About this Research Topic

Manuscript Submission Deadline 20 September 2022

Techniques of stochastic modelling have been widely used to simulate uncertainty in complex systems and in analysing risks in infrastructure networks. This special issue aims to further develop understanding on the theories and application of stochastic modelling as applied to risk and uncertainty in the context of infrastructure project. Incorporating diverse perspectives from economic and finance, decision science, options reasoning, design flexibility and tools such as BIM and Digital Twins, the editorial team will bring together multi-disciplinary research streams which address change and dynamic in the whole life cycle analysis of infrastructure project.

This Research Topic welcomes research addressing the following questions:
• What is the state of the arts literature on this topic, and what is the latest advance in technology which can help to better address change analysis and systems dynamics in the whole life cycle of infrastructure project?
• To what extent scholastic modelling provides an alternative perspective to the point base estimate of infrastructure finance model and what are the values for decision making posed by stochastic analysis?
• Does options reasoning and design flexibility provide an alternative framework for building resilience in infrastructure systems, and what are the key advantages and major hurdles to develop these approaches in real life project?
• How do BIM and Digital Twins help to reduce risk and uncertainty in infrastructure project, and how do these "stylised" models capture risks and uncertainties which evolve dynamically?
• How do stochastic models, options reasoning and BIM enhance the existing techniques and industry practices in the life cycle analysis of infrastructure? How do these models address human and machine behaviours and strategic interaction?

The scope of the Research Topic falls into the field of economics, engineering, planning, AI, BIM and behavioural theories. Key themes under each area are not exhaustive:
• Stochastic modelling of infrastructure, including finance and decision analysis for infrastructure projects;
• Real options reasoning on infrastructure planning and development;
• Design flexibility and modulization in infrastructure;
• BIM and digital twins in the context of infrastructure;
• Behavioural approach which examines human and machine bias and strategic interactions.

We are particularly interested in attracting papers which have strong empirical rigours and those which invite practitioners’ perspectives on the above theme while remaining theoretically advanced in subject knowledge.

Keywords: Stochastic modelling of infrastructure, Real option reasoning, Design flexibility, BIM, Digital twins, Behavioural approach to infrastructure


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Techniques of stochastic modelling have been widely used to simulate uncertainty in complex systems and in analysing risks in infrastructure networks. This special issue aims to further develop understanding on the theories and application of stochastic modelling as applied to risk and uncertainty in the context of infrastructure project. Incorporating diverse perspectives from economic and finance, decision science, options reasoning, design flexibility and tools such as BIM and Digital Twins, the editorial team will bring together multi-disciplinary research streams which address change and dynamic in the whole life cycle analysis of infrastructure project.

This Research Topic welcomes research addressing the following questions:
• What is the state of the arts literature on this topic, and what is the latest advance in technology which can help to better address change analysis and systems dynamics in the whole life cycle of infrastructure project?
• To what extent scholastic modelling provides an alternative perspective to the point base estimate of infrastructure finance model and what are the values for decision making posed by stochastic analysis?
• Does options reasoning and design flexibility provide an alternative framework for building resilience in infrastructure systems, and what are the key advantages and major hurdles to develop these approaches in real life project?
• How do BIM and Digital Twins help to reduce risk and uncertainty in infrastructure project, and how do these "stylised" models capture risks and uncertainties which evolve dynamically?
• How do stochastic models, options reasoning and BIM enhance the existing techniques and industry practices in the life cycle analysis of infrastructure? How do these models address human and machine behaviours and strategic interaction?

The scope of the Research Topic falls into the field of economics, engineering, planning, AI, BIM and behavioural theories. Key themes under each area are not exhaustive:
• Stochastic modelling of infrastructure, including finance and decision analysis for infrastructure projects;
• Real options reasoning on infrastructure planning and development;
• Design flexibility and modulization in infrastructure;
• BIM and digital twins in the context of infrastructure;
• Behavioural approach which examines human and machine bias and strategic interactions.

We are particularly interested in attracting papers which have strong empirical rigours and those which invite practitioners’ perspectives on the above theme while remaining theoretically advanced in subject knowledge.

Keywords: Stochastic modelling of infrastructure, Real option reasoning, Design flexibility, BIM, Digital twins, Behavioural approach to infrastructure


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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