Pushover Analysis for Plan Irregular Building Structures

Nonlinear static procedures (NSPs), also known as “pushover methods”, represent the most used tool in the professional practice for assessment of seismic performance of building structures. Most of the methods subscribed by major seismic codes for seismic analysis of new or existing buildings have been originally defined for simple regular structures. Nevertheless, perfect regularity is an idealization that very rarely occurs and, in principle, the concept of irregularity itself is a fuzzy one. Most codes attempt to give a definition to the concept of “regularity”, considering issues related to the distribution of mass, stiffness and strength in the building, both in plan and in elevation. Real buildings rarely comply with these regularity requirements, resulting in a barely reliable application of the basic NSPs. Code specifications concerning irregular structures are in need of improvement and they do not provide for clear and specific guidelines for the seismic analysis of such structures. Therefore the problem of the seismic evaluation of irregular structures is still an open one and basic issues need to be further explored. The present paper aims at providing a wide outlook on the problem of the seismic assessment of plan irregular building structures. Firstly, a brief review of the elastic and inelastic methods for the assessment of the torsional effects induced by in-plan irregularity is presented, mainly aimed at the definition of the variables governing the problem. Then, the basic features of the most important NSPs are discussed, followed by the description of the recent improvements developed for irregular structures. Since there is not yet a fully satisfactory solution, pros and cons of the various approaches are outlined, highlighting the most promising methods and the issues that are yet to be investigated. Finally, recommendations for code improvement are suggested. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.