Surrogate modeling of structural seismic response using probabilistic learning on manifolds
Surrogate modeling of structural seismic response using probabilistic learning on manifolds
复制标题
DOI：
10.1002/eqe.3839
复制
发表时间：
2023-02
影响因子：
4.5
通讯作者：
Kuanshi Zhong;Javier G. Navarro;S. Govindjee;G. Deierlein
中科院分区：
文献类型：
--
作者：
Kuanshi Zhong;Javier G. Navarro;S. Govindjee;G. Deierlein
非线性反应时程分析（NLRHA）被认为是评估建筑物在强地震动作用下抗震性能的可靠和稳健的方法。虽然NLRHA在评估特定建筑场地的一组选定地面运动的单个结构时相当简单，但执行大量分析以评估（1）具有特定场地地面运动组的替代设计实现的多个模型，或（2）具有多组地面运动的多个场地的单个原型建筑模型变得不太实际。在这方面，代理模型提供了一种替代运行重复的NLRHA可变设计实现或地面运动。本文提出了一种新的替代建模技术，称为流形上的概率学习（PLoM），估计结构地震反应。从本质上讲，PLoM方法提供了一个有效的随机模型来开发随机变量之间的映射，然后可以用于有效地估计系统的结构响应与设计/建模参数或地面运动特性的变化。介绍了PLoM算法，并将其用于两个12层建筑的案例研究，以估计结构响应的概率分布。第一个例子集中在多自由度分析模型的可变设计参数与其峰值层间位移和加速度响应之间的映射。第二个例子应用PLoM技术来估计场地特定地震动特性变化的结构响应。在这两个示例中，针对正交输入参数网格生成训练数据集，并且针对具有规定统计分布的输入参数开发测试数据集。进行验证研究，以检查PLoM模型的准确性和效率。总体而言，这两个例子显示PLoM模型估计和验证数据集之间的良好协议。此外，与其他常见的替代建模技术相比，PLoM模型能够保持峰值响应之间的相关结构。进行参数研究，以了解不同的PLoM调谐参数对其预测精度的影响。
Nonlinear response history analysis (NLRHA) is generally considered to be a reliable and robust method to assess the seismic performance of buildings under strong ground motions. While NLRHA is fairly straightforward to evaluate individual structures for a select set of ground motions at a specific building site, it becomes less practical for performing large numbers of analyses to evaluate either (1) multiple models of alternative design realizations with a site‐specific set of ground motions, or (2) individual archetype building models at multiple sites with multiple sets of ground motions. In this regard, surrogate models offer an alternative to running repeated NLRHAs for variable design realizations or ground motions. In this paper, a recently developed surrogate modeling technique, called probabilistic learning on manifolds (PLoM), is presented to estimate structural seismic response. Essentially, the PLoM method provides an efficient stochastic model to develop mappings between random variables, which can then be used to efficiently estimate the structural responses for systems with variations in design/modeling parameters or ground motion characteristics. The PLoM algorithm is introduced and then used in two case studies of 12‐story buildings for estimating probability distributions of structural responses. The first example focuses on the mapping between variable design parameters of a multidegree‐of‐freedom analysis model and its peak story drift and acceleration responses. The second example applies the PLoM technique to estimate structural responses for variations in site‐specific ground motion characteristics. In both examples, training data sets are generated for orthogonal input parameter grids, and test data sets are developed for input parameters with prescribed statistical distributions. Validation studies are performed to examine the accuracy and efficiency of the PLoM models. Overall, both examples show good agreement between the PLoM model estimates and verification data sets. Moreover, in contrast to other common surrogate modeling techniques, the PLoM model is able to preserve correlation structure between peak responses. Parametric studies are conducted to understand the influence of different PLoM tuning parameters on its prediction accuracy.