ISRERM2026 10^th International Symposium on Reliability Engineering and Risk Management

The long-term reliability of structural systems pose critical research challenges on a global scale. Over their life cycles, structural systems face deterioration and exposure to hazardous loads, leading to significant uncertainties that are both time-dependent and space-dependent. These uncertainties give rise to a variety of complex failure modes that require robust analytical approaches for comprehensive evaluation.

This Mini-Symposium aims to unite leading experts in structural reliability to share and discuss recent advancements in the field. The forum will provide a platform for addressing a broad spectrum of topics while emphasizing the distinct characteristics that define and advance our understanding of structural reliability.

Topics for potential contributions include, but are not limited to:

• Structural reliability and risk assessment
• Time-dependent reliability analysis methods
• Dynamic reliability analysis methods
• Advanced stochastic models for structural reliability
• Reliability-based design and optimization methodologies

The safety and reliability of engineering structures critically depend on the accurate identification of system parameters, boundary conditions, and external loads. However, real-world engineering systems are inherently affected by various sources of uncertainty, including material variability, measurement noise, model inadequacy, and environmental randomness. Conventional deterministic inverse problem methods often fall short in such settings. This mini-symposium focuses on the integration of uncertainty inverse problems and stochastic system identification, aiming to develop robust and efficient computational and experimental approaches to quantify the propagation of uncertainty throughout the inversion process. Such efforts are essential to enhance the credibility of structural condition assessment, damage diagnosis, and life prediction, and to provide a solid scientific basis for risk-informed decision-making in reliability engineering. contributions on the following topics (but not limited to) are invited:

• Problem setting and theoretical frameworks for uncertainty inverse problems;
• Bayesian inference and Markov chain Monte Carlo (MCMC) methods;
• Stochastic optimization and robust inversion techniques;
• Methods and techniques for stochastic system identification;
• Hybrid identification methods integrating multi-source data (e.g., sensor networks, images, physics-based models);
• Machine learning and data-driven approaches for uncertainty quantification;
• Deep learning in inverse problems, including Bayesian neural networks;
• Model reduction and surrogate modeling for accelerated stochastic computation;
• Stochastic parameter identification from vibration responses;
• Identification of time-varying and nonlinear systems under uncertainty;
• Random field modeling and spatial variability quantification;
• Stochastic loading identification;
• Engineering applications and case studies, including: Stochastic damage identification in civil infrastructure (bridges, buildings, dams); Inverse reliability analysis of aerospace and mechanical structures; Stochastic response reconstruction under extreme events (earthquakes, wind, impact).

We strongly encourage and warmly welcome contributions that focus on pioneering theories and real-world applications in engineering mechanics, civil engineering, mechanical engineering, and related fields.

Reliability engineering and risk management increasingly rely on exploring diverse models (e.g., data-driven, physics-based, and hybrid frameworks) to enable robust analysis and decision-making under limited information and uncertainty. The predictive credibility of these models—critical for trustworthy design and risk assessment—hinges on rigorous inference frameworks. Bayesian model inference offers a principled paradigm for model validation, calibration, selection, fusion, and credibility-enhancing experimental design, bridging theoretical foundations with engineering practice. This Mini-Symposia aims to convene researchers and practitioners to explore cutting-edge advances in Bayesian model inference, addressing the following interconnected themes (but not limited to):

• (1) Theoretical Foundations, including novel Bayesian nonparametric methods, hierarchical modeling for multi-scale systems, Bayesian model calibration/selection/averaging for uncertainty quantification, credibility metrics integrating physical constraints, structural health monitoring, high-precision learning from small informative datasets or large, heterogeneous and (spatially and temporally) correlated dataset, handling the statistical nature of the measurement uncertainty, etc.
• (2) Numerical Innovations, focusing on scalable MCMC algorithms, deep learning-driven variational inference, approximate Bayesian computation for complex physics models, and computational efficiency strategies for high-dimensional parameter spaces.
• (3) Engineering Applications, spanning reliability assessment in aerospace propulsion systems, risk-informed design for civil infrastructure under climate uncertainty, fault diagnosis in energy grids, and safety-critical systems in healthcare.
• (4) Emerging Frontiers, welcoming interdisciplinary ideas at the intersection of Bayesian inference with AI-driven automated model construction, quantum-informed inference, and real-time data assimilation for dynamic systems.

