I-Corps: Sustainable Optimization of Circular Economy Practices During Post-Disaster Recovery Phase

I-Corps项目的更广泛的影响/商业潜力是技术的发展，它为利益相关者和决策者在灾难恢复阶段做出明智和工程决策提供知识支持。在美国，建筑拆除和解构的市场规模每年超过70亿美元。另一方面，自然灾害和紧急救援服务行业的市场规模约为140亿美元。这项研究为倡导新的政策和管理战略提供了技术，这些政策和管理战略可以通过确保更快和更可持续的复苏来增强受影响社区的能力，为弱势群体提供就业机会，这些群体极易受到贫困和无家可归的影响，特别是在灾后，并提供了在灾后恢复阶段利用循环经济实践的范式转变。这个I-Corps项目的基础是技术的发展，以帮助决策者做出明智的决定，以确定拆卸建筑部件所需的时间，预测过程中产生的废物量，并估计回收部件的美元回收价值。为了实现这一目标，研究活动将采用多种技术，包括使用激光雷达技术评估重建地点和建筑构件的任何损坏、裂缝、变形或缺陷，使用数据库和ASP。NET web应用程序来确定拆卸时间、产生的废物和回收材料的价值，并应用机器学习和预测建模来自动化拆卸过程。这项技术将通过利用来自土木工程、编程和灾害管理等多学科知识领域的方法和技术来开发。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。

The broader impact/commercial potential of this I-Corps project is the development of technology which provides knowledge to support stakeholders and decision makers take astute and engineered decisions during the disaster recovery phase. In the United States, the market size of building demolition and deconstruction exceeds $7 billion per year. On the other hand, the market size of the Natural Disasters and Emergency Relief Services industry is approximately $14 billion. This research provides technologies to advocate new policies and management strategies that can empower impacted communities by ensuring a faster and more sustainable recovery, providing employment opportunities to underrepresented minority groups who become extremely vulnerable to poverty and homelessness, particularly post-disasters, and offering a paradigm shift to utilize circular economy practices during a post-disaster recovery phase. This I-Corps project is based on the development of technology to help decision-makers make informed decisions to determine the time required to disassemble building components, predict the amount of waste generated in the process, and estimate the dollar salvage value of the recovered components. To achieve this goal, the research activities will adopt multiple technologies including using LiDAR technology to assess the reconstruction site and any damages, cracks, deformation, or defects in building components, use databases and ASP.NET web applications to determine disassembly time, waste produced, and value of recovered materials, and apply machine learning and predictive modeling to automate the deconstruction process. This technology will be developed by leveraging approaches and technologies from multidisciplinary fields of knowledge from civil engineering, programming, and disaster management.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.