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

该中期职业发展（MCA）奖将使首席研究员（PI）能够接受机器学习（ML）方法的培训，以弥合基于性能的地震工程和ML之间的差距，以估计结构的倒塌极限状态。结构倒塌是建筑物最具灾难性的失效模式，也是最难用传统方法评估的。本研究将ML演算法应用于强震下建筑物结构倒塌之预测。这些算法将在没有明确编程的情况下进行学习训练，并可以改变结构系统的设计和评估方式。基于性能的方法用于重要结构的评估或设计，但由于模型的复杂性以及漂移和其他响应参数对小输入参数变化的敏感性，估计结构失效有很大的局限性。此外，大多数建筑物是使用简化的弹性方法设计的，自然灾害事件下的预期结构损伤仅从一般考虑近似。在这项研究中，ML算法将使用数值模拟和实验测试结果进行训练，以有效地预测结构设计方案的崩溃。与斯坦福大学和马萨诸塞州理工学院的研究伙伴的协同合作将建立PI在这一领域的研究能力。这项研究将由一个基于高中推广的教育计划，研究生和本科生研究生的支持和培训示范作为补充。该奖项将有助于国家科学基金会（NSF）在国家地震减灾计划中的作用。 项目数据将存档在NSF支持的自然灾害工程研究基础设施（NHERI）数据库（https：//www.example.com）中。www.designsafe-ci.org目前的崩溃方法是基于复杂的非线性有限元模型，这可能是一个繁重的任务，在设计过程中，甚至现有系统的性能评估。该项目的研究目标包括：（i）应用于失效极限状态的最佳ML技术的实施，（ii）从现有数据库中挖掘建筑结构部件的动态响应，以及（iii）开发提高结构系统性能的方法。几个有前途的战略研究结构倒塌的应用将被考虑，如人工神经网络，支持向量机和响应面模型的变化。将回答以下基本问题：（i）需要什么水平的建筑物输入数据才能有效地预测倒塌？以及（ii）ML算法是否可以被训练来评估受损系统的储备能力和冗余度，其中材料退化是高度不确定的，或者关键结构部件（例如，#21453;列）被删除。ML算法将被用于寻找与结构倒塌相关的隐藏关联，并将被训练以考虑多组输入数据，从基本建筑信息到非线性恶化输入参数。该奖项反映了NSF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。

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.