CAREER: Predictive Analysis of Stability-Critical Structures: an Uncertainty-Informed Path from Measurements to Theory

这个教师早期职业发展（CAREER）计划奖的基本主题是开发一个统一的预测分析框架，这将显着提高稳定性关键结构的基于分析的设计状态。该框架通过将数据科学中的数据挖掘、信息论和统计推断技术以及基于嵌入式非线性预测器的高保真随机非线性求解器整合到计算科学中的增量迭代路径跟踪技术中，跨越了学科界限。薄壁结构部件如冷成型钢构件和薄壁圆柱形结构对材料和制造缺陷极其敏感，因为它们在受到压缩应力时以屈曲模式失效。屈曲/稳定性失效模式使得难以预测倒塌载荷。为组件设计预测分析框架的挑战在于，与完美的轻微偏差会极大地影响其对负载的响应。结果是结果的很大分散，特别是当对倒塌载荷的响应感兴趣时。可观测和不可观测不确定性的现实输入模型，适用于全球和地方尺度以及随机和确定性方法，将是拟议框架的一个独特之处。目标是开发包括所有变量的分析非线性计算模型，将研究纳入课程，并向社区大学和高中学生提供推广服务。计算模型将与实验数据进行验证，已经是available.在这个项目中开发的方法将推进最先进的稳定性关键结构的计算建模，并将创造一个飞跃，缓和大的安全系数涉及这些结构的设计。将为博士课程开发一门新的结构稳定性概率方法课程。

The underlying theme of this Faculty Early Career Development (CAREER) Program award is the development of a unified predictive analysis framework that will significantly improves the state of analysis-based design for stability-critical structures. The framework crosses disciplinary boundaries by bringing together data mining, information theory and statistical inference techniques from data sciences and high fidelity stochastic nonlinear solvers that are based on embedded nonlinear predictors into incremental-iterative path following techniques from computational sciences. Thin-wall structural components such as cold-formed steel members and thin wall cylindrical structures are extremely sensitive to material and fabrication imperfections because they fail in buckling mode when subjected to compressive stresses. The buckling/stability failure mode makes it difficult to predict collapse loads. The challenge in devising predictive analysis framework for components is that a slight deviation from perfection dramatically affects their response to loads. The result is a large scatter in results especially when the response up to collapse load is of interest. Realistic input models for observable and unobservable uncertainties that are adaptable to both global and local scales as well as to stochastic and deterministic methods will be a unique feature of the proposed framework. The goal is to develop analytical non-linear computational model that includes all variables, to integrate research in to curriculum and to provide outreach to community college and high school students. The computational model will be validated with experimental data that is already available.The methodologies developed in this project will advance the state of the art in computational modeling of stability-critical structures and will create a leap towards moderating large safety factors involved in designing these structures. A new course in probabilistic methods in structural stability will be developed for the PhD curriculum.