List of accepted Special Sessions
 

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

S.01: Advances in Well Engineering Reliability and Risk Management
S.02: Artificial intelligence and machine learning for reliability analysis and operational reliability monitoring of large-scale systems
S.03: Transdisciplinary infrastructure asset management for sustainable and resilient infrastructure
S.04: Statistical approaches and novel methodologies for accelerated Life Testing & Degradation Testing 
S.05: Exploring new trends in Machine Learning approaches
S.06: Safety and Reliability in Road and Rail Transportation
S.07: Risk, Security and Research in the Area of Customs and Border Control: Establishing Key concepts and neglected areas of research 
S.08: Resilience-Informed Decision Making to Improve Complex Infrastructure Systems
S.09: Novel strategies for the safety assessment of dynamic and dependent systems
S.10: Human-Robot Collaboration: The new scenarios for safety 
S.11: Standardization in Risk Analysis and Safety
S.12: Dynamic risk assessment and emergency techniques for energy systems
S.13: Modeling Complexity  in  predicting Reliability and Resilience of Systems, and Systems of Systems
S.14: Digital twin: recent advancements and challenges for dealing with uncertainty and bad data
S.15: Reliability, Durability, Sustainability of Consumer Electronic Devices
S.16: Risk and resilience analysis for the low-carbon energy transition
S.17: Living near natural hazards in the age of climate change
S.18: Advanced tools and methods for occupational health and safety in logistics and assembly activities
S.19: Energy Sector Adaptation & Climate Change Vulnerability Assessment
S.20: Reliability and Resilience of Interdependent Cyber-Physical Systems
S.21: Joint ESReDA - ESRA Session on Advancements in Resilience Engineering of Critical Infrastructures
S.22: Reinforcement Learning for RAMS Applications
S.23: Fault-Tolerant and Attack-Resilient Cyber-Physical Systems (CPSs)
S.24: Artificial Intelligence, Meta-Modelling and Advanced Simulation for the Analysis of the Computer Models of Nuclear Systems
S.25: Climate Change and Extreme Weather Events Impacts on Critical Infrastructures Risk and Resilience
S.26: Bayesian Networks for Oil & Gas Risk Assessment
S.27: Transfer Learning methods for Prognostics and Health Management
S.28: Reliability and Maintenance for Internet of Things and 5G+ Networks
S.29: Natural Language Processing, Knowledge Graphs and Ontologies for RAMS
S.30: Special session on “Synergies between Machine Learning, Reliability Engineering and Predictive Maintenance”
S31: Advancing Human Factors Integration in Aviation and Maritime Domains: the SAFEMODE Project
S32: In memory of Ioannis A. Papazoglou: new methods and applications on quantified risk assessment for process and energy systems
S33: Collaborative Intelligence in Manufacturing and Safety Critical Systems. The CISC and Teaming-aI EU projects
S34: Digitalisation and risk assessment – a new ball game? (promoted by EU-OSHA)
S35: Risk Blindspots and Hotspots
S.01: Advances in Well Engineering Reliability and Risk Management

Organised by: 

 

Motivation: The world energy balance has been changing, and the oil and gas industry is facing an ultimate challenge: how to be sustainable, resilient in the next years with deep cost reduction and almost zero environmental impact and human exposure? In this scenario, Well Engineering (especially, subsea) needs to be reinvented and pushed for developing brand new, disruptive solutions in all activities. This comprises autonomous and remote offshore activities by using digital twins for production management, the development of robots for unmanned operations, prognostic and health management for predictive maintenance and real-time integrity management, and electrification. Indeed, the latter is an enabler for the adoption of most of the other initiatives due to its potential cost reduction. All those efforts are linked to digitalization in the oil and gas industry allowing for data availability and integrated databases to improve well design, technical specification, maintenance, and operational decisions.

Given that, reliability and risk management play an important role to address the above mentioned challenges. Indeed, machineries, which are installed in deepwater oil wells, are typically exposed to quite harsh conditions such as high temperature and high pressure. In spite of that, they need to be fit to function 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 oil wells. These challenges are commonplace for most of the oil and gas operators around the world and, then, are of special interest for scholars and reliability practitioners who have dealt with them. 

 

Objective: This special session welcomes papers that bring up innovative solutions for reliability and risk management within the Well Engineering field, which includes different aspects in each phase of a wellbore development (especially, subsea wells), 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.

 
S.02: Artificial intelligence and machine learning for reliability analysis and operational reliability monitoring of large-scale systems

Organised by: 

  • Ji-Eun Byun (j.byun@tum.de) Technical University of Munich (TUM), Germany

  • Fink Olga ETH Zurich, Switzerland

  • Daniel Straub,  Technical University of Munich (TUM), Germany


 

Motivation: Technological systems tend to become increasingly complex and interconnected and thereby, have a growing number of components. This makes system reliability analysis and also operational reliability monitoring challenging. In particular, the system reliability analysis needs to deal with high-dimensional probability spaces, which often leads to an exponential increase in computational cost. Furthermore, monitoring the reliability of large-scale systems may require the aggregation and propagation of system states at different hierarchical levels. Among many possibilities to address these issues, this special session focuses on the potential of Artificial Intelligence (AI) and Machine Learning (ML). Despite their potential, the applicability of these methods to reliability analysis is still highly limited at present. For example, purely data-driven approaches do not appear promising for most reliability analyses due to the lack of data on extreme system behaviour. Moreover, the ML methods are often lacking interpretability for their application in operational reliability monitoring.


