Special Sessions

List of accepted Special Sessions

Please click on the title of the session to open further information about it.

Organised by:

Motivation: Economic and social consequences of natural hazards (such as surface flooding, river flooding, landslide, heat wave, wildfire, hurricanes, droughts, coastal flooding) have gained a lot of national and international attention in the last decades, since frequency and intensity of such natural disasters have been increasing due to climate change. Overall, more than 1.5 billion people have been globally affected by such disasters. Therefore, as highlighted in the UN’s Sendai Framework for disaster risk management 2015-2030, analyzing and managing the risks of disasters while accounting for the changing climate, play crucial roles in building more resilient societies and critical infrastructures (such as transport, energy, water, waste, ICT). However, understanding and analyzing the climate risks and assessing the resilience of spatially-distributed critical infrastructures while taking into account the changing climate is inherently complex and challenging due to various factors such as the uncertainties associated with extreme climate hazards, intensity of exposure of critical infrastructures, performance of existing response and recovery measures, interdependencies and complexities of critical infrastructure networks, widespread failure impacts and associate costs.

Objective: The aim of this Special Session is to provide an opportunity for the researchers to share and exchange their knowledge and experience on fields relevant risk and resilience assessment of critical infrastructures while accounting for climate change. Related topics are listed as, but not limited to: 1)  Natural hazards modelling as stressors for critical infrastructures; 2) Spatial/temporal modelling and simulation of extreme climate events; 3) Climate change and their impact on critical infrastructure networks resilience; 4) Climate adaptation; 4) Natural hazards risk and susceptibility maps; 5) Extreme spatial hazards and risk of disruptions of multiple infrastructure systems; 6) System-of-system approach to risk and resilience assessment of interdependent critical infrastructure networks; 7) Cascading failures.

Organised by: 

Motivation:  Cyber-physical systems integrate sensing, computation, control and networking into physical objects and infrastructure, connecting them to the Internet and to each other. Integrated cyber-physical systems (CPSs) are increasingly becoming the underpinning technology for major industries and are transforming the way people interact with engineered systems, although it has been observed that interdependency among the systems tends to make the CPS more fragile against failures, natural hazards, and attacks. Indeed, the inherent vulnerability stemming from increasing strengths of system complexity and coupling, intertwined with an increasingly complicated and uncertain risk landscape, might even push the CPSs towards the brink of catastrophic failures. Therefore, the CPS reliability (against high frequency low impact events) and resilience (against low frequency high impact events) should be paid significantly attention from researchers, industrial practitioners, and policymakers.

Objective:  This special session aims to gather researchers to discuss recent advances in the study of interdependent CPS reliability and resilience. Also aims to invite researchers to share their successful experience and knowledge on the study of interdependent CPS reliability and resilience. Innovative approaches to addressing these issues in the context of interdependent CPS, such as the smart grids, intelligent road and railway transportations, intelligent civil infrastructures, are preferred. A list of related candidate topics includes but not limited to:

  • Interdependent CPS modelling
  • Reliability analysis of interdependent CPS
  • Resilience assessment of interdependent CPS
  • Multiple hazards
  • Cascading failure
  • Artificial intelligence for CPS reliability and resilience
  • Reliable and resilient design of CPS
  • Optimization for CPS reliability and resilience improvement

Organised by: 

Motivation:  Cyber-Physical Systems (CPSs)feature a tight combination of (and coordination between) physical processes and cyber systems enabling innovative opportunities of command, control, and communication in a number of applications such as smart grids, autonomous vehicles, and intelligent robots. The existence of communication links has widened the attack surface (physical and cyber), and faults originating in either system may have potential, cascading effects on the other interdependent one. In this new and evolving scenario, risk assessment of CPSs faces new challenges and the convergence of safety and security concerns should be properly addressed, in particular during the CPS design that must be proven fault-tolerant and attack-resilient.

Objective: The aim of this special session is to provide a forum for researchers and engineers to discuss how to develop condition monitoring, diagnostics, protection and mitigation strategies that can be implemented on CPSs to be safe and secure in presence of faults and attacks and increase their resilience to such threats.

Organised by: 

Motivation:  Risk assessment in oil and gas (O&G) industry is necessary to prevent undesired events that may cause catastrophic accidents with financial and environmental losses. The special session aims at presenting the most recent advancements in Bayesian Network modelling for risk assessment in O&G industry, challenges and perspectives.

Objective:  A list of related candidate topics includes, but not limited to:

  • Multi-states Bayesian Networks
  • Dynamic Bayesian Networks
  • Methods for the characterization of the Conditional Probability Tables (CPTs) be means of, among others, field data, maintenance reports, monitored data.

