journal article·2023-12Open access

Cyclic compressive behavior of hook-end steel and macro-polypropylene hybrid fiber reinforced recycled aggregate concrete

Junjie Feng, Xiaomin Jia, Xiaomei Dong, Pengbo Wang, Bing Xu, Zheng Wang

Abstract

One widely accepted strategy is to add fiber into recycled aggregate concrete (RAC) to improve compressive strength. Few existing investigations have focused on the cyclic compressive behavior of hybrid fiber reinforced recycled aggregate concrete (HFRRAC). A total of eighteen groups of prismatic specimens with different fiber combinations and recycled coarse aggregate (RCA) replacement rates were conducted under cyclic compression to investigate the mechanical properties of hooked-end steel and macro-polypropylene hybrid fiber reinforced recycled aggregate concrete. Results indicate that specimens under cyclic loading fail similarly to those subjected to monotonic loading. The envelope curve of the cyclic stress-strain curve for HFRRAC closely resembles the corresponding monotonic stress-strain curve. RCA replacement rates and fiber combinations do not significantly affect plastic strain and stress deterioration. Fibers added to specimens mitigate stiffness degradation and improve hysteretic energy dissipation. The proposed constitutive equations can be used to describe the cyclic compressive behavior of HFRRAC. The fibers improve the density and structure of mortar and aggregate-paste interfaces from the microstructure morphologies. The fracture mechanisms of HFRRAC were also studied using mesoscale numerical models under uniaxial compression loading. The cohesive zone model is suitable for simulating the compressive behavior of HFRRAC. Numerical specimens fail in mixed fracture modes, primarily mode II fractures.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Recycled Aggregate Concrete Performance · Innovative concrete reinforcement materials · Structural Behavior of Reinforced Concrete

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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