Objective: This session aims to discuss the potential as well as recent implementations and developments of AI and ML for reliability analysis and operational reliability monitoring of large-scale systems, such as transportation networks, utility distribution networks, process systems, and structural systems. Methods of interest include (but are not limited to) theory-guided ML, deep learning, reinforcement learning, graph neural networks, deep Gaussian processes and probabilistic graphical models. Example applications are surrogate modelling, operational reliability monitoring, health monitoring, pre- and post-disaster management.

 
S.03: Transdisciplinary infrastructure asset management for sustainable and resilient infrastructure

Organised by: 

 

Motivation: The Sustainability Development Goals (SDGs) by the UN recognize the importance of designing resilient infrastructure in a sustainable manner, especially during the upcoming climate and energy transitions that we are facing. An infrastructure system is a Complex Adaptive System that should be seen as a combination of three major parts: the physical infrastructure, the human actors managing and making use of it, and the environment in which everything is embedded. The interactions between these components shape how the infrastructure system responds to changes. If we can capture the dynamics of such interactions, we can proactively prevent cascading failures, minimize losses, and boost recovery. Transdisciplinary approaches are thus required to properly address such a complex task.

Objective: This special session calls for research works proposing transdisciplinary approaches to infrastructure asset management for supporting the infrastructure resilience mission during the upcoming (energy) transitions and sustainable development challenges. The management of infrastructure can purposely contribute to sustainable development that addresses issues such as climate resilience, economic prosperity, and societal wellbeing. Such transdisciplinary management should be characterized by the uncertainty of future disturbances (e.g., hazards), the high interdependency among the interconnected systems, and the existence of multiple stakeholders whose requirements might conflict with one another. Contributions which focus on different aspects of the asset management process, including design, operation, and maintenance are welcomed.

 

This special session supports a broad spectrum of topics that aim at boosting infrastructure resilience and management with a concrete vision on the upcoming transitions such as climate change and ongoing urbanization. This includes but not limited to:

  • Emphasizing the role of infrastructure interdependency and stakeholders coordination/participation towards a sustainable a resilience infrastructure;

  • Understanding costs (investments) and benefits (results) for resilience and sustainability improvement measures;

  • Assessing/prioritizing needs and goals for the sustainable growth of communities;

Exploring the role of tools such as simulation and Serious Games in the co-design process of circular solutions.

 
 
S.04: Statistical approaches and novel methodologies for accelerated Life Testing & Degradation Testing

Organised by: 

 

Motivation: Thanks to the ever improving manufacturing process and technology, products and devices are becoming highly reliable with substantially long life-spans these days, which makes the standard testing procedure at normal operating conditions practically unfeasible. For gaining sufficient information about the lifetime distribution of a product or even a prototype, such tests are too time-consuming and costly to the industry. For such reasons, accelerated life testing (ALT) and degradation testing (ADT) are not only getting increasingly popular but also necessary as they quickly yield information on the lifetime distribution of highly reliable products in a shorter period of time. ALT and ADT subject the test units to more extreme stress levels than normal operating conditions so that more information about the lifetime characteristics of a product or device can be collected rapidly. Through extrapolation, the lifetime distribution at the usage stress is then estimated with an appropriate regression model. 


Objective: The special session aims to present current research on statistical modelling, design and inference for accelerated life testing and accelerated degradation testing. The session aims to bring together both academic and industrial researchers and practitioners interested in theoretical developments and practical applications in this reliability field.

S.05: Exploring new trends in Machine Learning approaches

Organised by: 

 

Motivation:  In recent years, the risk and reliability community has expressed immense interest in data-driven models and their application to risk assessment and condition-based monitoring. In particular, machine learning and deep learning techniques have been widely adopted by researchers for problems in which some type of operational data exists and therefore the challenge resides in finding a model that can be trained on such data (learning process) and then correctly identifies the learned patterns in new, unseen data (inference process). For at least the last five years, this learning-inference idea has been further refined with more complex models, better ways to process the data and improved training algorithms. Nevertheless, as the understanding of these techniques become more mature and their application more widespread in the industry, the general machine learning research community has been shifting its focus to novel approaches that depart from the most common AI techniques to explore new and promising ideas. 

 

Objective: We firmly believe that the risk and reliability community could benefit immensely from exploring these novel techniques, both from a theoretical and application point of view, to prepare itself for the new challenges that a hyper-digitalized world and industry will entail in the near future. For that reason, we propose as a starting point the following 4 novel machine learning trends to serve as guidelines for this special session:

 

  • Physics Informed Machine Learning

  • Quantum Machine Learning

  • Causality in Machine Learning

  • Model Interpretability

 
 
S.06: Safety and Reliability in Road and Rail Transportation

Organised by: 

 

Motivation: Road and rail transportation form the cornerstone of our movement and connect societies through trade, culture, security and forms some of our infrastructure lifelines. On the other hand, they are constantly exposed to uncertain and significant hazards from natural and anthropogenic sources. The infrastructure assets related to these sectors are ageing, demands on their performance are on the rise and the owners (both government and private) do not have adequate funds to replace them. Under such circumstances, there is a fundamental need to understand, assess, calibrate, analyze and decide on managing and resolving such hazards. To make this knowledge translate to practice, there is also a need to create an extensive evidence base around the topic. Several EU projects, like the EU Interreg Atlantic Area funded SIRMA projects are investigating such hazards and trying to provide better guidelines and recommendations, underpinned by a fundamental understanding of uncertainty, risk and reliability. 

 

Objective: This mini-symposium intends to bring a range of scientists, practitioners, owners and policy-making stakeholders to address this contemporary and significant issue. We invite fundamental and applied contributions from numerical and experimental perspectives to create a robust discussion around the topic.

