Multi-scale stochastic damage model for concrete and its application to RC shear wall structure. Issue 6 (14th September 2018)

• Record Type: Journal Article
• Authors: Feng, De-Cheng; Yang, Cheng-Dong; Ren, Xiao-Dan
• Is Part Of: Engineering computations. Volume 35:Issue 6(2018)
• Page Start: 2287
• Page End: 2307
• Publication Date: 2018-09-14
• Subjects: Multi-scale -- Probability density evolution method -- Random field -- Shear wall structure -- Stochastic damage model; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-09-2017-0371
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: This paper aims to present a multi-scale stochastic damage model (SDM) for concrete and apply it to the stochastic response analysis of reinforced concrete shear wall structures. Design/methodology/approach: The proposed SDM is constructed at two scales, i.e. the macro-scale and the micro-scale. The general framework of the SDM is established on the basis of the continuum damage mechanics (CDM) at the macro-scale, whereas the detailed damage evolution is determined through a parallel fiber buddle model at the micro-scale. The parallel buddle model is made up of micro-elements with stochastic fracture strains, and a one-dimensional random field is assumed for the fracture strain distribution. To represent the random field, a random functional method is adopted to quantify the stochastic damage evolution process with only two variables; thus, the numerical efficiency is greatly enhanced. Meanwhile, the probability density evolution method (PDEM) is introduced for the structural stochastic response analysis. Findings: By combing the SDM and PDEM, the probabilistic analysis of a shear wall structure is performed. The mean value, standard deviation and the probability density function of the shear wall responses, e.g., shear capacity, accumulated energy consumption and damage evolution, are obtained. Originality/value: It is noted that the proposed method can reflect the influences of randomness from material level to structural level, and is efficient for stochastic response determination of shear wall structures.