By fostering cross-disciplinary dialogue, the symposium seeks to advance methodological rigor, promote practical implementations, and identify new frontiers in leveraging Bayesian inference to enhance model credibility and their applications in risk & reliability. This platform will facilitate knowledge exchange, collaborative problem-solving, and the translation of theoretical insights into actionable engineering solutions.

Amidst the development of sensor technologies and structural health monitoring (SHM) systems, various model updating methods have been developed to update the in-situ properties of structures and their future performance based on measured data. The response of structures under working conditions is complex and influenced by operational/environmental variations. The need to explicitly consider the uncertainty due to these factors is well recognized. A Bayesian perspective, i.e., viewing probability as a plausibility of propositions conditional on given information instead of relative frequency of occurrence, provides a powerful approach for mathematical formulation, delivering the ‘posterior’ probability distribution of system parameters given the information from data and consistent with modeling assumptions. Recent years have seen significant progress in methodologies that combine traditional domain-specific models with machine learning techniques. Challenges still remain, however, such as extracting interpretable information and learning governing physical principles from monitoring data, improving computational efficiency when dealing with high-dimensional systems and large SHM datasets, etc.

This min-symposium arouses contributions to Bayesian approaches of model updating and risk management with applications such as structural health monitoring. Possible topics include but are not limited to: (1) Bayesian system identification of engineering structures, (2) AI-based methods for data processing, (3) performance assessment and risk managements in SHM, (4) physics-data fusion model combing machine learning and physics, (5) new technologies for operational modal analysis.

The proper quantitative treatment of uncertainties is a fundamental prerequisite for accurately assessing the response and reliability of various engineering systems. Over the past few decades, the stochastic dynamics community has focused on determining the system responses and evaluating the associated risks in complex dynamic systems subjected to stochastic excitations. In this context, many researchers have made significant contributions to determining the response and reliability of diverse linear and nonlinear systems, including those exhibiting hysteretic behaviour, as well as systems incorporating fractional derivative elements. However, challenges remain regarding the precision and computational efficiency of the associated analytical and numerical methodologies, particularly in high-dimensional systems and systems exposed to non-Gaussian, non-white, and non-stationary excitations.

The primary goal of this MS is to highlight recent breakthroughs and emerging interdisciplinary approaches within the broad field of computational techniques aimed at addressing current challenges in stochastic engineering dynamics, with a specific focus on uncertainty modelling and propagation. Additionally, this MS seeks to establish a platform for the exchange of ideas and collaboration among diverse technical and scientific disciplines. We invite contributions that encompass both fundamental research and practical applications in the domain of stochastic dynamics, including order reduction techniques, risk and reliability assessment applications, joint time-frequency analysis methods and tools, sparse representation methodologies, compressive sampling, and data driven approaches. Furthermore, we encourage submissions related to stochastic and fractional calculus modelling and applications, Monte Carlo simulation methods, stochastic structural design applications, performance-based engineering methodologies, and related subjects.

Ensuring the reliability of dynamic structures under environmental loads requires a strong understanding of uncertainties and their effects on structural performance. Civil, offshore, and energy infrastructure, such as high-rise buildings, long-span bridges, offshore wind farms, and industrial facilities, must endure variable and often extreme environmental forces, including wind, waves, earthquakes, and temperature changes. These uncertainties, present in both external loads and structural characteristics, create major challenges in predicting structural responses and managing risk. This mini-symposium will highlight recent advances in the quantification and management of uncertainties that affect the behavior of dynamically sensitive structures. Topics will include, but are not limited to:

• Probabilistic and stochastic modeling techniques for representing environmental loads and their effects on structures, including approaches such as surrogate modeling, advanced Monte Carlo methods, and data-driven techniques
• Risk analysis and reliability assessment, covering methods for quantifying failure probabilities, evaluating resilience under extreme events, and integrating risk-informed decision-making into engineering practice
• Uncertainty quantification and propagation, exploring how epistemic and aleatory uncertainties affect structural response predictions and how computational methods can enhance their characterization
• Strategies for robust and adaptive structural design, focused on developing systems that maintain reliable performance under variable and unforeseen conditions
• Real-world applications and case studies demonstrating how uncertainty quantification and risk assessment methodologies are applied in practice

This mini-symposium aims to facilitate interdisciplinary exchange and highlight new methods for improving the performance of structures exposed to uncertain environmental conditions.

