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Three-Dimensional Dynamic Nonlocal Beam Formulation for Simulation of Damage and Failure in Reinforced Concrete Structural Elements

Three-Dimensional Dynamic Nonlocal Beam Formulation for Simulation of Damage and Failure in Reinforced Concrete Structural Elements
用于模拟钢筋混凝土结构构件损伤和失效的三维动态非局部梁公式
批准号:
2032352
负责人:
Petros Sideris
金额:
$25.94万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-10-01 至 2024-09-30

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中文摘要
翻译
钢筋混凝土(RC)结构占国家基础设施的最大部分。钢筋混凝土结构通常会受到地震、飓风和海啸等自然灾害的复杂动态荷载作用,从而导致三维同时荷载。在这种复杂的荷载条件下,准确预测钢筋混凝土结构的损伤和失效对于开发新设计和量化现有结构的脆弱性至关重要,并且已被确定为“NHERI五年科学计划:多灾害研究,使世界更具弹性”的关键需求。通过提供这样的能力,这项研究将支持基于性能的设计和风险评估方法,在决策的各个步骤中由利益相关者,包括联邦和州机构和地方社区考虑。这项研究将提高结构和社区的复原力,准确评估新的和现有的结构对自然灾害的表现,确定关键的脆弱性,优先考虑和指导改造/升级工作,并开发和完善新的设计,以保护人民和财产免受自然灾害的影响。这项研究将培养研究生和本科生主要来自代表性不足的少数民族,将包括外展到少数民族占多数的学校,并将允许一个新的研究生课程,将与广大的工程社区共享的发展。这项研究的目标是推进计算模拟和基本的理解损坏和失败的钢筋混凝土构件。在这样做的过程中,本研究将建立一个计算单元制定能够整体模拟(和预测)的弯曲,剪切和扭转破坏的钢筋混凝土构件进行三轴动态加载条件下产生组合轴向,弯曲,剪切和扭转构件荷载。该公式标志着从传统的梁-柱建模方法的范式转变:(i)通过高阶横截面运动学精确地描述构件内的三轴应力/应变状态,所述高阶横截面运动学经由横截面子域建模确定,所述横截面子域建模在横截面上强制局部平衡;(ii)通过引入材料和构件,自然再现强度和延性尺寸效应,水平特征长度,以描述损伤的启动和传播,从材料水平的成员规模,和(iii)一贯模拟加载速率的影响,通过率相关的材料法律与成员动态。由于杆件动力学与率相关本构关系消除了拟静力学的解的多重性并减少了问题的非线性,因此该公式获得了改进的计算收敛性,这使得它对高度非线性问题特别有效,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。
英文摘要
Reinforced concrete (RC) structures account for the largest portion of the nation’s infrastructure. RC structures are typically subjected to complex dynamic loading conditions from natural hazards, such as earthquakes, hurricanes and tsunamis, which result in simultaneous loads in three dimensions. Accurate prediction of damage and failure of RC structures under such complex loading conditions is essential to developing new designs and quantifying the vulnerability of existing structures, and has been identified as a key need in the “NHERI Five-Year Science Plan: Multi-Hazard Research to Make a More Resilient World”. By providing such a capability, this research will support performance-based design and risk assessment methodologies, considered in various steps of decision-making by stakeholders, including Federal and State agencies and local communities. This research will improve structure and community resilience in accurately assessing the performance of new and existing structures against natural hazards, identifying key vulnerabilities, prioritizing and guiding retrofitting/upgrading efforts, and developing and refining new designs in order to protect people and property from natural disasters. This research will train graduate and undergraduate students primarily from underrepresented minorities, will include outreach to minority-majority schools, and will allow the development of a new graduate course that will be shared with the broad engineering community.The goal of this research is to advance computational simulation and fundamental understanding of damage and failure of RC members. In doing so, this research will establish a computational element formulation capable of holistically simulating (and predicting) flexural, shear and torsional failures of RC members subjected to triaxial dynamic loading conditions generating combined axial, flexural, shear and torsional member loads. This formulation marks a paradigm shift from conventional beam-column modeling approaches by: (i) accurately describing the triaxial stress/strain state within members through higher order cross-section kinematics determined via cross-section sub-domain modeling that enforces the local equilibrium over the cross-section; (ii) naturally reproducing strength and ductility size effects by introducing both material- and member-level characteristic lengths to describe damage initiation and propagation from the material level to the member scale, and (iii) consistently simulating loading rate effects through rate-dependent material laws together with member dynamics. Because member dynamics together with rate-dependent constitutive laws eliminate the solution multiplicity of quasi-statics and reduce the problem nonlinearity, this formulation attains improved computational convergence properties, which makes it particularly efficient for highly nonlinear problems, such as those of structural damage and failure.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.
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  • 批准号:
    1538585
  • 项目类别:
    Standard Grant
  • 资助金额:
    $39.45万
  • 财政年份:
    2015
  • 负责人:
    Petros Sideris
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis