An empirical approach for nonlinear modelling and deformation capacity assessment of RC columns with plain bars. (1st December 2018)

• Record Type: Journal Article
• Authors: Verderame, Gerardo M.; Ricci, Paolo
• Is Part Of: Engineering structures. Volume 176(2018)
• Page Start: 539
• Page End: 554
• Publication Date: 2018-12-01
• Subjects: Reinforced concrete columns -- Plain bars -- Empirical -- Nonlinear modelling -- Deformation capacity; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2018.09.022
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A database of tests on flexure-controlled RC columns with plain bars is collected. Characteristic points of the cyclic response envelopes are identified. Parameters identifying the response envelopes are evaluated. Empirical predictive equations are developed. A comparison is carried out with models from literature and codes. Abstract: This study proposes an empirical-based approach for the nonlinear modelling and deformation capacity assessment of flexure-controlled Reinforced Concrete (RC) columns with plain bars, providing an estimate of the expected post-elastic response backbone through formulations based on regression of experimental data. To this end, a database of tests on RC columns with plain bars is collected from literature. The specimens have different axial load, material properties, geometry, and longitudinal and transverse reinforcement ratio. Force-displacement data are collected and processed for each specimen. The backbone of the experimental base moment-chord rotation response is evaluated for each test, and characteristic points corresponding to yielding, maximum, "ultimate", and zero resistance conditions are identified. Potential predictors are investigated and empirical predictive equations are proposed for these points, based on a statistical analysis of data. Predictions of the proposed model are compared with literature and code provisions.