Organised by: 

Motivation:  The use of the Best-estimate Plus Uncertainty (BEPU) approach is currently of great interest for the international scientific nuclear technical community in evaluating the safety margins. In the BEPU framework, the response of nuclear systems under different uncertain conditions is studied in general by means of mathematical models implemented in corresponding BE computer codes for numerical simulations. Repeated BE model simulations are typically used to identify undesired or abnormal states, which is of paramount importance for optimally designing and operating such systems and defining accident prevention and mitigation actions. However, this way of proceeding is in general challenging because the corresponding BE codes are :i) computationally demanding(i.e., they require a long time to run a simulation compared to the available computational resources); ii) high-dimensional(i.e., they involve large number of inputs and/or outputs); iii) black-box(the mathematical function underlying the input-output relation is not known explicitly and is usually nonlinear); iv) dynamic(i.e., they evolve in time);and v)affected by severe uncertainties(often due to the scarcity of quantitative data available).Within this broad framework, this Special Session is aimed at gathering expert researchers, academics and practicing engineers to present their recent findings, methodological developments, as well as innovative applications, related to the use (and possibly to the combination) of artificial intelligence, meta-modelling and advanced simulation tools for the efficient analysis of the BE computer models of nuclear systems, in the presence of uncertainties.

Objective:  A list of related candidate topics includes, but not limited to:

  • Sensitivity Analysis methods and applications
  • Forwards Uncertainty Quantification methods and applications
  • Inverse Uncertainty Quantification methods and applications
  • Failure Domain Characterization methods and applications
  • Safety Margins Quantification methods and applications

Organised by:  
  • Francesco Di Paco (dipaco@phd.unipi.it), Department of Civil and Industrial Engineering, University of Pisa, Italy
  • Leonardo Marrazzini (marrazzini@unipi.it), Department of Civil and Industrial Engineering, University of Pisa, Italy
  • Roberto Gabbrielli (gabbrielli@unipi.it), Department of Civil and Industrial Engineering, University of Pisa, Italy
  • Marco Frosolini (marco.frosolini@unipi.it), Department of Civil and Industrial Engineering, University of Pisa, Italy

Motivation:  New methods and tools, framework, assessments, case studies, and epidemiological studies of the increase in prevalence and severity of known hazards and risks to workers in the work environment due to climate change and identification of the emergence of new ones.

Workers are often the first to be exposed to the effects of climate change and may be affected for longer durations and at greater intensities. Hence, a proper investigation of hazards and risks climate change-related is needed.

Objective:  The proposal aims to investigate the direct (i.e., warming, extreme weather, …) and indirect impacts (i.e., air pollution, UV exposure, vector-born disease, …) of climate change on workers’ health and safety in the indoor and outdoor worksites. Our Special Session aims to deal with a current topic that, to the best of our knowledge, does not seem to be covered by other regular sessions.

Organised by: 
  • Francesco Di Maio (francesco.dimaio@polimi.it), Department of Energy, Politecnico di Milano, Milan, Italy
  • Tarandom Parhizkar (tparhizkar@g.ucla.edu), The B. John Garrick Institute for the Risk Sciences, University of California, Los Angeles, USA
  • Enrico Zio (enrico.zio@polimi.it), Mines Paris, PSL Research University, CRC, Sophia Antipolis, France

Motivation:  Risk assessment is adopted in safety-critical industry for guiding design, operation and maintenance, and for informing regulations. The consolidated manner for conducting a risk assessment requires risk problem to be decomposed by experts in terms of initiating events, fixed-order Event Trees (ETs)and Fault Trees (FTs). With the rising complexity of the systems, this may not be sufficient to capture the large variety of accidental scenarios that may develop from different timing and magnitude of events occurrences. Computational Risk Assessment (CRA)is based on a simulation-framework that allows overcoming the expert-bias in the definition of the scenarios, enabled by the recent increase in computational power.

Objective:  This Special Session is aimed at gathering expert researchers, academics and practicing engineers to present their recent findings and methodological developments related to the use of advanced simulation in risk assessment and the application to CRA.A list of candidate topics of interest includes, but is not limited to:

  • Dynamic Risk Assessment
  • Dynamic Reliability Methodologies Natural Hazards CRA
  • Complex Systems CRA
  • Uncertainty analysis in CRA
  • Advanced Simulation Techniques (Dynamic Bayesian Belief Networks, (Advanced) Monte Carlo Simulation methods, Kriging, Gaussian Processes, Deep Artificial Neural Networks, Convolutional Artificial Neural Networks, Support Vector Machines, Grey-box models, …)

Organised by: 

Motivation:  In this session we invite presenters to share their findings in human-robot interaction topics within the healthcare context. We expect contributions on findings from applied studies, but also reflections on methods and transfer of knowledge from HF and HRI to this context.

The interest on applied human robot interaction topics in connection with the healthcare sector has grown. Our motivation is to establish a knowledge base on assistive robotics, mobilizing professionals working on the area to share their experiences.

