Scalable Assessment of Urban Earthquake Resilience: A Novel Model-informed Deep Learning Paradigm

弹性指的是一个系统在不严重破坏其功能的情况下有效地吸收压力的能力。如果社区通过减灾和灾前准备，实现维持重要社区功能的适应能力，并在灾害发生后迅速恢复，则社区具有抗震力。社区的复原力取决于建筑群(一组服务于住房等共同功能的建筑物)和配套基础设施系统的表现。为了量化城市功能和地震后的恢复，需要对建筑群的弹性进行可靠的评估。评估地震恢复力的一个基本挑战是如何可靠和有效地估计在给定地震严重程度下可伸缩到城市水平的建筑物的损坏/失效概率。为了应对这一挑战并弥合现有的知识差距，这一灾难恢复研究补助金(DRRG)项目将通过开发一种从根本上新颖且可扩展的人工智能授权模型来评估大规模城市建筑群的地震恢复能力。该项目的研究成果可用于大型城市建筑群的抗震能力评估。这一范例有助于减轻地震风险的决策，为灾后响应和恢复规划提供信息，并有助于改进未来的建筑设计。此外，将与一位高中物理老师合作，在课堂上实施后，开发一个关于“抗震力对我的社区意味着什么？”的迷你单元。为了利用记录的地面运动(GM)可靠地估计城市地区大量建筑物在地震情景下的地震需求，需要：(A)真实地估计不同时期、不同地点和不同土壤类别的建筑物的GM严重程度度量(本项目中的谱加速度)变化；(B)GM时程的适当和最佳选择；以及(C)用于数值模拟的建筑物的详细结构模型。然而，要对大量建筑物进行详细建模，并在大量GM概率地表示不同场景的情况下对这些模型进行非线性时程分析，成本高得令人难以接受。我们的项目将解决这一根本问题，并通过开发和实现一系列新颖的人工智能授权算法和方法来实现可伸缩性。该项目的总体目标是通过开发一个从根本上新颖和可扩展的模型信息深度学习框架，使大规模城市建筑群的地震复原力评估成为可能。这将通过以下方法实现：(1)开发贝叶斯深度学习方法来模拟不同振动周期、位置和土壤类别的谱加速度的变化，给定地震情景；(2)创建新的无监督的深部自动编码器-分类器算法，用于地面运动的聚类和选择；(3)建立创新的模型信息符号深度学习方法，用于详细的非线性结构模型的元建模；(4)确定典型建筑模型的元模型使能的易损性函数，以评估地震恢复能力，考虑多个失效准则和多个性能目标；以及(5)展示旧金山湾区代表性建筑的研究框架。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Resilience refers to a system’s ability to efficiently absorb stresses without significant disruption to its functioning. Communities are earthquake resilient if by mitigation and pre-disaster preparation, they achieve the adaptive capacity for maintaining important community functions and recover rapidly following disasters. Community resilience depends on the performance of building clusters (a set of buildings that serve a common function such as housing) and the supporting infrastructure systems. Reliable assessment of the resilience of building clusters is required for quantifying urban functionality and recovery after an earthquake. A fundamental challenge in evaluating earthquake resilience is how to reliably and efficiently estimate probability of damage/failure of buildings with scalability to the urban level for a given earthquake severity. To address this challenge and bridge the existing knowledge gap, this Disaster Resilience Research Grants (DRRG) project will enable assessment of earthquake resilience for large-scale urban building clusters via developing a fundamentally novel and scalable AI-empowered model. The outcome of this project can be used to evaluate earthquake resilience of building clusters in large-scale urban areas. This paradigm facilitates decision making for seismic risk mitigation, informs planning for post disaster response and recovery, and helps improve future building design. Furthermore, in collaboration with a high school physics teacher and following classroom implementation, a mini-unit will be developed on “What does earthquake resilience mean to my community?” which will be available to other science teachers across the country.In order to reliably estimate the seismic demand on a large number of buildings in an urban area under earthquake scenarios, using recorded ground motions (GMs), there is a need for: (a) realistically estimating a GM severity measure (spectral acceleration in this project) variation for buildings with different periods, at different locations and on different soil classes; (b) proper and optimal selection of GM time-histories; and (c) a detailed structural model of buildings for use in numerical simulations. However, it is prohibitively expensive to conduct detailed modeling of a large number of buildings and carrying out nonlinear time history analyses of such models under a large set of GMs probabilistically representing different scenarios. Our project will address this fundamental issue and allow for scalability through the development and implementation of a series of novel AI-empowered algorithms and methods. The overarching goal of this project is to enable the assessment of earthquake resilience for large-scale urban building clusters, through developing a fundamentally novel and scalable model-informed deep learning framework. This will be achieved by: (1) developing a Bayesian deep learning approach to model the variation of spectral acceleration over different periods of vibration, locations, and soil classes, given an earthquake scenario; (2) creating a new unsupervised deep autoencoder-classifier algorithm for ground motion clustering and selection; (3) establishing an innovative model-informed symbolic deep learning method for metamodeling of detailed nonlinear structural models; (4) determining metamodel-enabled fragility functions for representative building models to assess earthquake resilience, accounting for multiple failure criteria and multiple performance objectives; and (5) demonstrating the researched framework for representative buildings in the San Francisco Bay Area.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.