S.07: Risk, Security and Research in the Area of Customs and Border Control: Establishing Key concepts and neglected areas of research 

Organised by: 

 

Motivation: The functions of customs and border control have been formalised in both institutions, and in the official role of the customs officer since the days of the City-state. Today the forces of globalization such as increased international standardization, increased digitalization and the steady increase of the flow of goods, people and ideas are changing the organizational environment of customs and border control institutions, creating new challenges and opportunities.  


Objective: The aim of the special session is to bring together researchers and specialists from a variety of disciplines, to present research related to customs and border control, to discuss various disciplinary contributions to the field, to isolate neglected areas of research,  and to establish key concepts in a joint platform for future research. 

 
S.08: Resilience-Informed Decision Making to Improve Complex Infrastructure Systems

Organised by: 

 

Motivation: Infrastructure systems are the cornerstone of the economic prosperity and well-being of societies. Given their vital role, it is of utmost important to limit their exposure and vulnerability to potentially disruptive events, i.e. events that can negatively affect the provision of service resulting in excessive direct and indirect consequences. To limit such adverse consequences, infrastructure managers need to regularly assess the resilience of their systems and plan resilience enhancing interventions. Such need is exacerbated as the frequency and intensity of climate-induced hazards, such as heavy rainfall, flooding, and landslides are increasing due to effects of climate change. An important tool to facilitate the assessment and planning of interventions is the ability to simulate the probabilistic nature of hazard, their effect on infrastructure systems and the extent to which potential interventions can improve their resilience.

Simulation of complex infrastructure systems for this purpose is a challenging task. The challenges include the modeling of the occurrence and spatiotemporal evolution of hazards, assessing their impact on the functionality of infrastructure components and systems, and the process for response and recovery measures. Additionally,  more challenges are introduced when considering different types of resilience enhancing interventions, e.g. diverting rivers, strengthening bridges, rerouting traffic, and evaluating their effects on system resilience. This is in addition to the uncertainty inherent in every step of the simulation process. Overcoming these challenges to enable the evaluation of the resilience of complex systems would greatly improve the ability of infrastructure mangers to cost-efficiently improve the resilience of their infrastructure systems.

 

Objective: This session will provide a platform for the researchers in the fields of risk and resilience of complex infrastructure systems to present their recent advancements, exchange their ideas, and interact in the charming city of Dublin. The session will be mainly focused on modeling resilience improvement interventions and evaluating their effect on spatially distributed hazard events and consequences. The related topics are as follows, but not limited to:

  • Pre-hazard and mid-hazard intervention programs

  • Post-hazard restoration intervention programs

  • Climate adaptation and mitigation measures

  • Modeling the spatiotemporal evolution of hazards and infrastructure systems

  • Effect of uncertainties on resilience enhancing decision making

  • Effect of climate change on resilience enhancing decision making

  • Simulation of spatially distributed infrastructure networks

  • Resilience assessment of complex infrastructure systems

  • Simulation methodologies to model complex infrastructure systems

  • Quantifying direct and indirect consequences of hazard events on infrastructure systems.

 
S.09: Novel strategies for the safety assessment of dynamic and dependent systems

Organised by: 

Motivation: Most common risk analysis techniques, such as fault and event trees, are well-rooted in engineering practice thanks to their computational efficiency and numerical robustness. However, their ability to model the complexity of modern engineering systems is limited. Such techniques lack the ability to integrate into the probabilistic analysis crucial aspects of systems behaviour, such as component dependencies and dynamic features, on which many safety aspects of modern systems rely. This has resulted in the adoption of a plethora of alternative techniques (e.g. Petri Nets, Markov Models etc.) which offer high modelling accuracy but that, due to their complexity, can easily become computationally unfeasible when applied to industrial-scaled systems. The identification of novel strategies for the accurate and efficient modelling of complex engineering systems plays therefore a crucial role in the enhancement of reliability and safety analysis practice. 

Objective: The main aim of the special session is to promote the on-going debate around the needs for more realistic system modelling, identifying the weakness of current methodologies and addressing potential solutions able to tackle their limitations, both in terms of modelling resolution and computational efficiency. The objective is to provide a clear representation of the current state of the art in terms of cutting-edge system reliability methodologies, as well as to highlight the capabilities still missing and the direction of future research. 

 

The proposed special session will welcome papers addressing the limitations of current risk methodologies and system reliability analysis practice. Novel numerical strategies and their application to significant case-studies will be considered. 

 
S.10: Human-Robot Collaboration: The new scenarios for safety 

Organised by: 

 

Motivation: Industry 4.0 is a high-tech approach for automating manufacturing utilizing the Internet of Things to create smart factories, a new organization of the entire process and management.

 

In this paradigm, human-robot collaboration becomes more and more critical. In fact, both in the smart factory and in everyday life, the use and interaction of robots are essential and will grow in the future. According to this scenario, all the dispositive and robots have to be designed and managed  Men have to collaborate and work safely with robots. 

 

Furthermore, the company may face dangers or opportunities due to implementing these technologies. Indeed, the deployment of Industry 4.0 has revealed that the real-time connections between persons, systems, and things have become a more complex, dynamic, and optimized network. On the other hand, new infrastructure, management, and technology requirements emerge as the volume and availability of real-time data grow.


Objective: In this track, we would like to study all the aspects regarding the human-robot collaboration in the new era and all the smart disposals with attention to safety, maintenance, and process management.

 
S.11: Standardization in Risk Analysis and Safety

Organised by: 

  • Landi Luca, luca.landi@unipg.it, University of Perugia, Italy, Associate professor of Machine Design, president of UNI CT024 Machine Tools, Italy

  • Heinrich Moedden (h.moedden@vdw.de), Senior Expert on Safety of Machine Tools, German Association of Machine tool builders, Frankfurt. Germany

Motivation: Admittedly, theoretical risk assessment starts in the hypothetical “what-if”  domain, where theoretical risk can be logically modelled in cause and effect (at the most), but it often cannot be calculated accurately as regards absolute values (i. e. severities of damage and their likelihood), since assumptions have to be made always for the situation at hand. Thus, in risk estimations, the necessary theoretical model-based approach is sensible (probabilities), but it needs to be checked by empirical data (“real” relative frequencies), too.