Objective:  This proposal is linked to the ongoing innovation Project “Human Interactive Robotics for Healthcare –HIRo” funded by the Norwegian Research council. We expect this session to scope down a specific topic within the healthcare application area and the Human Factors methodology area. The objectives are:

  • Share knowledge about assistive robots in healthcare;
  • Gather experts with interest/experience on the topic;
  • Establish a network for assistive robots in healthcare.

Organised by: 

Motivation:  Digital Twins are seen as one of the emerging technologies in the reconnected world. This special session aims to gather ongoing theoretical and practical effort son the design, development, and implementation of Digital Twins in important industrial application areas such as Critical Infrastructures, Energy. Digital Twins possess the potential to strengthen safety, reliability, and cyber security of Critical Infrastructures/Cyber Physical Systems. At the same time, it is also important to understand the safety and cyber security issues in adopting Digital Twins of critical systems.

Over the recent years, the conceptualization, development, and implementation of digital twins in different critical application areas is clearly evident. It also shows modelling and simulation potential utilizing disruptive technologies such as Artificial Intelligence and Machine Learning. On the other hand, it is susceptible to different type of safety and cyber security risks that could lead to large-scale negative consequences. Important steps and developments are taken in different application areas taking into account the above-mentioned challenges, however, there is a very limited dedicated setting that facilitate discourses and sharing efforts, which is one of the motivations for this session proposal.

Objective:  The objectives of this special session proposal are:

  • To bring researchers, practitioners, and students together to discuss scientific and practical challenges and solutions related to “Reinforcing Safety, Reliability, and Cyber Security through Digital Twins”.
  • To provide an interdisciplinary platform to present mainly about real-world/industrial problems and scientific innovations under the theme of our special session “Reinforcing Safety, Reliability, and Cyber Security through Digital Twins “covering the above-mentioned topics and industrial application areas”.

The peculiarities of this special session are as follows:

  • Call for papers invites both theoretical and practical contributions especially from researchers, and practitioners.
  • Focus not only how Digital Twins can reinforce safety, cyber security, but also what are the safety and cyber security issues in adopting Digital Twins.
  • Facilitates knowledge sharing on challenges and best practices on the conceptualization/development/use of Digital Twins in different critical domains, which helps in learning between domains.

Organised by:

Motivation:  This SS welcomes papers that bring up innovative solutions for reliability and risk management within the realm of OG industries. It may include different aspects for each phase of a wellbore development from well construction and operation to abandonment. Scientific approaches and practical studies are expected, encompassing autonomous and remote offshore activities, real time integrity management and electrification.

The world energy balance has changed, and then the OG sector faces an ultimate challenge: how to be sustainable, resilient with deep cost reduction, almost zero environmental impact and human exposure? In this scenario, OG companies need to be reinvented and pushed to develop brand new, disruptive solutions. Then, this SS will be a forum for discussing reliability and risk related aspects of these ideas.

Objective:  Machineries, which are exposed to quite harsh conditions in deep water oil wells, need to operate without failures for long time periods. Otherwise, the maintenance costs are exorbitantly high in a way that it may even result in the early abandonment of faulty oilwells. This SS is of special interest for scholars and reliability practitioners who have dealt with these challenges and then, need to come up with innovative approaches to solve them.

OG industry has been digitalized, allowing for data availability and integrated databases to improve well design, technical specification, maintenance, and operational decisions. Then, this SS comprises papers in these fields (just to name a few): autonomous and remote offshore activities by using digital twins for production management, development of robots for unmanned operations, prognostic and health management for predictive maintenance and real time integrity management, and reliability of electrified fields.

Organised by: 

Motivation:  The complexity of human-machine and environmental scenarios bring huge challenges to risk assessment and emergency management. Due to those challenges, more efforts should be done for offering a comprehensive analysis, and some widely used methodologies require a modification and promotion. Therefore, this subject aims to provide an opportunity for researchers to share and to learn current studies for dealing with risk and emergency in complex scenarios.

Due to the increasing coupling and complexity of scenarios in energy systems and operations, dealing with the risk generated by them has grasped increasing attention from the domain of industry and academia. Through risk assessment and emergency management, engineers can monitor, prevent, and control risky scenarios dynamically and timely, so that they can effectively avoid some possible negative consequences. Risk assessment and emergency analysis have been investigated in the literature, particularly those in the area of safety and reliability. Along with the growing complexity, more considerable elements such as human, machine, fire and explosion are involved in the complex scenarios. Therefore, the hybrid of risk assessment methods and emergency management also requires further discussion and investigation.

Objective:  This special session aims to provide a platform to present new methods and applications for the dynamic risk assessment and emergency technologies, particularly for complex scenarios in energy systems (e.g., complex human-machine interaction scenarios, emergency scenarios, fire and explosion scenarios). The special session invites researchers from academia and industry to share research and experience on the study of risk analysis, risk management, emergency preparedness, emergency response and recovery (not limited to). The multi-disciplinary (e.g., system reliability, human reliability, safety engineering, operations research, artificial intelligence, and prognostics and system health management) methods and applications are encouraged.