Ensuring reliable infrastructure performance under uncertainty is one of the central challenges in modern infrastructure asset management. Infrastructure asset management is fundamentally defined as a set of coordinated activities aimed at realizing value from assets by systematically guiding investment, operation, and maintenance decisions throughout the life cycle of complex, interdependent infrastructure systems. These systems, comprising networks of roads, bridges, water systems, and other essential assets, undergo deterioration processes that are uncertain, spatially and temporally heterogeneous, and often coupled through shared risks and external stressors. This mini-symposium focuses on uncertainty-informed approaches to infrastructure asset management, where both statistical deterioration modeling and (stochastic) control theory play a critical role. We invite contributions that address uncertainty in data, models, and system dynamics, arising, for example, from inspection errors, limited monitoring, environmental variability, or model misspecification. Topics of interest include, but are not limited to, probabilistic performance prediction, reliability-based decision frameworks, robust and adaptive maintenance strategies, risk-informed life-cycle optimization, and deep reinforcement learning methods. A particular emphasis is placed on methodological advances that enhance decision-making under deep uncertainty, and on integrative approaches that treat infrastructure asset portfolios as complex systems requiring coordinated, system-wide management. By bringing together researchers and practitioners from reliability engineering, infrastructure systems analysis, and decision science, this session aims to advance the theory and practice of infrastructure asset management toward more resilient and value-driven infrastructure development under uncertainty.

The rational assessment of risk and reliability in marine and offshore structures is of critical importance for various reasons—for example, to prevent catastrophic events, to protect the ocean environment, and to attain efficient maneuvering and operation. Taking the safety of a floating structure (e.g., a marine vessel or a floating offshore wind turbine) as an example, the floating structure in ocean are subjected to stochastic excitations from waves, currents, and wind, which are often complex and mutually interacting. Consequently, the response characteristics exhibit significant complexity on top of the structural nonlinearities, motion nonlinearities, material nonlinearities, and other contributing factors. Furthermore, uncertainties due to limited data or knowledge hinder us from perfectly characterizing the complex nonlinearities involved. Proper consideration of these aspects is also essential for the advancement of autonomous operation technologies.

This mini symposium aims to provide a platform for researchers working on the assessment of risk and reliability in maritime and ocean engineering. A wide range of applications can fall within the scope of this session, including but not limited to marine vessels, offshore platforms, autonomous surface vehicles, offshore wind turbines, and wave energy converters.

This session welcomes contributions related to, but not limited to, the following topics:

• Probabilistic estimation of extreme responses
• Uncertainty quantification
• Surrogate modeling of complex phenomena
• Advanced numerical techniques
• Reliability-based design approaches
• Data-driven and robust operations
• Autonomous systems
• Machine learning (ML) and artificial intelligence (AI) techniques
• Risk and reliability of AI/ML in safety-critical applications

Both theoretical developments and practical applications are highly encouraged.