On the other side, verifiably founded probabilities, such as e.g. real findings in the operational field and logically deduced probability estimations are not better than a scalable subjective good feeling, speculation and pure hypothetical assumption. Isn’t it obvious that the real risk actually can be measured precisely, e.g. in terms of the yearly accident statistics, i.e. in terms of objective numbers? For the sake of operator‚ safety of course, only real risk reduction matters: it is a combination of a) reducing the magnitudes of consequences of possible failures and, b) simultaneously diminishing the frequencies of such consequences.

Objective: Therefore, the special session tries to support plausible risk considerations connecting theory and reality. This is also a goal of the special session "Risk analysis and safety in standardization", because unfortunately, in the discussions of safety standardization experts, it seems to be the most important goal to establish a formal procedure, which could be suitable to defend against possible product liability suits. Furthermore, a majority of experts prefers a simplistic “worst-case scenario” approach rather than probabilistically scaled realistic methods. Reasonable cause and effect relations do not seem to be important to the majority of experts, nor a plausible scaling of effects. Corresponding safety designs may be “legally safe” but their risks to the operators are presumably not at all “as low as reasonably practicable” (ALARP). Only if the decisions that form a safety design are plausibly justified (i.e. objectively), the risk in the ALARP-range can be achieved. Therefore, an understanding of the design requirements is all-important to the readers of safety standards.  

Accordingly, the aim of the special session is to promote bridging/discussion between academic research and industrial 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).

 
S.12: Dynamic risk assessment and emergency techniques for energy systems

Organised by: 

Motivation: Safety and reliable energy systems, that include fossil and renewable energy sources, are indispensable for our daily activities. Due to the increasing coupling and complexity in energy systems and tasks, their operational risk has grasped the increasing attention from the domain of industry and academia. Through risk assessment, engineers can dynamically monitor and manage the risk in the systems and by conducting emergency techniques, practitioners can effectively control and mitigate the possible consequences. 

The risk assessment and emergency techniques have been investigated in the literature, particularly those in the area of safety and reliability. Along with the growing complexity, with considerable involved elements, the hybrid of risk assessment methods (mainly for reducing risk probabilities) and emergency techniques (primarily for reducing risk consequences) also requires further discussion and investigation.

Objective: This special session aims to provide a platform to disseminate new methods and applications for the dynamic risk assessment and emergency technologies, particularly for the energy systems (e.g., fossil energy systems, energy storage systems, nuclear power plants, hydrogen energy systems). The special section invites researchers from academia and industry to share research and experience on the study of risk assessment, risk warning, risk control, emergency preparedness, emergency response, and emergency recovery (not limited to). The multi-disciplinary (e.g., reliability engineering, safety engineering, operations research, artificial intelligence) methods and applications are encouraged.

 
S.13: Modeling Complexity  in  predicting Reliability and Resilience of Systems, and Systems of Systems

Organised by: 

Motivation: Beyond traditional RAMS metrics, resilience is increasingly a desirable characteristic of complex systems, and systems of systems. Resilience includes the ability to withstand 'shocks' and to recover from them. While the controllable systems of yesterday were characterized by weakly connected or independent components, today's complex systems comprise strongly connected or interdependent components. So, instead of being dominated by their components, complex systems are rather defined by their interactions.

Nonlinearities and behaviour close to instability must be taken into account.  Especially when dealing with systems of systems, i.e. ensembles of heterogeneous systems that are mostly independent but most coordinate occasionally to fulfil an overall function, somewhat unpredictable behaviours occasionally emerge. 


Objective: This calls for novel risk management strategies to build resilience. Critical infrastructures such as large electrical power grids, multimodal ground transport systems, or supply chain networks, fall in that category.

 
S.14: Digital twin: recent advancements and challenges for dealing with uncertainty and bad data

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 tradeoff 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 on digital twinning and uncertainty quantification 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?

 
S.15: Reliability, Durability, Sustainability of Consumer Electronic Devices

Organised by: 

Motivation: Consumer electronic devices are ubiquitous in our daily life. Millions of such devices are manufactured, purchased, and discarded every year. Consumers and regulators / regulatory bodies often consider the sustainability of the products when making purchasing decisions. Sustainability of the device can be achieved through the extension of useful life, which in turn calls for reliable, durable, and (possibly) repairable devices. Historically, the consumer electronics industry has been very secretive on the topics of product design as well as product reliability and product useful life beyond "limited" manufacturer warranty. As a result, the topics of reliability, durability, and reparability in consumer electronics have been mainly addressed by tech enthusiasts (JerryRigEverything) and repair businesses (iFixit, iMyPhone). Promoting sustainability within consumer electronics has been hindered because open forum discussion about reliability and durability is not occurring.

Why now: 1) Increased consumer attention to sustainability; 2) Increase regulatory interest in reparability, durability, sustainability. Several examples include: “Right To Repair” (EU legislation, US discussion) and “Durability Index” (France).

Increase of device useful life is a common goal for groups promoting sustainability and companies building eco-systems of connected consumer devices. Reliable electronic devices working seamlessly with each other benefit both sustainability effort as well as eco-system business model. The monetization model for eco-system is pay-for-service. Thus, device manufacturers interest of producing long-lasting products aligns with the sustainability vision. At the moment, several big tech companies, such as Amazon, Apple, Facebook, Google, Microsoft, Xiaomi, Yandex, are building Connected Smart Home eco-systems. 