Organised by: 

Motivation:  Investigations on the use of Digital Twins as a means to embed physics, engineering knowledge, and data into PHM frameworks. Both applications and theory are of interest.

PHM (Prognostics & Health Management) draws on data, which are increasingly abundant with connected objects and the IoT, but also on the knowledge of physical laws that govern normal operation and degradations. The combination of data and physics is called ‘hybrid PHM’.

Objective:  Digital twins – in short, digital replicas of physical objects, can be an enabling technology for hybrid PHM in industry, by continually updating physical models as new data is acquired. The objective of the session is to make this statement more precise by showcasing examples of successes and presenting remaining challenges.

We expect this special session to result in coherent and focused contributions on the topic of hybrid PHM driven by Digital Twins. By carefully selecting complementary contributions from academia and industry, we expect this special session will be a forum to exchange knowledge between these two worlds.

Organised by: 

Motivation:  Reliability analysis offers the possibility of quantifying the level of safety of engineering systems. In practice, one may be confronted with the challenge of coping with aleatoric and epistemic uncertainty, leading to a problem of imprecise reliability analysis. Imprecise reliability analysis is much more involved than its purely aleatoric counterpart. Thus, there is a need for development of novel methods for uncertainty quantification involving aleatoric and epistemic uncertainty that are numerically efficient. Regular sessions focus on the topic of structural reliability considering classical probabilistic analyses. The proposed special session extends on this issue by addressing both aleatoric and epistemic uncertainty.

Objective:  The aim is bringing together the latest developments on approaches for imprecise reliability analysis. The scope covers: novel formulations for coping with aleatoric and epistemic uncertainty; advanced simulation methods; development and application of surrogate models and other models.

Organised by: 

Motivation:  Many modern engineering systems have features such as: non-constant failure and repair rates, dependencies between the component or sub-system failures and complex maintenance strategies.  Commonly used methodologies such as fault tree/event tree analysis do not adequately analyse such systems. This session will explore novel developments in methods used for predicting the failure probability or failure frequency of systems operating normal or phased missions.

Objective:  The main goal is to generate awareness and a discussion in a focussed session which promotes methods which overcome the limitations in the currently accepted failure analysis methods.  Applications to industrial case study examples will also be included. The expected contributions will address the development of methods to enable analysis of system wear-out, system failure dependencies to enable sophisticated maintenance strategies to be accurately incorporated into a system analysis.

Organised by: 

Motivation:  Digital Twin is a popular modelling strategy used to predict the behaviour of complex systems. This allows reducing the necessary requirements and experimental analysis, testing the behaviour of the system under critical situations, and creating scenarios that are difficult or impractical to be recreated in practice. Often the digital twin is only treated as a static and deterministic model. Instead, a realistic digital twin is a dynamic model, constantly updated with different streams of data and information and able to predict (simulate) the performance of the system with the required level of confidence. To make this approach applicable in practice the digital twin needs to address some fundamental challenges and overcome the limitations in the common perception from the users. For instance, the computational cost of high-fidelity simulations may often be incompatible with the computational cost required to make timely decisions. Data are required to improve the quality of digital twins but not all data is the same: data can be imprecise, incomplete, truncated, missing, censored, corrupted, and so on. At the design stage, assessing the trade-off between quality and precision can save money, time and contribute to a reduction of our environmental impact. In order to answer these questions, data- and physics-based models need to explicitly account for uncertainty, while empirical data must be collected and stored alongside other vital information about the measurement protocol.

Objective:  This special session aims to bring together experts from academia and industry on digital twinning in order to address the following challenges:

  • Computational cost (and stability in fact) to propagate uncertainty through a high-fidelity simulation: intrusive vs non-intrusive approaches.
  • Quantify the uncertainty in the simulation and model: how twin is the twin?
  • How to assimilate data from different sources and different quality and different representations into the model?
  • How to control the different fidelity levels of the digital twin in different tasks or analyses?

Organised by: 

Motivation:  Digital technologies (DT), including but not limited to Building Information Modelling (BIM), digital twins, augmented and virtual reality, cyber-physical systems, big data analytics and AI can facilitate automated, data-driven and smart management of risks in construction. Digitalisation is a cross-cutting theme which can be considered under several domains such as health-safety, structural reliability, sustainability as well as cost-schedule. There is a need for new risk assessment methods to capture the complex behaviour of construction projects as socio-technical systems. This special session will investigate how digital technology and AI may address current challenges and change the way risks are managed in construction projects.

Objective:  Objective is to gather researchers working on risk management of construction projects and initiate a debate on the future of RM in the digital era. Papers demonstrating the application and impacts of DT are welcome as well as opinion papers on the future of RM in construction.

