Objective - The objective of this study is to improve the simulation and prediction of the shear and axial load-deformation response of ordinary reinforced concrete columns in the context of PBSE. This study will be conducted at (1) the element-level, where a new shear and axial load failure model will be developed for individual columns; (2) the system-level, where the newly developed numerical model will be employed to predict the collapse risk of a set of archetype buildings with the goal of improving the accuracy of building collapse assessment.
The expected delivery date for this project will be Q4 of 2021.
What is the Technical Idea? Collapse assessment of reinforced concrete buildings has been conducted by academic researchers and engineering practitioners for a number of years. The focus of much of this work has been on special reinforced concrete columns, those with improved reinforcement detailing appropriate for regions of moderate to high seismicity. Nonductile reinforced concrete columns represent a less ductile class of column where behavior under large demands can lead to abrupt loss of capacity. The collapse mechanics of nonductile reinforced concrete columns/frames is usually governed by shear failure of the columns followed by loss of their axial load capacity. Experimental and analytical research has been performed in recent decades to develop numerical models for predicting shear failure (e.g. [Vecchio and Collins (1986); Lehman and Moehle (1998); Sezen (2002); Elwood (2004); Ghannoum and Moehle (2012); Baradaran Shoraka and Elwood (2013)]), as well as the axial load failure (e.g. [Elwood (2004), 2004; Baradaran Shoraka and Elwood (2013)]) of concrete columns. Among these available models, the challenging question that still remains is how well each model can predict the response of nonductile concrete columns in an extreme event, and which model is suitable for implementation in the PBSE framework. The current models need improvements to be used for the seismic assessment of nonductile concrete frames in three-dimensional analysis and to incorporate important response characteristics such as cyclic degradation.
Sattar (2013) showed the need for improvements in the shear failure models for nonductile concrete columns to capture the shear failure at small drift ratios (<0.01), which is typical in this type of column. In addition, it is believed that improved models to simulate the axial load failure of concrete columns and the interaction between the shear and axial failure also are needed. In the first phase of the project, this study will identify experimental data from available resources worldwide, and then will develop accurate yet simple tools for predicting the shear and axial load-deformation response of reinforced concrete columns under strong shaking. Moreover, during this phase, an inventory of shear/axial tests on concrete columns will be developed, particularly data from tests of nonductile reinforced concrete columns. This portion of the project will be conducted primarily by researchers at the University of Texas at San Antonio and the University of Massachusetts Amherst supported by Earthquake Engineering Initiative funds awarded through the NIST Disaster Resilience Initiative grant program in FY2017. The NIST internal team will provide oversight for the development of the new numerical tools which will be used by the NIST internal team to conduct the second phase of the project.
The findings of the first phase of this project, i.e. the element-level, will be carried to the second phase, to assess the collapse performance at the system-level. A large portion of the research conducted by the earthquake engineering community in recent years has focused on collapse assessment. However, the definition of collapse still remains challenging. This project will conduct a study on the collapse assessment of a system (a building) composed of nonductile columns, in order to (1) evaluate the performance of the shear/axial failure model developed in this study when employed in the response prediction of a building system of columns, (2) improve the accuracy of the collapse assessment of nonductile reinforced concrete structures in regions of high seismicity in the US, and (3) identify the research needs for predicting system-level collapse for evaluation at the building scale.
What is the Research Plan? Earthquake Engineering Initiative funds will be used to support researchers at the University of Texas at San Antonio and the University of Massachusetts Amherst through the Disaster Resilience Initiative grant program in FY2017to conduct a portion of the research objectives for this project. The internal NIST team will collaborate with the DRI awardees to conduct complementary work on this topic in conjunction with the work done by the Awardee. The NIST internal research group will collaborate with the awardee to develop the detailed framework for conducting the multi-organization team effort including the portion to be conducted by EEG. The potential tasks that NIST team will work on include but not limited to performing collapse assessment of archetype buildings that represent nonductile concrete buildings built in high seismic regions of US such as Los Angeles prior to 1980, i.e. before improvement of seismic detailing in the standards. In this task, a set of archetype buildings will be designed, through the IDIQ contractor, to represent the pre-1980 construction in Los Angeles. The shear/axial model developed by the DRI awardees as part of this project will be employed to develop the nonlinear models of the archetype buildings in OpenSees. The Incremental Dynamic Analysis (Vamvatsikos and Cornell, 2002) will be performed to assess the collapse risk of archetype buildings. The residual capacity of the buildings after a strong motion will also be quantified. The outcome of this task will help decision makers in prioritizing the seismic mitigation plan in regions with high seismicity. The NIST team is currently working closely with the DRI awardee to finalize the scope of the work that will be conducted by the NIST EEG team.