Objective: Currently, quantitative data on consumer device reliability, durability, and sustainability is limited. Also, interpretation of term definitions and test methodologies often differs from one manufacturer to the other. At the same time, consumer attention to the sustainability of products is increasing, as well as regulatory attention to reparability, durability, and sustainability. To help move the field forward, we propose the following topics for the session and open forum discussion:

  • Reliability of consumer electronics devices: reference test conditions, metrics, standards, targets. Can we enable customers to compare the reliability of products from different manufacturers?

  • Reliability targets;

  • Reliability test conditions and standards;

  • Reliability evaluation.

  • Durability of consumer devices: definitions and metrics.

  • Sustainability of consumer electronics devices: definitions and approaches.

  • Durability and sustainability are relatively new concepts in the consumer electronics domain, and need a common understanding of terms and approaches to measure impact.

 

This session is intended for discussion among device manufacturers (reliability and product design experts from industry), academic researchers (reliability focus), and policymakers (consumer electronics focus).

 
S.16: Risk and resilience analysis for the low-carbon energy transition

Organised by: 

Motivation: Energy systems are critical infrastructures that must fulfil present demand. At the same time, energy systems must evolve to become less CO2-intensive and inclusive of new technologies. These goals are broadly agreed upon but what is less clear is the pathway that ought to be taken to achieve these ideal future systems. The importance of energy systems to achieving sustainability goals motivates the desire to have risk-aware transition planning and resilient transition pathways.

 

This special session highlights current approaches for risk and resilience analysis in the low-carbon energy infrastructure transition. In particular, the session highlights quantitative methods for managing risks inherent to sustainable transitions themselves and in ensuring a resilient transition. 


Objective: This special session will provide an opportunity to promote and disseminate current state-of-the-art quantitative methods and their applications for risk management and resilient transition planning in the context of energy systems.

 
S.17: Living near natural hazards in the age of climate change

Organised by: 

Motivation:  Understanding and adapting to climate change is one of the greatest ongoing societal challenges. It is of primary importance to develop knowledge and tools to make sense of and deal with the effects of climate change on society, i.e. the ability to protect the life and health of its citizens and to maintain critical infrastructure and function. The purpose of this special session is to present the ongoing research of the project called "Risk governance of climate-related systemic risk in the Arctic".  In particular, the key role the Arctic plays in understanding and mitigating the challenge of climate adaptation, as the climate already is changing more rapidly in these regions than anywhere else in the world. The case of the project is Longyearbyen, a Norwegian settlement at 78 degrees North at the archipelago Svalbard.  Research results on successful risk governance strategies developed in response to destabilized climate conditions in Arctic locations serve as important experiences for future climate change adaptation in other relevant parts of the world.

Objective: The purpose of the proposed special session is to present the results of an ongoing research project called "Risk governance of climate-related systemic risk in the Arctic".  In the special session, we want to present results so far in the projects. In addition, we will invite members of our international scientific reference group to submit papers to the special session.

 
S.18: Advanced tools and methods for occupational health and safety in logistics and assembly activities

Organised by: 

Motivation: Growing business competition pushes companies to continuously enhance their operational performance by considering both the effectiveness and efficiency of logistics and assembly activities. Consequently, companies are under immense pressure to meet increased consumer demand while at the same time maintaining a safe working environment. The logistics industry vitally ensures the storage and delivery of goods around the country and involves not only haulage and distribution but warehousing and material handling as well. It is a complex industry that exposes workers to a whole variety of risks. These include not only accidents on the road and deaths and injuries resulting from unsafe use of forklifts, but also the consequences of poor fire safety, long-term health risks due to poor manual handling techniques, and problems relating to mental health. In addition, in the last few years, workers have recorded an increasing number of serious work-related accidents, diseases, and dangerous incidents while performing assembly activities. Numerous factors can cause accidents, ranging from overexertion to mishandling of hazardous materials. There are also a multitude of variables that can contribute to influencing a workplace incident such as, for instance, lack of training, documentation, and procedures. All workers are responsible for managing risks associated with their work activities and supervisory staff, at every level, has specific responsibilities to oversee the implementation of effective occupational health and safety risk management. Designing the right tools and methods will help to identify, evaluate and mitigate risks, and to reduce the complexity of risk management, and improve their operational performance maintaining, at the same time, a safe work environment. The Special Session focuses on the development and implementation of advanced tools and methods for occupational health and safety risk management in work activities, with a particular focus on internal and external logistics and assembly activities.

 

The Special Session focuses on the development and implementation of advanced tools and methods for occupational health and safety risk management in work activities, with a particular focus on internal and external logistics and assembly activities. Internal logistics, which involves several processes such as material handling and warehouse, and external logistics, which involves haulage and distribution expose employees to a large number of risks. In addition, workers have recorded an increasing number of serious work-related accidents, diseases, and dangerous incidents while performing assembly activities. Designing the right tools and methods will help companies to identify, evaluate and mitigate risks and to reduce the complexity of risk management to build a safe work environment.

Objective:  The main aim of the Special Session is to promote critical discussion between academic research and industrial state-of-the-art solutions in the field of occupational health and safety risk management in internal and external logistics and working activities. Submission for this Special Session include but are not limited to the following topics: 

  • Tools and methods for occupational health and safety risk management in internal logistics, such as warehousing, stock control, storage systems, and material handling;

  • Tools and methods for occupational health and safety risk management in internal logistics, such as haulage and distribution;

  • Tools and methods for occupational health and safety risk management in manual assembly activities;

  • Tools and methods for the occupational health and safety risk management for workers with special needs, such as pregnant women, young workers, ageing workers, and workers with disabilities;

  •  Implementation of safety control measures to minimize risks during the handling and storage of heavy loads;

  • Occupational health and safety risk management considering the cooperation of workers and machines in assembly lines;

  • Occupational health and safety risk management in digital factories;

  • Occupational health and safety risk management in Industry 5.0: new methods and case studies;

  • Risk assessment at work and prevention strategies during COronaVIrus Disease 19 pandemic.