Organised by: 

Motivation:  Globally, the annual number of natural disasters has doubled since the 1980s. International guidance on disaster risk management (e.g., Sendai Framework for Disaster Risk Reduction, 2015) has advocated that officials, civil society, and scientists collaborate with citizens and communities to better understand, manage, and respond to natural disasters. To enhance the extent to which these collaborations can be effective and efficient, a deeper understanding is needed of the psychological, behavioural, and organizational processes that can affect how individuals and societies cope with natural disasters. The attendees of the proposed Special Session will share their expertise, experiences, and research findings on human factors in natural disasters to attain this understanding and to develop both theoretical and practical methods that can improve natural disaster preparedness and responses.

Objective:  This interdisciplinary Special Session will bring together experts in disaster preparedness and management to share their knowledge, experience, and research findings, with the aim of identifying (i) the role of human factors in the efficacy of natural hazard preparedness, and (ii) theoretical and practical approaches that can be applied to ensure that citizens, communities, officials, civil society, and scientists can work together to prepare better for and manage natural disasters. It is anticipated that this collegial forum will facilitate future collaborations and potential funding bids.

Organised by: 

Motivation:  Operation of civil aviation near or over conflict zones poses a risk of intentional or unintentional downing of an aircraft. Recent examples of civil aircraft shot down over the conflict zones include downing of Malaysia Airlines flight MH17 over Eastern Ukraine in 2014 and Ukraine International Airlines flight PS752 downing over Iran in 2020 (ASN, 2022). Current global geopolitical situation results in significant number of conflict areas around the globe that makes secure flight operation a very complex issue. Methodologies of risk assessment used in aviation industry are based on qualitative risk matrices methodologies that are subjective and dependent on the expertise of the decision-makers with limited outcomes of risks values (‘low’, ‘medium’, ‘high’) that can be misinterpreted by users. Also, countries use different inputs for the risk assessment, such as various number of security threat levels, that makes the process complex and inconsistent when applied across border. Hence, the industry lacks of harmonization and new objective approaches to risk assessment of flight operation over and near conflict zones.

Objective:  This Special session aims to provide a platform for safety and security researchers as well as professionals from industry regulators, international organizations, airlines and other private entities related to aviation security to discuss the ways of harmonization and improvement of current methodologies; possible developments of existing methodologies by application of quantitative predictive tools, machine learning, potential adaptation of risks management methodologies from other industries. We foresee that this workshop will have the following sessions:

  • Risk assessment methodologies – approach of regulators and international organization
  • Artificial intelligence algorithms in security risk assessments
  • Novel methodologies for aviation security risk assessment
  • Expert judgment elicitation methods in aviation security risk assessment

Organised by: 

Motivation:  Industry 4.0, the fourth industrial revolution, aims at creating smart factories, equipped with disruptive technologies such as advanced robotics, high computing power and connectivity, etc., which are integrated with analytical and cognitive technologies that enable Machine-to-Machine (M2M) and Machine-to-Human (M2H) communication. The smart factory can offer new services and products to customers, with efficiency, standards of quality and reliability higher than before. Also, new analytics to detect production anomalies, diagnose their causes and predict the components Remaining Useful Life (RUL) are becoming available.

Objective:  To fully exploit the Industry 4.0 capabilities, advanced methods must be used at the asset level for Reliability, Availability, Maintainability and Safety (RAMS). To succeed in this objective, ‘cognitive’ systems must be developed, which rely on advanced technology at the intersection of big data, machine learning, and artificial intelligence analytics. Reinforcement Learning (RL) is one of the most promising technologies to build these cognitive models for RAMS. This special session will host contributions showing the potential of RL for RAMS, as well as theoretical and technological enhancements.

Organised by: 

Motivation:  Work orders, safety reports and other documents contain a large amount of information, which is typically not systematically exploited due to its unstructured textual nature. Natural Language Processing (NLP), Knowledge Graphs (KG) and ontologies can be used to extract, organise, and classify information from textual data and to develop models in support to Reliability, Availability, Maintenance and Safety (RAMS).

Objective:  This special session will host contributions showing the potential of NLP, KG and ontologies for RAMS, as well as establish communities to share ideas, code and data and discuss future developments.

Organised by: 

Motivation:  Some of the most critical challenges to the factual deployment of Artificial Intelligence-based models for Prognostics and Health Management (PHM) include the lack of labelled data, i.e. signal values corresponding to known degradation and fault states, and the high variability of operating conditions and system configurations. This high variability of operating conditions and system configurations in a fleet violate some of the basic assumptions of traditional AI-based models that training and test data follow the same data distribution, come from the same input feature space (defined by the same signals) and contains the same labels.

Objective:  Possible solutions to the challenges above include Domain Adaptation (DA) and Transfer Learning (TL).  Their basic idea is to improve the performance of a model in the (target) domain, containing the test data, by transferring information from a related (source) domain, overcoming thereby the domain shift (e.g. induced by different operating conditions or different units of a fleet).

This special session accepts contributions on theoretical and technological advancement in DA and TL for prognostics and health management applications.

