Postgraduate research opportunities

Human reliability and interaction with intelligent systems

This project will develop models to assess human performance after the occurrence of a disruptive event of interconnected critical infrastructures.

Number of places

1

Funding

Home fee, Stipend

Opens

6 March 2020

Deadline

30 September 2020

Duration

42 months

Eligibility

UK/EU citizen living in UK in the last 3 years

Project Details

Foundational services that are essential to the security, development, and welfare of a society are defined as Critical infrastructures. These are often of considerable age and were not designed tocope with modern-day usage. In addition, there exist new extraordinary risks, such as climate change and terrorist attacks that are not predictable on the basis of historical data. This is confounded by the increased interconnectivity of assets, with the function of one dependent on constant input from others, this provides mechanisms for one event acutely damaging one assets performance to cascade to others. Although the human intervention remains the ultimate resource to mitigate the consequences of extreme events, Human also remain the weakest link of such critical infrastructure. In particular, the interaction of human an autonomous and intelligent systems remains largely unknown. 

This project will develop models to assess human performance after the occurrence of a disruptive event of interconnected critical infrastructures. The existing Human Reliability Analysis methods are focused on the human actions needed to maintain or activate the protection and mitigation measures of a system. It means that they are focused on the probability of a human making an error that initiates an accident event. However, if the protection measures in place fail to contain the evolution of the disruptive event, new human actions are needed. Existing research on human performance on this phase usually covers the human escaping behaviour, to define better escape routes, but not the human performance for taking the necessary actions to recover the system. Currently qualitative reliability methods do not provide the human error probability, but only its identification and possible solutions to prevent or mitigate human errors. Although some safety regulators do accept qualitative analysis on human errors, human error probabilities are required by probabilistic safety assessment. Quantitative human reliability methods such as THERP, SPAR-H, HEART, CREAM and ATHEANA are often affected by imprecision, leading to under-estimated or over-estimated probabilities. This uncertainty may be one of the causes that is preventing industries from adopting risk assessments that account for human errors.

The present research proposes to develop an innovative approach to construct an calibrate a model for calculating the human error probability using data extracted from existing major accident investigation reports. This approach has the potential to provide data that depict contexts and scenarios not fully achieved by simulator, near-misses and expert elicitation data.

Since the number of such reports are usually very small, additional information will be gained by accessing simulated data (e.g. from simulators and learning from similarities).

The methodology allows to minimise the expert judgement in the definition of human error probability, assess the uncertainty and variability of human errors under different scenarios.

 Credal Networks will be adopted to model the relationship between performance shaping factors and human errors. Credal Networks are extension of Bayesian Networks. They offer the possibility of identifying the reasons for the result of interest rather than providing single numerical values and they can deal with different representation of uncertainty by providing confidence bounds to the results.  This is particular important in the definition of the Conditional probability tables required by the model and the possibility of discriminating a lack of data from no events. For instance, observing 1 event over 2 or 50 over 100 produces the same probability but the confidence in the number need to be included in the analysis. Therefore, confidence boxes will be used to characterise such imprecision. 

Graphical models (as shown below) are also more accessible to different research disciplines, facilitating multi-disciplinary risk analysis.

Finally, the proposed approach will be applied to analyse and model human reliability during emergency and recovery situation, as they are considered to require significant human intervention to re-establish normal operation. The objective functions will be not only the safety of the workers and adjacent communities but also the business continuity, as interconnected critical infrastructure can affect essential services provided to population resulting in socioeconomic losses over time.

Aims:

  • Investigate human performance under threats and critical state and the interaction between human and intelligent systems
  • Model and quantify human reliability with confidence based on credal networks and imprecise probability.

Expected outcomes

During the PhD studies the following outcomes are expected.

  • Develop human reliability models based on credal network
  • Develop machine learning techniques to extract features from accident reports and simulators
  • Develop an efficient methodology to learn the structure of Credal Network from data
  • Quantification methods for human reliability analysis.
  • Analysis of the interaction between human and intelligent systems

 The student is expected to produce 3 scientific publications in prestigious journals and presentations at leading international scientific conferences.

STUDENT EXPERIENCE AND TRAINING

The project will provide access to the expertise of the internationally leading group in Human factor and reliability at PSI. The collaboration with CRA Risk Analysis will also create new opportunities for a future collaboration (CRA is the UK and Europe’s largest integrated Human Factors, Safety and Risk Consultancy with an enviable reputation for supplying high quality and value-for-money services to safety-critical industries).

The student will join a multi-disciplinary research group. Co-supervised by experts in Human Reliability, the students will access the state-of-the-art research and facilities at Paul Scherrer Institute (PSI, Switzerland). The supervisors are chairs of the Technical committee on Human Factors as part of the European Safety and Reliability Association (ESRA). This will provide further access to training activities. In addition, the PhD student will benefit by the strong collaboration with the European Training Network DyVirt and Urbasis providing continuous training in research related activity and transferable skills (including research ethics, programming, open research, dissemination and communication).

The student will also join the developing team of the Cossan software (https://cossan.co.uk) and he/she will participate to the collaborative development of the tools and the training activities (hackathon, challenge problems etc.)

Funding Details

UK/EU funding for fees & stipend.