 
S.19: Energy Sector Adaptation & Climate Change Vulnerability Assessment

Organised by: 

  • Tarannom Parhizkar (parhizkar.t@gmail.com

  • B. John Garrick Institute for the Risk Sciences, University of California, Los Angeles (UCLA)

Motivation:  The energy sector is crucial to almost all the critical facilities and infrastructure systems on which the modern world relies. Climate change and extreme weather events endanger energy sector reliability thereby limiting power system operability and serviceability. Therefore, proactive mitigation plans and solutions to improve the resilience of systems must be designed and implemented to deliver significant benefits to the current and future users and industries. While the existence of the threat of climate change is known, there are specific and challenging requirements to assess and mitigate climate risk to the resilience of the electric sector.

Objective:   Our focus in this session is on research projects that study the methods to increase the resiliency of the energy sector to climate change and extreme weather events. The proposed session intends to cover scientific research that focuses on anticipating and adapting to climate and weather-related challenges to increase energy sector resiliency. Weather-related challenges include, but not limited to:

  • Increases in water temperature:  That is likely to reduce generation efficiency, especially where water availability is also affected.

  •  Increases in air temperature: It will reduce generation efficiency and output as well as increase customers‚Äô cooling demands, stressing the capacity of generation and grid networks.

  •  Changes in precipitation patterns and surface water discharges, as well as an increasing frequency and/or intensity of droughts: It may adversely impact hydropower generation and reduce water availability for cooling purposes to thermal and nuclear power plants.

  •  Extreme weather events, such as stronger and/or more frequent storms: They can reduce the supply and potentially the quality of fuel (coal, oil, gas), reduce the input of energy (e.g., water, wind, sun, biomass), damage generation and grid infrastructure, reduce output, and affect security of supply.

  •  Rapid changes in cloud cover or wind speed: It can affect the stability of those grids with a sizeable input of renewable energy.

  •  Sea level rise: it can affect energy infrastructure in general and limit areas appropriate for the location of power plants and grids.

 
S.20: Reliability and Resilience of Interdependent Cyber-Physical Systems

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 significant 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.

This special section 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 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

 
S.21: Joint ESReDA - ESRA Session on Advancements in Resilience Engineering of Critical Infrastructures

Organised by: 

  • Giovanni Sansavini (sansavig@ethz.ch), ETHZ, Switzerland, Chair of the ESRA Technical Committee on Critical Infrastructure

  • Rasa Remenyte-Prescott (r.remenyte-prescott@nottingham.ac.uk), University of Nottingham, UK, Lead of ESReDA PG on Resilience Engineering and Modelling of Networked Infrastructure


 

Motivation:  We believe that this topic is important to the field of safety and reliability assessment. Our modern society is dependent on many critical infrastructure systems. These include transport networks (rail, metro, highway, air traffic and shipping routes), utilities (electricity, gas, water) and communications (mobile phone, landline phones, internet). The disruption of such systems can have a big impact on the communities that they serve. The nature of the threats to these systems is also changing and includes failures, especially of ageing infrastructure, natural disasters, the effects of climate change and deliberate acts such as terrorism. Such critical systems need to be resilient and there is a need for knowledge and skills of how to model and assess the resilience of such systems. 

 

Objective: This special session will focus on highlighting and disseminating current state-of-the-art methods and their practical applications in resilience engineering of critical infrastructures. We expect to have contributions for this special session jointly supported by the membership of ESReDA Project Group on Resilience Engineering and Modelling of Networked Infrastructure and by the members of the ESRA Technical committee on Critical Infrastructure.

 
S.22: Reinforcement Learning for RAMS Applications

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.

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. 

Objective: This special session will host contributions showing the potential of RL for RAMS, as well as theoretical and technological enhancements.

 
 
S.23: Fault-Tolerant and Attack-Resilient Cyber-Physical Systems (CPSs)

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.

S.24: Artificial Intelligence, Meta-Modelling and Advanced Simulation for the Analysis of the Computer Models of Nuclear Systems

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 a 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).

Objective: Within this broad framework, this Special Session is aimed at gathering expert researchers, academics and practitioner 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.

A list of related candidate topics applied to nuclear systems includes, but not limited to:

  • Sensitivity Analysis methods 

  • Forwards Uncertainty Quantification methods

  • Inverse Uncertainty Quantification methods

  • Failure Domain Characterization methods 

  • Safety Margins Quantification methods

 
S.25: Climate Change and Extreme Weather Events Impacts on Critical Infrastructures Risk and Resilience

Organised by: 

  • Francesco Di Maio (francesco.dimaio@polimi.it), Politecnico di Milano, Italy 

  • Masoud Naseri, University of Tromsø - The Arctic University of Norway, Norway

  • Enrico Zio, Politecnico di Milano, Italy, Ecole de MinesParisTech, France

Motivation: Economic and social consequences of natural hazards (such as surface flooding, river

flooding, landslide, heatwave, wildfire hurricanes, floods, droughts, coastal flooding) have gained a

A lot of national and international attention in the last decades, since the 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, analysing 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, analysing 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, the intensity of exposure of critical infrastructures, the performance of existing response and recovery measures, interdependencies and complexities of critical infrastructure networks, widespread failure impacts and associated 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 to risk and resilience assessment of critical infrastructures while accounting for climate change. Related topics are listed as, but not limited to:

