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

LEAP-HI:在不断变化和不确定的环境中土木基础设施的优化设计和终身适应,实现可持续的未来

基本信息

  • 批准号:
    2053620
  • 负责人:
  • 金额:
    $ 199.8万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-09-01 至 2025-08-31
  • 项目状态:
    未结题

项目摘要

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.
与天气有关的事件的频率和强度不断增加,对国家建筑环境的运行、完整性和寿命产生了负面影响,造成了重大的经济和其他损失。这在一定程度上是由于建筑物和民用基础设施一直并将继续根据历史数据和气候趋势进行设计,而这些数据和气候趋势已不能准确反映有关未来可预期情况的不确定性。此外,尽管在设计、建造和维护国家建筑物和基础设施方面进行了大量投资,但这些活动中的每一项都是独立管理的,导致了冗余成本和其他负面影响。最终,这些因素的共同作用将导致美国人民和经济面临越来越大的风险。为了应对这些挑战,这个美国繁荣、健康和基础设施领先工程(LEAP-HI)项目将研究一个新的框架,改变国家建筑和基础设施的设计、维护和适应方式,以促进对可持续未来的知情和负责任的决策。该框架平衡了安全性和复原力与资源消耗和环境影响之间的关系,同时考虑了当前和未来的不确定性。该项目将研究一个计算框架,基于概率生命周期指标,同时考虑不确定和非平稳的生命周期需求,同时优化建筑环境的设计、维护和终身适应。该项目的目标是:根据最新的气候科学并考虑到各种不确定性,量化不同尺度的水文气象灾害;通过深度强化学习解决部分可观察的马尔可夫决策过程问题,从数学上考虑维护和适应行动,将这些不确定性传播到生命周期决策标准(例如成本、环境影响、危害相关损失);设计一种方法和工具,通过适应平均风险和随机优势的概念,有效地、严格地评估和比较一组设计和适应备选方案,并考虑到多个生命周期决策标准;了解各种不确定性来源如何影响区分设计方案的能力;确定在哪些情况下,绿色基础设施组件可以带来最佳设计和适应解决方案;并在实际设计场景中展示新的框架和发现,例如受到洪水和与年龄相关的老化威胁的河桥,受到温度上升和自然和操作压力威胁的城市中层建筑,以及受到海平面上升威胁的港口和基础设施资产系统。该项目将通过建设研究能力和通过讲习班和教育工作向决策者和最终用户传播新知识和新方法,提高工程领导能力,以应对不稳定的气候。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning
  • DOI:
    10.1016/j.ress.2023.109144
  • 发表时间:
    2022-09
  • 期刊:
  • 影响因子:
    0
  • 作者:
    P. G. Morato;C. Andriotis;K. Papakonstantinou;P. Rigo
  • 通讯作者:
    P. G. Morato;C. Andriotis;K. Papakonstantinou;P. Rigo
Identifying Sweet Spots for Green Stormwater Infrastructure Implementation: A Case Study in Lancaster, Pennsylvania
确定绿色雨水基础设施实施的最佳点:宾夕法尼亚州兰开斯特的案例研究
  • DOI:
    10.1061/jswbay.sweng-513
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    1.9
  • 作者:
    Yavari Bajehbaj, Rouhangiz;Wu, Hong;Grady, Caitlin;Brent, Daniel;Clark, Shirley E.;Cibin, Raj;Duncan, Jonathan M.;Kumar Chaudhary, Anil;McPhillips, Lauren E.
  • 通讯作者:
    McPhillips, Lauren E.
Deep reinforcement learning-based life-cycle management of deteriorating transportation systems
基于深度强化学习的恶化交通系统的生命周期管理
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Saifullah, M.;Andriotis, C.P.;Papakonstantinou, K.G.;Stoffels, S.M.
  • 通讯作者:
    Stoffels, S.M.
Optimizing policies for deteriorating systems considering ordered action structuring and value of information
考虑有序行动结构和信息价值,优化恶化系统的策略
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Gordon Warn其他文献

Gordon Warn的其他文献

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{{ truncateString('Gordon Warn', 18)}}的其他基金

Discrete Structural Optimization through a Sequential Decision Process
通过顺序决策过程进行离散结构优化
  • 批准号:
    2322853
  • 财政年份:
    2023
  • 资助金额:
    $ 199.8万
  • 项目类别:
    Standard Grant
RSB/Collaborative Research: A Sequential Decision Framework to Support Trade Space Exploration of Multi-Hazard Resilient and Sustainable Building Designs
RSB/合作研究:支持多灾种弹性和可持续建筑设计贸易空间探索的序贯决策框架
  • 批准号:
    1455444
  • 财政年份:
    2015
  • 资助金额:
    $ 199.8万
  • 项目类别:
    Standard Grant
CAREER: A Performance-Based Multi-Objective Optimization Framework to Define Innovative Structural Concepts and Support the Seismic Design of Critical Buildings
职业生涯:基于性能的多目标优化框架,用于定义创新结构概念并支持关键建筑的抗震设计
  • 批准号:
    1351591
  • 财政年份:
    2014
  • 资助金额:
    $ 199.8万
  • 项目类别:
    Standard Grant
Stability of Elastomeric and Lead-Rubber Seismic Isolation Bearings Under Extreme Earthquake Loading
弹性和铅橡胶隔震支座在极端地震荷载下的稳定性
  • 批准号:
    1031362
  • 财政年份:
    2010
  • 资助金额:
    $ 199.8万
  • 项目类别:
    Standard Grant
NSF East Asia Summer Institutes for US Graduate Students
美国研究生 NSF 东亚暑期学院
  • 批准号:
    0305010
  • 财政年份:
    2003
  • 资助金额:
    $ 199.8万
  • 项目类别:
    Standard Grant

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  • 批准号:
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Hydrogen Integration for Accelerated Energy Transitions Hub (HI-ACT)
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  • 批准号:
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LEAP-HI: Compounding Risk Assessment and Mitigation Options for Building Infrastructure Experiencing Coastal Flooding-Related Saltwater Deterioration and Seismic Hazard
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