Experimental Investigation on Shear Behavior of Dune Sand Reinforced Concrete Deep Beams

An experimental study on the shear behavior of dune sand reinforced concrete (DSRC) deep beams was conducted to determine the feasibility of using dune sand (DS) in engineering. Nine DSRC deep beams were designed and thoroughly analyzed for failure modes, diagonal cracks, and load-deflection curves in this study. The results showed that the shear strength and ductility of DSRC deep beams increased when the DS replacement rate was 30%, but the opposite effect occurred when the DS replacement rate was 50%. To analyze the differences in the effects of the DS replacement rate, shear span-to-depth ratio, concrete strength, and stirrup ratio on the shear strength of DSRC and normal reinforced concrete (NRC) deep beams, a total of 227 shear experimental tests of NRC deep beams were conducted. Furthermore, four national codes were evaluated and compared based on experimental data. The evaluation results showed that the four codes underestimated the shear strength of DSRC and NRC deep beams. Among them, ACI 318-11 provided more reliable predictions for both NRC and DSRC deep beams. It is in this regard that a new empirical model for predicting the shear strength of DSRC deep beams is proposed, in which a reduction coefficient of the DS replacement rate is incorporated. The verification results indicates that the predicted results of the proposed model are in good agreement with the experimental results.

Automated summary

An experimental study was conducted to analyze the shear behavior of dune sand reinforced concrete (DSRC) deep beams. The results showed that the shear strength and ductility of DSRC deep beams increased with a 30% DS replacement rate but decreased with a 50% DS replacement rate. The effects of various factors on the shear strength of DSRC and normal reinforced concrete (NRC) deep beams were analyzed through shear experimental tests. Furthermore, four national codes were evaluated and compared, with ACI 318-11 providing the most reliable predictions.