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：
Gordon Warn
金额：
$ 199.8万
依托单位：
Pennsylvania State Univ University Park
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

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2053620&HistoricalAwards=false
关键词：
LEAP HI Optimal Design Life

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.

与天气相关的事件的频率和强度不断增加，对国家建筑环境的运行、完整性和寿命产生负面影响，造成重大经济和其他损失，部分原因是建筑物和民用设施受损。基础设施已经并将继续根据历史数据和气候趋势进行设计，这些数据不再准确地反映未来预期的相关不确定性。此外，尽管在设计、建设和维护国家基础设施方面进行了大量投资。建筑物和基础设施，每项活动都受到管理最终，这些因素的融合导致美国人民和经济面临的风险不断增加，为了应对这些挑战，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，该框架将改变国家建筑和基础设施的设计、维护和改造方式，以促进明智和负责任的决策，以实现可持续的未来。该框架平衡了安全性和复原力与资源消耗和环境影响。，尽管考虑到当前和未来的不确定性。该项目将研究一种计算框架，用于基于概率生命周期指标，同时优化建筑环境的设计、维护和终身适应，同时考虑不确定性和非平稳生命周期该项目的目标是：根据最新的气候科学并考虑各种不确定性，量化不同规模的水文气象灾害；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、通过深度强化学习解决的部分可观察马尔可夫决策过程问题，从数学角度考虑维护和适应行动； - 通过调整平均风险和随机优势的概念来循环决策标准；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；框架和实践结果该项目将设计场景，例如受到洪水和与年龄相关的恶化威胁的河岸桥梁，受到气温升高和自然和运营压力因素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系统。通过建设研究能力和外展活动，提高工程领导力，以应对非平稳气候，通过研讨会和教育工作向决策者和最终用户传播新知识和方法。该奖项是 NSF 的法定使命，被认为值得通过评估提供支持基金会的智力价值和更广泛的影响审查标准。

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）

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：
2022-07
期刊：
Safety and Management (IABMAS
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

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：
2023-08
期刊：
Journal of Sustainable Water in the Built Environment
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.

Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning

通过贝叶斯网络和深度强化学习，对具有概率依赖性的恶化系统进行推理和动态决策