LEAP-HI: Optimal Design and Life-Long Adaptation of Civil Infrastructure in a Changing and Uncertain Environment for a Sustainable Future

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The increasing frequency and intensity of weather-related events is negatively affecting the operation, integrity, and life-span of the Nation’s built environment, resulting in significant economic and other losses. This is due, in part, to the fact that buildings and civil infrastructure have been, and continue to be, designed based on historical data and climate trends that no longer accurately reflect the relevant uncertainties about what can be expected in the future. Furthermore, although substantial investments are made in designing, constructing, and maintaining the Nation’s buildings and infrastructure, each of these activities is managed independently, resulting in redundant costs and other negative impacts. Ultimately, the convergence of these factors is leading to increasing risks for the American people and economy. To address these challenges, this Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) project will research a new framework that transforms the way in which the Nation’s buildings and infrastructure are designed, maintained, and adapted to facilitate informed and responsible decision-making for a sustainable future. That framework balances safety and resilience with resource consumption and environmental impacts, while accounting for current and future uncertainties.The project will research a computational framework for simultaneously optimizing the design, maintenance, and life-long adaptation of the built environment, based on probabilistic life-cycle metrics, while accounting for uncertain and non-stationary life-cycle demands. The objectives of the project are to: quantify hydrometeorological hazards at various scales based on the most current climate science and considering a variety of uncertainties; propagate those uncertainties to the life-cycle decision criteria (e.g. costs, environmental impacts, hazard related losses) by mathematically considering the maintenance and adaptation actions through a partially observable Markov decision process problem solved with Deep Reinforcement Learning; devise a methodology and tools for efficiently and rigorously evaluating and comparing a set of design and adaptation alternatives with respect to multiple life-cycle decision criteria by adapting concepts of mean-risk and stochastic dominance; understand how various sources of uncertainty affect the ability to discriminate among design alternatives; determine under which scenarios green infrastructure components can lead to optimal design and adaptation solutions; and demonstrate the new framework and findings on practical design scenarios, such as a riverine bridge threatened by flooding and age-related deterioration, an urban mid-rise building threatened by increasing temperatures and natural and operational stressors, and a port and systems of infrastructure assets threatened by sea level rise. The project will improve engineering leadership in response to a non-stationary climate by building research capacity and outreach through workshops and educational efforts that disseminate the new knowledge and methods to decision-makers and end-users.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.