  • Natural hazards modelling as stressors for critical infrastructures

  • Spatial/temporal modelling and simulation of extreme climate events

  • Climate change and its impact on critical infrastructure networks resilience

  • Climate adaptation

  • Natural hazards risk and susceptibility maps

  • Extreme spatial hazards and risk of disruptions of multiple infrastructure systems

  • System-of-system approach to risk and resilience assessment of interdependent critical

  • infrastructure networks

  • Cascading failures

 
S.26: Bayesian Networks for Oil & Gas Risk Assessment

Organised by: 

  • Luca Decarli,  Eni, Italy

  • Francesco Di Maio (francesco.dimaio@polimi.it), Politecnico di Milano, Italy 

  • Enrico Zio (enrico.zio@polimi.it),  Politecnico di Milano, Italy, Ecole de MinesParisTech, France and Politecnico di Milano, Italy

 

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. 


 

Objective:  The special session aims at presenting the most recent advancements in Bayesian Network modelling for risk assessment in O&G industry, challenges and perspectives. 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

 
S.27: Transfer Learning methods for Prognostics and Health Management

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, coming from the same input feature space (defined by the same signals) and contains the same labels.

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).  


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

 
S.28: Reliability and Maintenance for Internet of Things and 5G+ Networks

Organised by: 

  • Dr. Yan-Fu Li, Professor, Department of Industrial Engineering, Tsinghua University

  • Dr. Muxia Sun, Assistant Researcher, Department of Industrial Engineering, Tsinghua University

  • Enrico Zio, Professor, Centre de Recherche sur les Risques et les Crises (CRC), MINES ParisTech/PSL University Paris, France and Energy, Politecnico di Milano, Italy

 

Motivation: Internet of Things (IoTs), as the foundation of cyber-physical systems (CPSs), connect real-world industrial systems or products through Internet-based cyber networks, potentially achieving coordinated monitoring, communication, computing, control and decision-making among large-scale interconnected physical entities. 

In this context, the 5G+ wireless communication network represents a critical part of the modern/future CPSs, providing solid infrastructure functions, such as the ultra-reliable low latency communication (uRLLC) for critical information transmission, and, the massive machine-type communication (mMTC) for the internet of massive industrial devices. 

The reliability analysis of IoTs and 5G+ networks is a key challenging issue for their success. The capability of the cyber services to satisfy the demands from the physical counterparts can be a bottleneck on its own, for Quality of Service (QoS) performance, including the end-to-end latency, data rate and package loss in communication systems. Then, the quantification of the relation between the cyber QoS performance and the reliability of the corresponding physical production/service quality is essential for the reliability-and-maintenance based design of future 5G+ networks/IoTs/CPSs. 

 

Objective: In this session, we aim to discuss the following topics (and others) related to reliability and maintenance analysis of IoTs and 5G+ networks:

1. Definition and assessment of the end-to-end service reliability of IoTs and 5G+ networks.

2. Reliability allocation and maintenance scheduling for 5G+ networks in service of industrial/consumer objectives.

3. Maintenance scheduling policy design for industrial systems under IoTs.

4. Risk analysis for future industrial systems, IoTs, 5G+ communication networks, and CPSs.

 
S.29: Natural Language Processing, Knowledge Graphs and Ontologies for RAMS

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

 
S.30: Special session on “Synergies between Machine Learning, Reliability Engineering and Predictive Maintenance”

Organised by: 

 

Motivation: Over the last decade, machine learning has been irrigating the field of reliability engineering in multiple ways. Classical statistical approaches, typically parametric estimation, allow for the estimation of a small number of parameters that characterize a population. However, if we add a number of supplementary parameters—i.e.  ‘covariates’, which characterize mission profiles of various individuals of the population (the context, the stresses to which they are subjected, the number of hours under power, the asset’s maintenance history, etc.), and which can vary considerably from one individual asset to the next, classical statistical methods  tend to break down because sample sizes are usually  too small with respect to the number of parameters to be estimated. 

New methods can now be drawn upon which, rather than relying on parametric estimation, utilize algorithms to explore big data sets and  progressively to discover  structures in those data sets, i.e. performing  exploratory data analysis. 

Those methods are made possible thanks to several advances:   

 

 i.The ability, with the IoT, to acquire massive amounts of data that reflect the diversity in the various individuals of  a population;

 ii.The appearance of new algorithms based   on machine learning ( and often relying on the Bayesian approach);

 iii.The ability to process efficiently vast amount of data in real time  thanks to parallel processing (with architectures such as GPU). 

 

Those advances endow the reliability engineer with the ability to monitor individually each asset, and thus to tailor reliability predictions to each individual  asset’s  specific operational context,  and subsequently  to perform PHM  (prognostics & health management), essentially predicting the remaining useful life of the asset and devising a customised maintenance policy; instead of issuing only population-based  predictions and following the same maintenance policy for the entire population in spite of the diversity of mission profiles to which the various individuals that constitute it are subjected. 

Asides from PHM, exploratory data analysis and machine learning  can help identify poor performers in a population, and also  bring increased efficiency to fault troubleshooting.

 

Finally there are potential benefits to PHM that may  result from reliability engineering techniques developed since the 1950s in survival analysis ( e. g. the study of mean residual life and, more generally, residual life distribution.

 

Objective: The purpose of this special session is to explore synergies between machine learning, reliability engineering and advanced maintenance management such as predictive maintenance; and to illustrate ,on specific application domains such as rail transport, electric power generation and transmission, and aerospace, the benefits brought about by those synergies and the obstacles encountered.