Advancements in artificial intelligence (AI) are rapidly transforming the field of rock mechanics, offering unprecedented capabilities for characterizing the mechanical behavior, heterogeneity, and structural evolution of rock masses, especially in complex underground environments such as deep and water-rich tunnels. This Mini-Symposium aims to bring together leading researchers and practitioners to explore cutting-edge methodologies that integrate AI techniques—including deep learning, physics-informed neural networks, and Bayesian optimization—with traditional geo-mechanical modeling and field monitoring. Topics of interest include, but are not limited to: AI-based recognition of rock mass structures from multi-source data (e.g., 3D point clouds, acoustic signals, borehole imagery), intelligent prediction of mechanical parameters and deformation responses, real-time tunnel face characterization, and hybrid modeling frameworks that bridge data-driven inference and constitutive rock mechanics principles. Particular attention will be given to case studies and engineering applications that demonstrate the effectiveness and robustness of AI-augmented methods in enhancing design, risk evaluation, and decision-making in tunnel construction. By fostering interdisciplinary dialogue between AI scientists, geotechnical engineers, and underground space researchers, this Mini-Symposium seeks to catalyze a new wave of intelligent, adaptive approaches for addressing the geo-mechanical challenges of modern tunneling projects. Participants are encouraged to contribute both theoretical advances and field-validated innovations, as we collectively reimagine the future of intelligent geomechanics in subterranean environments.

Recent years have seen an emerging shift towards data-driven approaches for modeling complex and nonlinear systems, alongside an increased recognition of the associated uncertainties. This trend has been further accelerated by innovations in e.g., scientific machine learning and deep generative models. Besides, these data-driven techniques have the potential to foster new frontiers in forward and inverse uncertainty quantification, particularly with regard to high-dimensional and/or highly nonlinear problems.

This mini-symposium aims to bring together experts, researchers and practitioners from academia and industry to explore and discuss the latest developments, methodologies and applications related to data-driven modeling and uncertainty quantification for understanding complex and nonlinear systems. Topics for potential contributions include but are not limited to:

• Identification of (reduced order) models and governing equations from data.
• Fast and accurate inferences under uncertainties.
• Machine/deep learning-aided forward and inverse uncertainty analysis.
• Quantification of mixed aleatory and epistemic uncertainties.
• Efficient solutions for high-dimensional and/or highly nonlinear problems.
• Applications in the fields of structural engineering, geotechnical engineering, engineering seismology and others.

Geotechnical engineering practice is always involved with a considerable level of uncertainty. It is primarily attributed to (1) inherent randomness in geologic materials (aleatory uncertainty) and (2) imperfect knowledge of the subsurface conditions (insufficient data to build a three-dimensional ground model) and the soil-structure interaction (idealization of complicated soil behavior and its interaction with the structure) (epistemic uncertainty). In principle, aleatory uncertainty cannot be reduced, as naturally occurring geomaterials manifest itself on both spatial and temporal scales; however, it can be characterized more accurately by integrating the data and information from geologic survey, geophysical investigation and geotechnical (laboratory and in-situ) testing. Epistemic uncertainty can be considered to include (1) measurement error, (2) statistical uncertainty in the determination of soil and rock properties, and (3) model uncertainty in the calculation of soil behavior and its interaction with the structure. Better understanding and characterization of the uncertainties is critical for developing reliability-based geotechnical design procedure and risk management. National or benchmarking geo-test sites worldwide are an efficient tool to do this work. These sites represent a wide range of geologic materials and were investigated by a variety of methods. They provide a lot of subsurface data to characterize the spatial variability and statistical uncertainty in the estimation of soil properties. Furthermore, field instrumented tests have also been performed on full-scale geo-structures (e.g., embankment, shallow and deep foundations), which can be effectively used for the calibration of analysis and design methods, ranging from empirical/semi-empirical, theoretical to advanced numerical models. As such, this session aims to present benchmarking examples of using these sites to quantify aleatory and epistemic uncertainties in site characterization and geotechnical analysis, and ultimately, their effect on geotechnical decision.

Proving a quantitative reliability target is a challenging issue in both research and industrial practice. Different reliability requirements must be taken into account at various product levels, such as the system, assembly, or component level. Uncertainties must be considered in both test planning and test evaluation, which in turn affect planning parameters such as test duration and sample size. The consequences for the confidence level must also be examined. Failure-oriented tests in particular are often performed as accelerated tests, which represents another aspect of reliability testing.

