MCA: Using Machine Learning to Predict Seismic Failure Limit States in Buildings

MCA：使用机器学习来预测建筑物的地震破坏极限状态

• 批准号: 2121169
• 负责人: Luis Ibarra
• 金额: $ 39.57万
• 依托单位: University of Utah
• 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 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2121169&HistoricalAwards=false
• 关键词: MCA; Using; Machine; Learning; Predict

This Mid-Career Advancement (MCA) award will enable the Principal Investigator (PI) to train in machine learning (ML) methodologies in order to bridge the gap between performance-based earthquake engineering and ML to estimate the collapse limit state of structures. Structural collapse is a building’s most catastrophic failure mode and the most difficult to evaluate using traditional methodologies. This study will apply ML algorithms to predict structural collapse of buildings under strong seismic events. These algorithms will be trained to learn without being explicitly programmed and can transform the way in which structural systems are designed and evaluated. Performance-based methodologies are used for evaluation or design of important structures, but there are significant limitations to estimate structural failure due to model complexity and the sensitivity of drift and other response parameters to small input parameter variations. Also, most buildings are designed using simplified elastic methods, and the expected structural damage under natural hazard events is only approximated from general considerations. In this study, ML algorithms will be trained using numerical simulations and experimental test results to efficiently predict collapse of structural design alternatives. The synergistic collaboration with research partners at Stanford University and the Massachusetts Institute of Technology will build the PI's research capabilities in this area. The study will be complemented by an educational program based on high school outreach, support of graduate and undergraduate research students, and training demonstrations. This award will contribute to the National Science Foundation (NSF) role in the National Earthquake Hazards Reduction Program. Project data will be archived in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot (https://www.designsafe-ci.org). Current collapse methodologies are based on sophisticated nonlinear finite element models, which can be an onerous task in the design process and even for performance evaluation of existing systems. The project research objectives include: (i) implementation of optimal ML techniques for application to failure limit states, (ii) data mining of dynamic response of building structural components from available databases, and (iii) development of approaches to improve the performance of structural systems. Several promising strategies for the researched structural collapse application will be considered, such as variations of artificial neural networks, support vector machines, and response surface models. The following fundamental questions will be answered: (i) what level of building input data is required to efficiently predict collapse? and (ii) can ML algorithms be trained to assess the reserve capacity and redundancy of damaged systems, in which material deterioration is highly uncertain or key structural components (e.g., columns) are removed? The ML algorithms will be used to find hidden correlations associated to structural collapse and will be trained to consider several sets of input data, ranging from basic building information to nonlinear deteriorating input parameters.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.

这项职业中期进步（MCA）奖将使主要研究人员（PI）能够训练机器学习（ML）方法，以弥合基于绩效的地震工程和ML之间的差距，以估计结构的崩溃限制状态。结构崩溃是建筑物最灾难性的故障模式，也是使用传统方法最难评估的。这项研究将应用ML算法来预测强大的地震事件下建筑物的结构崩溃。这些算法将经过培训，可以学习，而无需明确编程，并且可以改变设计和评估结构系统的方式。基于绩效的方法用于评估或设计重要结构，但是由于模型复杂性以及漂移和其他响应参数对小输入参数变化而引起的结构性故障存在重大局限性。同样，大多数建筑物都是使用简化的弹性方法设计的，并且在自然危害事件下的预期结构损害仅在一般考虑中近似。在这项研究中，将使用数值模拟和实验测试结果对ML算法进行训练，以有效预测结构设计替代方案的崩溃。与斯坦福大学和马萨诸塞州理工学院的研究合作伙伴的协同合作将在该领域建立PI的研究能力。这项研究将由一项基于高中宣传，研究生和本科研究专业的教育计划以及培训示范的教育计划完成。该奖项将促进国家科学基金会（NSF）在国家地震危害计划中的角色。项目数据将在NSF支持的自然危害工程研究基础设施（NHERI）数据仓库（https://www.designsignsafe-ci.org）中存档。当前的崩溃方法基于复杂的非线性有限元模型，这可能是设计过程中的一项繁重任务，甚至是对现有系统的性能评估。项目研究目标包括：（i）实施用于应用到故障限制状态的最佳ML技术，（ii）可用数据库中建筑结构组件的动态响应的数据挖掘，以及（iii）开发方法以改善结构系统的性能。将考虑对研究结构崩溃应用的几种承诺策略，例如人工神经元网络的变化，支持向量机和响应表面模型。将回答以下基本问题：（i）有效预测崩溃需要什么水平的构建输入数据？ （ii）是否可以训练ML算法来评估损坏系统的储备容量和冗余，其中材料定义高度不确定或关键的结构组件（例如，列）被删除？ ML算法将用于查找与结构崩溃相关的隐藏相关性，并将接受培训以考虑几组输入数据，从基本构建信息到非线性恶化的输入参数，此奖项反映了NSF的法定任务，并认为通过基金会的知识优点和广泛的criperia criperia criperia criperia criperia criperia rection the Priews通过评估来获得支持。