AI4SURE: AI-empowered data-mining techniques for SUstainable and climate-REsilient infrastructure peacebuilding
Funding: British Academy Fellowship & University of Birmingham
Total funding: £250k
Duration: 2023-2025
Source: Mitoulis SA, Argyroudis SA, Panteli M, Fuggini C, Valkaniotis S, Hynes W, Linkov I (2023). Conflict resilience framework for critical infrastructure peacebuilding. Sustainable Cities and Society https://doi.org/10.1016/j.scs.2023.104405
The ultimate goal of this Fellowship will be to transform the decision-making in critical infrastructure peacebuilding in war torn countries, towards more resilient and more sustainable critical infrastructure development, using the power of ground-breaking AI methods and advanced data-mining techniques that the Fellow has recently developed. Big data and medium size data will be used to deliver rapid classification and decision-making for post-conflict reconstruction. The project includes a plan for delivering scientific research and publications for building back a better Ukraine that is in line with the global principles of resilience and sustainability for the build environment with no-regret and reliable AI-empowered decisions.
Inherent to infrastructure, uncertainty may lead to costly and technically inefficient designs and assessments in infrastructure management. The challenge becomes severe in low-medium income countries and more so in war-zone areas where minimal information is available whilst the conflict unravels. In Ukraine, the extensive damage to infrastructure has left numerous assets severely damaged and as a result, more than $349 bn will be needed to reconstruct the critical infrastructure. This is a unique opportunity to build back a better Ukraine, by prioritising peacebuilding and reconstruction towards more resilient and sustainable assets, systems and networks.
Objectives:
- Deliver an AI-empowered prioritisation framework for critical infrastructure reconstruction prioritisation and reconstruction
- Apply the framework and deliver strong case studies to benchmark the reconstruction of roads and bridges in war-torn regions
- Communicate the results of this project to governmental bodies, decision makers and external donors to positively influence efficient and objective decision making that will include participatory decision making and dynamically changing conditions in peacebuilding actions.
Related Publications:
- Izonin I, Muzyka R, Tkachenko R, Gregus M, Kustra N, Mitoulis SA (2024). An approach toward improvement of ensemble method’s accuracy for biomedical data classification. International Journal of Electrical and Computer Engineering http://doi.org/10.11591/ijece.v14i5.pp5949-5960 https://doi.org/10.1007/s00366-024-02048-1
- Izonin I, Muzyka R, Tkachenko R, Dronyuk I, Yemets K, Mitoulis S-A. (2024). A Method for Reducing Training Time of ML-Based Cascade Scheme for Large-Volume Data Analysis. Sensors 24(15):4762. https://doi.org/10.3390/s24154762