagating uncertainties in processing models (numerical and/or analytical), particularly for solving reliability problems. It addresses the methodological, algorithmic, and application aspects of reliability analysis.

Topics covered:

• Numerical methods for reliability,
• Stochastic finite elements, stochastic spectral approaches,
• Meta-models for uncertainty propagation,
• Reliability and time-variant or space-variant problems,
• Problems with a large number of random variables,
• Advances in reliability methods and their standardization, etc.

Theme 6: Decision support for risk management.

Risk management in infrastructure, industrial systems, and territories exposed to natural, technological, or systemic hazards is based on complex decision-making processes that incorporate uncertainty, multiple interactions, and operational constraints. Decision support aims to provide methodological tools for characterizing, quantifying, and prioritizing risks, comparing different prevention or mitigation strategies, and optimizing choices in an uncertain, evolving, and multi-risk context.

This theme includes the study of qualitative and quantitative methods (probabilistic, possibilistic, multi-criteria) for modeling hazards, vulnerabilities, and consequences, integrating interactions between systems, environments, and multiple risks. The use of data from monitoring, feedback and historical observations is combined with the participation of actors and stakeholders in order to strengthen the legitimacy, transparency and relevance of decisions. Also of interest are approaches aimed at integrating or anticipating the effects of climate change, which can alter the frequency and intensity of extreme events and their sequence (domino effects).

This theme aims to bring together contributions proposing robust, multi-risk, and operational risk management strategies that integrate technical, human, and social dimensions to guide the planning, maintenance, and resilience of critical systems and infrastructure.

Topics covered:

• Modeling and prioritization of natural, technological, and systemic multi-risk hazards,
• Multi-criteria approaches and optimization methods for decision-making,
• Integration of uncertainties, partial or imprecise data, and error propagation,
• Participatory approaches and co-design of management strategies with actors and stakeholders,
• Hazard scenarios and stochastic or deterministic simulations for planning,
• Assessment of the vulnerability and resilience of systems and infrastructure,
• Climate change mitigation and adaptation strategies, including extreme events and long-term impacts,
• Cost-benefit analysis and performance indicators for the selection of management measures,
• Industrial and territorial applications illustrating the effectiveness and robustness of the proposed methodologies.