Topics for potential contributions include but are not limited to:

• Reliability Demonstration Testing (zero failure testing)
• Failure Testing (e.g. bases on Weibull)
• Quantitative Accelerated Life Testing
• Testing on different system levels
• Consideration of prior knowledge (Bayes)
• Merging testing and simulation
• Uncertainties in Reliability Testing
• ...practical and theoretical investigations

High-fidelity simulations are central to tackling the complex, nonlinear, and uncertain behavior of geomaterials and geotechnical structures. However, conventional simulation-based models, while rigorous, are often computationally expensive-posing bottlenecks for uncertainty quantification, time-dependent reliability analysis, and optimization tasks. Recent advances in data-driven surrogate modeling are revolutionizing how geotechnical problems are approached. By learning the input-output mapping of a costly simulator or directly exploiting monitored data, surrogate modeling enables rapid uncertainty propagation, optimization, and adaptive control. Despite their promise, several critical challenges hinder the widespread adoption and robust application of surrogate models in practice. These include: the curation of training data, ensuring generalizability across diverse geological contexts, handling data scarcity, maintaining physical consistency and interpretability, and rigorous validation. This Mini-Symposium aims to bridge this gap by showcasing advancements in surrogate modeling techniques. The sessions will delve into innovative methodologies, practical implementations, and emerging trends, fostering interdisciplinary collaboration to accelerate their adoption in geotechnical engineering. Contributions are welcomed in areas including, but not limited to:

1. Development of advanced surrogate modeling techniques for geotechnical applications, including data-driven, statistical, and machine learning approaches
2. Physics-informed and hybrid surrogate models that integrate domain knowledge and physical constraints for enhanced prediction accuracy and interpretability
3. Strategies for surrogate model training and validation under practical constraints such as data scarcity, high dimensionality, and uncertainty
4. Efficient surrogate-based frameworks for computationally intensive geotechnical simulations and uncertainty quantification
5. Applications of surrogate models in geotechnical analysis, design, and decision-making processes
6. Integration of surrogate models with emerging technologies for real-time monitoring, digital twins, and intelligent geotechnical systems
7. Case studies, benchmarking, and practices for the implementation and validation of surrogate models in geotechnical engineering

Engineering structures are commonly vulnerable to catastrophic natural disasters, e.g., seismic motion and strong wind. Structural failure due to catastrophic disasters can cause numerous economic losses and casualties. With the increasing frequency and compounding effects of natural hazards, engineering structures are often exposed to multiple types of disastrous effects either simultaneously or sequentially. The multi-hazard disastrous effect modelling and dynamic reliability analysis are therefore of essential importance for the purpose of hazard mitigation. Nevertheless, high-fidelity and efficient techniques regarding this topic are still challenging. On the one hand, randomness inevitably involves both disastrous effects and structures. On the other hand, the nonlinear behavior of engineering structures (especially under the dynamic disastrous effect) makes the problem even more difficult. To this regard, this mini-symposium (MS) is devoted to reporting the recent advances and emerging approaches related to stochastic dynamics and reliability evaluations of complex engineering structures under single and multiple disastrous effects. Special attention is paid to the modelling, analysis, and reliability assessment of structures subjected to multi-hazard scenarios, including but not limited to earthquake-wind, earthquake-tsunami, and wind-rain interactions. This MS aims to foster interdisciplinary discussions and collaborations in the development of advanced computational methods, probabilistic models, and reliability-based design strategies against extreme events.

Topics for potential contributions include but are not limited to:

• Modelling of disastrous excitations, e.g., the modelling of seismic ground motions and strong wind;
• Pragmatic techniques for stochastic dynamics of complex nonlinear structures;
• Distinct failure mechanisms of complex engineering structures under disastrous effects;
• Dynamic reliability analysis under multi-hazard scenarios involving cascading or compounding effects;
• Dynamic reliability analysis of complex engineering structures;
• Dynamic reliability analysis involving multiple kinds of failure mechanisms;
• Data- and physics-informed approaches for multi-hazard reliability evaluation.