Organised by: 

Motivation:  Electronic Components and Systems (ECSs) are key enablers for a wide range of applications, ranging from transport and mobility to medicine and energy. In the 4th industrial revolution, ECS reliability is essential on device and system level and faces exceptional requirements in innovative applications. It is, therefore, fundamental to enhance and ensure reliability of ECS by reducing their failure rates along the entire value chain. The motivation of proposing this special session emerges from the research and development activities performed within the ECSEL JU European Project “Intelligent Reliability 4.0” (iRel4.0, https://www.irel40.eu/): the project outcomes will be shared.

Objective:  In this context, this special session aims at collecting contributions discussing theoretical and technological advancements for enhancing the reliability of ECS. Potential topics include, but are not limited to, the following:

  • Monitoring the production process of semiconductors
  • Quality of electronic devices
  • Burn-in tests
  • Fault detection, diagnostics, and prognostics of ECS
  • PHM at ECS
  • Maintenance of ECS
  • PoF and data-driven models for the reliability assessment of ECS

The special session accepts contributions from all interested colleagues (not necessarily only those involved in the IREL4.0 project).

Organised by: 

Motivation:  This special section addresses a particular issue of dependent failure modelling, which is a fundamental problem in risk/reliability and can be related to a wide variety of methodology areas like reliability modelling, maintenance and PHM. Traditional risk/reliability approaches are based on the assumption of independence. In practice, however, a large number of component/systems experience dependent failure behaviour, e.g., the interaction among failure mechanisms in the component-level, common cause and cascading failures in the system-level. Failing to consider dependencies will lead to inaccurate results that will mislead decision makers.

Objective:  This special session aims to gather researchers to discuss recent methodological advances in the study of dependencies modelling and its application in risk/reliability, maintenance and PHM. Innovative applications that addresses the issues of dependencies in engineering practices are also welcomed. We expect contributions on the following topics: Dependent failure modelling, dependent competing failure processes, degradation-shock-threshold models, copula, frailty models, multiple dependent degradation processes, Bayesian network, common cause failure, cascading failure, load-sharing, remaining useful life prediction considering dependencies, condition-based and predictive maintenance considering dependencies.

Organised by:
  • Taarup-Esbensen (jata@kp.dk), Jacob, University College Copenhagen, Denmark

Motivation:  The Arctic has witnessed some of the most profound climate-related changes ever known. Disastrous events related to climate change around the globe have spurred a need to revisit what we know and how to approach mitigation and adaptation. Social systems have struggled to respond to these hazardous events as they strained their technological and organisational resources. The session will explore empirical and conceptual contributions to how organisations, companies and communities in the Arctic develop their capacity to mitigate and adapt to climate-related hazards through the development of technology, utilisation of resources and ways of organising. Lessons learned here will affect how societies in the rest of the world will identify, manage, and recover from potentially disastrous events.

Objective:  This session focuses on the challenges facing Arctic organisations, companies and communities that threaten their ability to be resilient to potentially disastrous events. The purpose of this special session is to present ongoing research on the impact of these hazards and to discuss the conceptual implication the findings have for social systems’ future mitigation and adaptation. During the discussions, we will explore conceptual commonalities across the different cases and present subjects of further research and collaboration.

Organised by: 

Motivation:  Product development of systems with robot, intelligent and autonomous characteristics is rapidly progressing. Given the human-system issues of such systems, timely guidance covering these issues is necessary to help all sectors of industry to design, field and operate first-time quality robotic, intelligent, autonomous (RIA) systems, and build appropriate trust in products and services that use these systems. There is an urgent need for guidance to explain and provide guidance on the existing, emerging and potential human-system issues and consequences for use and users associated with operational environments whose features relies on automations involving robotic, intelligent and autonomous characteristics.

Objective:  The topics to be covered are around the following thematic areas:

  • physically embodied RIA systems, such as robots and autonomous vehicles with which users will physically interact
  • intelligent software tools and agents with which users actively interact through some form of user interface
  • intelligent software agents which act without active user input to modify or tailor the systems to the user’s behaviour, task or some other purpose, including providing context specific content/information, tailoring adverts to a user based on information about them, user interfaces that adapt to the cognitive or physiological state, “ambient intelligence”
  • the effect on users resulting from the combined interaction of several RIA systems such as conflicting behaviours between the RIA systems under the same circumstances
  • the complex system-of-systems and sociotechnical impacts of the use of RIA systems, particularly on society and government.

Organised by: 

Motivation:  The European Commission’s guidelines on ethics in artificial intelligence (AI), published in April 2019, recognised the importance of a ‘human-centric’ approach to AI that is respectful of European values. Dedicated training schemes to prepare for the integration of “human-centric” AI into European innovation and industry are now needed. AIs should be able to collaborate with (rather than replace) humans. Safety critical applications of AI technology are “human- in-the-loop” scenarios, where AI and humans work together, as manufacturing processes, IoT systems, and critical infrastructures. The concept of Collaborative Intelligence is essential for safety critical situations and it requires interdisciplinary approaches blending expertise across AI, Human Factors, Neuroergonomics and System Safety Engineering.

