SBIR Phase I: Rapid Calculation of Earthquake Damage and Repair Costs for High-Performance Building Design

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是，结构工程师将能够设计出在地震中受损最小的高性能建筑。 使用该项目中创建的软件工具将从根本上改变结构工程师开发建筑物初始设计的方式。通过使工程师能够提高他们对建筑性能的理解，他们将能够设计具有更高抗震性能的建筑物，从而提高建筑物在未来地震中的弹性。该软件有一个巨大的潜在新市场，因为它最终可以被美国西部大约10，000名设计建筑物的结构工程师中的每一个使用，以及在全球地震危险地区工作的数万名工程师。这份小型企业创新研究报告（SBIR）第一阶段项目将开发技术，以快速估计地震期间的结构行为，并提供快速估计的库存组件在建筑物中。这种快速的自动填充将使有效的计算，预测损坏，维修成本和维修时间的建筑物时，它经历了地震。 该公司的快速估算结构响应的方法将利用工程力学原理，适用于估算属性未完全指定的结构的响应。快速估算建筑物内容的方法将从用户对公司最初预测的更新中学习。拟议的统计模型将使公司能够研究这个问题，并部署一个模型，允许以可扩展的方式进行实时和用户特定的改进。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is that structural engineers will be enabled to design high-performance buildings that have minimal damage in earthquakes. Using the software tool created in this project would fundamentally change the way in which a structural engineer develops the initial design for a building. By enabling engineers to enhance their understanding of building performance, they will be able to design buildings for higher seismic performance and will thus improve the resilience of buildings in future earthquakes. There is a large potential new market for this software, as it can eventually be used by every one of the approximately 10,000 structural engineers designing buildings in the Western United States, and the tens of thousands of engineers working worldwide in regions with earthquake hazards.This Small Business Innovation Research (SBIR) Phase I project will develop technology to rapidly estimate the structural behavior during an earthquake and also provide a rapid estimation of the inventory of components in a building. This rapid autopopulation will then enable efficient calculations that predict the damage, the repair costs, and the repair time for a building when it experiences an earthquake. The company's method for rapid estimation of structural responses will utilize principles of engineering mechanics, applied to estimating response of a structure whose properties are not fully specified. The method for rapid estimation of building contents will learn from users' updates to the company's initial predictions. The proposed statistical model will enable the company to study this question, and deploy a model that allows real-time and user-specific refinements in a scalable manner.