Maintenance of infrastructure is an important and urgent issue because there are many structures to be maintained. However, it is not easy to maintain all the structures in good condition due to many problems. Moreover, natural disasters, such as earthquakes, tsunamis, typhoons, heavy rain, and volcanic eruptions, have been becoming more frequent. Many infrastructures are at these risks. However, information collection, such as manual inspection and survey after a disaster, is extremely difficult, and the convenient Health Monitoring Systems are expensive. Therefore, innovative technologies for these problems are expected. New technologies have been developing using AI (Artificial Intelligence) and IoT, such as deep learning and UAVs. Deep learning can be promising because it can find important feature characteristics automatically. IoT sensing, such as low-cost MEMS, UAV, satellite images, cloud servers, and so on, can be applied widely for real-time monitoring and collecting big data for decision making in natural disasters. This special session aims to bring together academics and practitioners to discuss the future of AI/IoT technologies for maintenance and natural disaster prevention of infrastructures.

Modern engineering systems often exhibit complex dynamic behaviors characterized by strong nonlinearity, high dimensionality, and multi-scale interactions. These systems operate under multi-source uncertainties, including external excitations (e.g. wind, seismic, wave, operational loads, etc.), internal randomness in material properties, geometry, and boundary conditions, as well as epistemic uncertainties arising from incomplete knowledge. Under such conditions, their responses typically display pronounced stochastic dynamic characteristics, with intricate uncertainty propagation across both temporal and spatial scales. Accurately characterizing propagation mechanisms and quantifying these uncertainties in nonlinear, high-dimensional, and even fractional-order dynamic systems remains an open challenge, which is critical for advancing the safety, reliability, and resilience of next-generation engineering systems.

This Mini-Symposium will serve as an interdisciplinary forum for researchers and practitioners to discuss theoretical and methodological advances in stochastic dynamics. Topics for potential contributions include but are not limited to:

• Full probabilistic modeling of random fields and stochastic processes.
• Uncertainty propagation in high-dimensional nonlinear dynamical systems, including emerging challenges at the computational mechanics-stochastic dynamics interface under exascale simulation conditions.
• Model-order reduction techniques for efficient stochastic analysis in large-scale systems.
• Complex excitation and structural mechanisms, including fractional-order dynamics, Poisson and Lévy-type excitations, and multi-source stochastic inputs.
• Representation, quantification, and propagation of epistemic uncertainty in dynamical problems.
• Advanced theoretical and computational methods in stochastic dynamics, ranging from physics-driven and physics-enhanced/encoded approaches to data-driven and AI-aided techniques.
• Applications and innovative solutions for real-world engineering systems (e.g., critical infrastructure, energy systems, and advanced equipment).

Infrastructure systems today face unprecedented challenges over their life cycles, including aging, environmental degradation, and increasingly severe, climate-induced hazards. Climate change has become a dominant driver of risk, intensifying the frequency and magnitude of extreme events and introducing new patterns of uncertainty that traditional engineering approaches are ill-equipped to manage. Ensuring long-term performance under compounded climatic, environmental, and operational stressors is now a critical imperative. This Mini-Symposium (MS) offers a focused platform for advancing methods and applications in reliability analysis, probabilistic risk assessment, resilience quantification, and life-cycle performance management, with a particular emphasis on climate adaptation and sustainable infrastructure development.

The MS will explore advanced methodologies for managing climate-driven multi-hazard risks across infrastructure life-cycle phases, from design through construction, operation, maintenance, and decommissioning under evolving climatic conditions. Key themes encompass: (1) probabilistic life-cycle performance models integrating material deterioration, structural aging, and climate change impacts; (2) climate-resilient design frameworks addressing non-stationary hazards and future climate scenarios; (3) multi-hazard risk assessment for infrastructure networks under climate uncertainty; (4) data-driven approaches enabling real-time reliability monitoring and climate-adaptive predictive maintenance; (5) decision frameworks optimizing resource allocation in climate-conscious asset management; and (6) sustainable infrastructure solutions reconciling circular economy principles with reliability requirements amid environmental change.

This symposium convenes researchers, practitioners, and policymakers to disseminate cutting-edge findings, address critical climate-related infrastructure challenges, and define future research trajectories. We especially encourage submissions on methodological innovations, climate modeling integration, adaptation techniques, case studies, and interdisciplinary approaches—particularly for structural, transportation, and coastal infrastructure exposed to climate impacts.