Structural behavior of prestressed self‐compacting concrete‐encased concrete‐filled steel tubes beams

Abstract

Concrete‐encased concrete‐filled steel tubes (CFST) has extensive applications in the world. According to previous researches, if the CFST is placed in the compression zone, the confinement increases, and the compressive capacity of the concrete is completely used. The prestressed strands also increase the core concrete confinement and eliminate tension cracks. Therefore, in this paper, to achieve the benefits of CFST and prestressed strand, for the first time, a novel concept called prestressed concrete‐encased CFST (PCE‐CFST) beams were introduced. The main objective of the combination of steel tube and pre‐stressed strands is to increase the core concrete compressive strength and control the concrete crack in the tension zone. Six beams were constructed using self‐compacting concrete to investigate the influence of variations in the pre‐stressing force level of the pre‐stressed strands, the pre‐stressed strands eccentricity, and the steel tubes diameter‐to‐thickness ratio on the structural performance of these members. The specimens were tested under four‐point loading. The results showed that the pre‐stressed strands increased the confinement effect on the core concrete and improved the bearing capacity, ductility, and bending stiffness. The steel tubes improved the bearing capacity, ductility, and energy absorption, while they did not significantly affect the bending stiffness. Finally, it was shown that the pre‐stressed strands increased the bearing capacity, energy absorption, and bending stiffness in reinforced concrete beams.

A novel strength behavior prediction model of self‐compacting concrete with foundry sand and hybrid fibers

“…However, the dense microstructure of SCC causes damage when it is exposed to fire. The current fire test outcomes show that the SCC has a better withstanding capacity to fire than the normal concrete 9–12 …”

“…The current fire test outcomes show that the SCC has a better withstanding capacity to fire than the normal concrete. [9][10][11][12] Several deep learning [13][14][15] and machine learning methods [16][17][18][19]31 are conducted to improve the durability, drying shrinkage, slump model, and so on. In our proposed approach, we utilize the SCC using foundry sand hybrid fibers to enhance durability and sustainability.…”

Vision‐based probabilistic post‐earthquake loss estimation for reinforced concrete shear walls

“…The development and propagation of surface cracks, along with concrete spalling and crushing which necessitates the use of various MoRs, is the most obvious visual sign of damage in RCSWs. [12][13][14][15][16][17][18][19][20][21] Therefore, crack detection in RCSWs is a crucial phase in figuring out the level of damage in RC buildings after an earthquake.…”

Post‐earthquake stiffness loss estimation for reinforced concrete columns using fractal analysis of crack patterns

“…The surface crack maps are crucial gauge of the damage for the RC components 20–27 . The automated image‐based damage quantification for RC components is categorized into two main steps.…”

“…18,19 The surface crack maps are crucial gauge of the damage for the RC components. [20][21][22][23][24][25][26][27] The automated image-based damage quantification for RC components is categorized into two main steps. In the first step, the surface crack patterns and crushing zones are identified and documented.…”