Objective:  The topics covered in this session should be at the intersections of the followings:

  • Modelling the dynamics of system behaviours for the production processes, IoT systems, and critical infrastructures (System Safety Engineering)
  • Designing and implementing processes capable of monitoring interactions between automated systems and the humans destined to use them (Human Factors/ Neuroergonomics)
  • Using data analytics and AI to create novel human-in-the-loop automation paradigms to support decision making and/or anticipate critical scenarios
  • Managing the Legal and Ethical implications in the use of physiology-recording wearable sensors and human performance data in AI algorithms.

Organised by: 

Motivation:  MASS will play an important role in developing new, more resilient and sustainable transport systems. MASS is also claimed to be a more realistic realization of autonomy than, e.g. cars. However, MASS poses new operational and safety challenges that need addressing. Several ongoing projects develop MASS concepts but are hampered by lack of cost-effective methods to describe the systems, including comprehensive risk assessment and mitigation principles. The interaction between remote operators and automation is a special challenge.

Objective:  Present and discuss methods and tools to address safety, risk, reliability, verification, validation and cybersecurity for maritime autonomous surface ships (MASS). Where relevant, compared to other transport modes and with special emphasis on human-automation interface.

Organised by: 
  • Jan-Iwo Jäkel (jaekel@icom.rwth-aachen.de), Institute of Construction Management, Digital Engineering and Robotics in Construction, RWTH Aachen University, Germany
  • Peter Gölzhäuser (goelzhaeuser@icom.rwth-aachen.de) Institute of Construction Management, Digital Engineering and Robotics in Construction, RWTH Aachen University, Germany
  • Sabine Hartmann (hartmann@icom.rwth-aachen.de) Institute of Construction Management, Digital Engineering and Robotics in Construction, RWTH Aachen University, Germany
  • Katharina Klemt-Albert (klemt-albert@icom.rwth-aachen.de) Institute of Construction Management, Digital Engineering and Robotics in Construction, RWTH Aachen University, Germany

Motivation:  Critical infrastructure systems have a special significance for the economy and society for Germany and Europe. At the same time, the provision of resilient infrastructure is a challenging task with multidimensional complexity. Especially due to the long operating phase of bridges, the operation and maintenance of bridge structures is of particular importance. Due to the bidirectional connection between physical system and digital image, the use of digital twins offers a novel and qualitative basis with many possibilities of data integration and networking for the optimized operation of critical infrastructures.

Objective:  The possibilities of generating and using digital twins using advanced technologies (AI, immersive technologies, sensor technology) will be considered in the special session.  To this aim, novel and innovative approaches from interdisciplinary research will be brought together and the diverse application and linkage possibilities of a digital twin in critical infrastructures from different disciplines (traffic road construction, energy systems, hospital systems) will be demonstrated.

Organised by: 

Motivation:  Construction workers engage in many activities that may expose them to severe hazards, such as falling from rooftops, unguarded machinery, being struck by heavy construction equipment, electrocutions, silica dust, and asbestos. This special session will explore ways to improve construction workers’ safety and understand the consequences of safety risks at multiple levels, such as physical, legal, and reputational. Occupational uncertainty and risk are inherent to the construction work environment. The construction industry has one of the highest worker injuries, illnesses, fatalities, and near-misses compared with other industries. This special session will explore ways to improve construction workers’ safety.

Objective:  The objective is to gather researchers interested in protecting the safety and health of construction workers to explore the implementation of safety and health management systems in the construction industry as well as the quantitative methods for analysing the effects of safety risks.

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Motivation:  Machine learning approaches are typically relying on sufficiently large and sufficiently representative (labelled) datasets. Several challenges arise if either the labels are scarce (or not available at all), datasets are not sufficiently representative and condition monitoring data is only captured infrequently. Such setups are very common in reliability and risk assessment setup and would generally preclude the application of machine learning models.

Objective:  The special session will focus on contributions made towards addressing scarcity in data (inputs and/or targets) including scenarios where inputs and/or labels are scarce (or not available at all), or are not sufficiently representative of all operating conditions of the system. Specifically, the following approaches for building ML models for risk and reliability assessment in the low data-regime will be considered:

  • Reliability of machine learning models built from digital twins
  • Physics-informed machine learning
  • Uncertainty quantification of deep learning models (Bayesian models, ensemble methods, and others)
  • ML approaches to handle scarcity in input and target data, and poor representativeness of training data (active learning, semi-supervised learning, unsupervised learning, generative modelling, data augmentation, and others).

