The NEES-Soft Project

Seismic Risk Reduction for Soft-Story Woodframe Buildings

Synopsis

Project Team

Advisory Committee

Schedule

Press

Test Updates

Misc.

Project Vision: The vision for the NEESsoft project is twofold: To provide a methodology to retrofit soft story woodframe buildings to (1) protect life safety and property by avoiding soft story collapse and excessive upper story accelerations, and (2) provide a mechanism by which soft story woodframe buildings can be retrofitted using performance-based seismic design (PBSD) to achieve a level of performance commensurate with their stakeholders target. This vision will be accomplished through a comprehensive combination of new numerical modeling procedures, hybrid testing for validation of two levels of soft story woodframe retrofit (i.e. ATC 71.1 and seismic protection systems), and system level validation to better understand the mechanisms of woodframe collapse and the effect of these two levels of retrofit on system performance. Understanding the collapse mechanisms and how to mitigate the risk imposed by them requires a transformative leap in modeling and analysis of structures, resulting in the ability to accurately predict performance over a wide range of seismic loading conditions.

Expected Project Outcomes: The following outcomes are expected as a direct result of the research, education, technology transfer, and outreach conducted within the NEESsoft project: (1) a fundamental understanding of the collapse mechanisms and process for soft story woodframe buildings; (2) experimental verification of the ATC 71.1 retrofit procedure and potential improvements to the stiffness and strength balance; (3) recommendations for the next update of ASCE Standard 41 for performance criteria for wood; (4) a performance-based retrofit methodology for woodframe buildings which ties into outcome 3; and (5) a method for and validation of the use of seismic protection devices for retrofit of soft story woodframe buildings to achieve improved performance.

NEES-Soft is funded by the National Science Foundation under Grant No. CMMI-1041631 (NEES Research). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the project team and do not necessarily reflect the views of the National Science Foundation.