Lateral Load Behavior and Modeling of Shear-Dominant RC Walls for Performance-Based Design

研究的目标是开发和验证适用于使用固体钢筋混凝土墙的建筑物的重力和地震联合荷载的新建模方法。特别是，该项目将研究通常用于低层、中型和高层建筑的钢筋混凝土墙的结构性能，以开发新的计算机/数值模型和建筑规范设计指南。为了实现这些目标，将在加州大学洛杉矶分校的结构/地震工程研究实验室在模拟地震荷载下测试大约十个典型墙壁配置的大型模型。墙壁将配备高分辨率传感器，以测量载荷、应力、应变及其变形。测试观察和收集的传感器数据将用于了解损伤如何累积并最终导致倒塌。该项目将提供方法来预测中到强烈地震动所造成的破坏。将提出对具有这些墙壁的新建筑和现有建筑进行基于性能的设计的建筑规范的更改。研究结果将有助工程师设计更安全及更经济的新楼宇，并评估现有楼宇的性能，以确定那些在预期伤亡和经济损失方面对社会构成重大危害的楼宇。新的数据以及以前测试的现有数据将被汇编成一个综合数据库，并存入社区数据储存库，以便将来进行研究和开发。经过验证的计算机模型将在开源环境中开发，以促进进一步的研究和设计研究。研究结果将通过一个互动网站和鼓励积极参与的其他基于网络的协作工具进行传播。该研究将为研究生和本科生提供高级培训，并纳入高中实习生的积极参与，以及工业界的积极参与。将利用建立良好的机构伙伴关系，在项目中招募代表性不足的群体

The goals of the research are to develop and validate new modeling approaches appropriate for combined gravity and earthquake loads for buildings that utilize solid reinforced concrete walls. In particular, the project will study the structural performance of reinforced concrete walls, which are commonly used in the low-, medium-, and high-rise building construction, to develop new computer/numerical models and building code design guidelines. To accomplish these goals, approximately ten large-scale models of typical wall configurations will be tested under simulated earthquake loading in the Structural/Earthquake Engineering Research Laboratory at UCLA. The walls will be instrumented with high-resolution sensors to measure loads, stresses, strains, and their deformations. Test observations and the collected sensor data will be used to understand how damage accumulates and ultimately leads to collapse. Project will provide methodology to predict damage under moderate to intense ground shaking. The changes to building codes for the performance-based design for both new and existing buildings with these walls will be proposed. The research results will enable engineers to design safer and more economical new buildings and to evaluate the performance of existing buildings to identify those posing significant hazard to the community in terms of expected casualties and economic losses. The new data, along with existing data from prior tests, will be compiled into a comprehensive database and archived into a community data repository to enable future research and development. The validated computer models will be developed in an open-source environment to facilitate further research and design studies. The results will be disseminated via an interactive web site and other web-based collaboration tools that encourage active participation. The research will provide advanced training to graduate and undergraduate students, incorporate active participation by high school interns, and include active participation from industry. Underrepresented groups will be recruited in the project using well established institutional partnerships