Organised by: 

Motivation:  The digital transformation is rapidly shaping modern industry, with some sectors particularly exposed (energy, aerospace, transportation). From a reliability perspective, new challenges are faced also for every-day use products (from smartphones to electrical cars). In the present competitive worldwide market, there is an increasing need for highly reliable products and the demand for longer warranty periods. In the current framework, it is important to have reliability predictions during early design phases and throughout the life cycle as this is a key part of product validation and risk assessment. Modern industry must adapt to this new scenario and be able to effectively deploy state of the art methods and tools to perform such predictions. The special session aims to present hands-on case studies illustrating modern methods and tools employed to perform reliability predictions within the industrial environment.

Objective:  This special session is dedicated to foster discussion over the state of the art of reliability predictions commonly performed in the industry. The objective is to gather applied case studies of such predictions performed by all industrial actors:  product manufacturers, tier 1 suppliers, software suppliers, consulting firms, and other actors.

Organised by: 

Motivation:  Predictive maintenance is key to deal with maintenance of complex systems. It consists in proposing data-driven/hybrid models able to predict and optimize maintenance actions with reduced costs and managed risks. In the era of digital twins, predictive maintenance will be instantiated and augmented thanks to the agility of the used twins. Collected data through digital twins allow to propose new and dynamic predictive maintenance models able to reconfigure the physical twin when reducing risk, cost and system’s complexity.

Objective:  The main goal of this proposal is to investigate and highlight the gap between digital twins and their role in predictive maintenance models of complex cyber physical systems. We also aim to investigate the digital twins’ values, challenges, and perspectives for predictive maintenance research topics.

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Motivation:  Resilience can be defined as an intrinsic characteristic of a material, body, individual or system that measures its ability to resist and recover following an adverse event. It is studied in fields like logistics, safety, mechanics, environment, and management. In the context of the safety of industrial plants, Resilience is the ability of the system to restore a safe operating configuration following a malfunction or disturbance of its nominal operating conditions, as can happen in the event of an accident. Therefore, the path proposed is for Resilience Engineering (RE) in socio-technical and dangerous environments by connecting it directly to the concept of Risk. In particular, the Risk management core is to identify and reduce the impact of risk factors while RE aims to increase the system’s ability to react to be intrinsically safe, compensating for the shortcomings of a system, which may derive from poor process design or poor management capacity. This is therefore a dynamic process in which the system itself takes part.

Consequently, we are no longer limited to analyzing only what can “go wrong”. Understanding how a socio-technical system works becomes a necessary condition for understanding how it can fail. A systemic approach becomes useful for managing the complexity of industrial plants. A detailed analysis of accidental events and components that increase Risk requires decision support tools to analyse the evolution over time of the risk factor first and the measure of Resilience afterwards.

Objective:  The goal of this special session is to explore the relation between the resilience of an Industrial Plant and the human factors in a socio-technical setting. Deepening the resilience analysis is strongly linked to human factors, control theory and safety engineering, not excluding the integration with the new Industry era. Precisely from this consideration arises the need to understand how people can adapt to an environment full of dangers and pitfalls and, in other words, how people who are part of a system can present “resilient characteristics. ”, Such as modifying the very Resilience of the system with the new technologies. The result that will be presented will describe how the whole industrial system and the human factor are connected to Resilience.

Organised by:
  • Luca Landi (luca.landi@unipg.it), University of Perugia, Italy
  • Heinrich Moedden (h.moedden@vdw.de), Verein Deutscher Werkzeugmaschinenfabriken e.V. (VDW), Frankfurt. Germany

Motivation:  Product safety and risk analysis have a direct relation with the health and safety directives and standards adopted in production machinery and machine tools. This special session will promote a discussion on the emerging problems in standardization, especially in the field of safety of machinery directive. The best practices in the industry are often not part of standards but are presented in citable papers. It is therefore imperative to understand the gap between academic research, industry and directive and standards.

Objective:  The aim is to promote a discussion between academic research and industrial about state-of-the-art solutions proposed in the field of standardization of safety related topics with a preference to topics covered by Machine Directive (2006/42/EC). We intend to test best the practice with examples of theoretical models (probabilistic) and empirical data (relative frequencies) related to safety and risk analysis in standardization. We also welcome comparisons between different standardization paradigms with clear examples to support the discussions.

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Motivation:  Lack of knowledge, e.g., about hazards, threats or attacker behavior, the performance of measures, or the impact on society and economy, makes it difficult to make risk-appropriate decisions. A promising method to address the complexity associated with today’s risk assessments is scenario analysis. An increasing threat and hazard scenario landscape poses major challenges for those responsible to make decisions regarding the investment in safety and security measures. Due to the dynamics of environmental and systemic boundary conditions, decisions have to be made under (deep) uncertainties.

Objective:  One focus will be to explore the extent to which scenario analysis can improve the accessibility of risk analysis to various modeling approaches (including AI) and metrics and the applicability to specific real-world problems. The special session aims to sharpen the methodological, modelling, and metrics tools in Decision Making under Uncertainty (DMUU).