 
S31: Advancing Human Factors Integration in Aviation and Maritime Domains: the SAFEMODE Project

Organised by: 

 

Motivation: Aviation is seen as the safest form of passenger transport, and ships transport ninety per cent of goods globally today. But how do these two very people-centred domains deal with Safety and Human Factors, and what can they learn from each other? Such questions led the European Commission to launch the three-year, 10 million euro SAFEMODE project, with 33 partners from diverse aviation and shipping organisations around the world, coming together to develop a unified framework for achieving better Safety and Human Factors integration into design and operations. The SAFEMODE project has followed five threads of research: a common taxonomy leading to a new incident and accident database called SHIELD; the development and testing of a Human Factors toolkit for aviation and maritime; expanded use of formal risk models in both domains; a common overarching framework incorporating all SAFEMODE tools and processes called HURID (Human Risk-Informed Design); and testing all of these elements via six major case studies, three each from aviation and maritime, ranging from a new cockpit alert, to remotely-piloted ships.

 

Objective: This mini-symposium will present papers from SAFEMODE partners, highlighting the best Human Factors practices that were found to deliver safety benefits to the organisations involved, and will summarise what each domain has learned from the other.

 
 
S32: In memory of Ioannis A. Papazoglou: new methods and applications on quantified risk assessment for process and energy systems

Organised by: 

  • Olga Aneziris, olga@ipta.demokritos.gr, Institute for Nuclear and Radiological Sciences, Energy, Technology and Safety (INRASTES), NCSR Demokritos, Greece

  • Myrto Konstantinidou myrto@ipta.demokritos.gr Institute for Nuclear and Radiological Sciences, Energy, Technology and Safety (INRASTES), NCSR Demokritos, Greece

 

Motivation: This session is dedicated to the memory of Ioannis Papazoglou, who passed away in 2021. Therefore, in this session, research in the fields where Ioannis Papazoglou pioneered may be presented. He worked with great devotion for ESRA and ESREL conferences, as ESRA chairman in the period 2005-2009 and officer of the association from 2000 to 2005. He was the general chairman of two ESREL conferences, organised in 1996 and 2010 in Greece.

 

Objective: This special session aims to provide new methods and applications on quantified risk assessment in the fields of chemical and process industry as well as energy systems and other relevant sectors. The special section invites researchers to share research and experience on the study of quantified risk assessment which can be used for different aspects such occupational safety, emergency preparedness, emergency response etc. The most recent advancements in risk assessment methods, such as dynamic methods and bowties may also be presented.

 S33: Collaborative Intelligence in Manufacturing and Safety Critical Systems. The CISC and Teaming-aI EU projects

Organised by: 

  • Maria Chiara Leva, mariachiara.leva@tudublin.ie, Coordinator for the CISC Marie Curie ITN project, Technological University Dublin Ireland  

  • Hector Diego Estrada Lugo, HectorDiego.EstradaLugo@TUDublin.ie , project manager for the CISC Marie Curie ITN project, Technological University Dublin Ireland 

  • Thomas Hoch Thomas.Hoch@scch.at , Key Data scientist for Teaming-AI EU project, Software Competence Center Hagenberg (SCCH) , Austria

 

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: This special session aims to provide an overview of approaches and use cases in smart manufacturing scenarios where AI technologies are shaping them by introducing greater customisation and personalisation of products and services and where, at the same time, the human performance aspect is also still very relevant. Therefore the session aims to share current interim development of new human and AI teaming frameworks in manufacturing processes where the greatest strengths of both these elements can be maximised while safety and ethical compliance guidelines are examined and maintained. (For more info see https://www.ciscproject.euhttps://www.teamingai-project.eu )

 
S34: Digitalisation and risk assessment – a new ball game? (promoted by EU-OSHA)

Organised by:

European Agency for Safety and Health at Work (EU OSHA)

Tim Tregenza tregenza@osha.europa.eu

Motivation

Legislation such as the 89/391/EEC “Framework” Directive requires employers to protect their workers from harm through the application of general principles of prevention including a “risk assessment” approach and occupatioanl safety and Health (OSH) managemetns.

Digitalisation is changing the world of work, and this includes the prevention process. Not only may the exact employment status of a worker not be clear (e.g. in the platform economy), the employer may not be in a work environment (i.e. teleworking from home), and the employee is working with a cobot.

Moreveover the actual process of risk assessment may be changing. Managers may be recieving OSH data in real time (e.g. through wearable technology) and be using algorythmnic management tolos to identify and implement prevention measures.

This session will discuss these changes and identify the benefits and challenges, drivers and barriers to better Occupational Safety and Health in the digital age.

Objectives

The purpose of the session is to highlight that the world of work – and of occupational safety and health management is changing – bringing new opportunities and challenges. Themes include digital OSH management (including the use of data from smart wearables and other technology, management of remote workers, management of platform workers.

 
 
S35: Risk Blindspots and Hotspots

Organised by:

LRF Institute for the Public Understanding of Risk

Motivation

Risk perceptions of the general public in relation to a source of risk sometimes diverge widely from expert assessments of the same risk. These ‘risk perception gaps’ can result in ineffective risk management interventions, high enforcement costs, loss of trust and, potentially, conflict between the public and authorities. This Workshop is convened by the LRF Institute for the Public Understanding of Risk as part of an ongoing research project which aims to assess the prevalence and severity of gaps in risk perceptions between experts and the general public and identify the factors that explain their presence and persistence. 

Objectives

The Workshop aims to elicit experts’ views on risks which the public tends to estimate much more highly than experts and risks that experts rate much more highly than the public through a facilitated discussion and live polling. The Workshop at ESREL 2022 is the second in a series; further workshops will take place in Singapore, Korea and China in 2022-2023. The data on expert perceptions will be complemented by surveys and focus group discussions on public risk perceptions in European and Asian countries.