Shear performance of recycled aggregate concrete beams: An insight for design aspects

Pradhan, Subhasis; Kumar, Shailendra; Barai, Sudhirkumar V.

doi:10.1016/j.conbuildmat.2018.05.022

Cited by 55 publications

(36 citation statements)

References 36 publications

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“…A comprehensive experimental database of RAC beams loaded in shear and their reference beams made with NAC was constructed in this study, as detailed in Appendix A Table A1 . This database comprised 264 datasets (206 beams without steel stirrups and 58 beams containing steel stirrups) collected from 28 published works across the world [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 ]. Experimental outcomes published in Chinese [ 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 ] were also included as a complement.…”

Section: Shear Test Database and Existing Prediction Methodsmentioning

confidence: 99%

“…Up to now, significant efforts have been made worldwide in response to the doubts just mentioned [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. Early shear tests [ 18 , 19 , 20 ] indicated that RAC beams have similar cracking pattern and failure modes with those of NAC ones, but present lower diagonal cracking load and inferior shear-resisting capacity.…”

Section: Introductionmentioning

confidence: 99%

“…Early shear tests [ 18 , 19 , 20 ] indicated that RAC beams have similar cracking pattern and failure modes with those of NAC ones, but present lower diagonal cracking load and inferior shear-resisting capacity. From then on, interest has been sparked, providing more invaluable experimental evidence into this area [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. For example, Fathifazl et al [ 24 ], Arezoumandi et al [ 25 , 26 ], and Knaack and Kurama [ 27 ] investigated the effects of replacement level and longitudinal reinforcement ratio on the shear behavior of RAC beams.…”

Section: Introductionmentioning

confidence: 99%

“…The results showed that an increased amount of RA may erode the beams’ initial stiffness, but the effect on the shear strength is relatively small; given the same volume of longitudinal reinforcement, the shear capacity of RAC beams is clearly lower than that of NAC beams [ 25 ]. Comparisons were also made between RAC beams with and without shear reinforcement [ 32 , 33 ]. An overall trend observed is that for shear-reinforced beams, a great portion of resistance is contributed by web steel, and there is no such evidence that the resistance is significantly affected by the use of recycled aggregates.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams

Yong

Zhao

et al. 2020

Materials

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Recycled aggregate concrete (RAC) is a promising solution to address the challenges raised by concrete production. However, the current lack of pertinent design rules has led to a hesitance to accept structural members made with RAC. It would entail even more difficulties when facing application scenarios where brittle failure is possible (e.g., beam in shear). In this paper, existing major shear design formulae established primarily for conventional concrete beams were assessed for RAC beams. Results showed that when applied to the shear test database compiled for RAC beams, those formulae provided only inaccurate estimations with surprisingly large scatter. To cope with this bias, machine learning (ML) techniques deemed as potential alternative predictors were resorted to. First, a Grey Relational Analysis (GRA) was carried out to rank the importance of the parameters that would affect the shear capacity of RAC beams. Then, two contemporary ML approaches, namely, the artificial neural network (ANN) and the random forest (RF), were leveraged to simulate the beams’ shear strength. It was found that both models produced even better predictions than the evaluated formulae. With this superiority, a parametric study was undertaken to observe the trends of how the parameters played roles in influencing the shear resistance of RAC beams. The findings indicated that, though less influential than the structural parameters such as shear span ratio, the effect of the replacement ratio of recycled aggregate (RA) was still significant. Nevertheless, the value of vc/(fc)1/2 (i.e., the shear contribution from RAC normalized with respect to the square root of its strength) predicted by the ML-based approaches appeared to be insignificantly affected by the replacement level. Given the existing inevitable large experimental scatter, more shear tests are certainly needed and, for safe application of RAC, using partial factors calibrated to consider the uncertainty is feasible when designing the shear strength of RAC beams. Some suggestions for future works are also given at the end of this paper.

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Section: Shear Test Database and Existing Prediction Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams

Yong

Zhao

et al. 2020

Materials

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“…Table 1 [ 15 , 20 , 21 , 22 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ] highlights the literature focusing on RCA’s effects on shear strength, with the last three rows focusing specifically on the effect of fibers addition on shear strength of RCA. RCA generally decreases shear strength in beams as well as cylindrical push-off specimens [ 22 , 50 , 52 , 55 , 56 , 57 , 58 , 61 ], in addition, other research efforts indicated that shear strength is decreasing as the RCA replacement ratio increases [ 15 , 63 ]. This reduction is caused by a myriad of factors, such as remaining mortar on RA and higher water absorption due to increased porosity in RA in comparison to normal weight aggregate (NWA) [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Shear Strength of Fiber Reinforced Recycled Aggregate Concrete

Ghoneim

Yehia

et al. 2020

Materials

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In this paper, shear strength of fiber reinforced recycled concrete was investigated. A Self Consolidated Concrete (SCC) matrix with 100% coarse recycled aggregate and different types of fibers were used in the study. Steel (3D and 5D), synthetic and hybrid fibers with a volume fraction of 0.75% were added to the concrete matrix to prepare eight beams. In addition, two beams were cast without fibers as control specimens. All beams were prepared without shear reinforcement and were tested to evaluate concrete contribution to the shear capacity. In addition, optical images were captured to allow for full-field displacement measurements using Digital Image Correlation (DIC). The results showed about 23.44–64.48% improvement in the average concrete shear capacity for fiber-reinforced beams when compared to that of the control specimens. The percentage improvement was affected by fiber type and the steel fiber beams achieved the best performance. The addition of the fiber delayed the crack initiation and improved the post-cracking and ductile behavior of all beams. Moreover, the experimental results were compared to those predicted by codes and proposed equations found in the literature for concrete strength with and without fibers.

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Automated analysis and design for recycled aggregate reinforced concrete beams

Saleh,

Murad,

Tarawneh

et al. 2023

Structural Concrete

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The goal of this study is to propose a robust method for the analysis and design of recycled aggregate reinforced concrete (RAC) beams. For this purpose, a machine‐learning framework, named, GA‐XGBoost was designed. A comprehensive dataset of 276 tests of RAC beams was compiled from available literature for training, validating, and testing the models. The results confirmed that the proposed models can offer a precise tool for failure mode classification and capacity prediction of RAC beams. For the design framework, a Generative Adversarial Network for tabular data was utilized to produce synthetic data. These synthetic data can provide options for the selection of design variables. In this framework, the design variables are selected by specifying the design constraints, the objective function to be optimized, and validating the capacity requirements using the developed GA‐XGBoost models. Lastly, a web application was developed to make the proposed GA‐XGBoost models appropriate for practical design.

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Shear performance of recycled aggregate concrete beams: An insight for design aspects

Cited by 55 publications

References 36 publications

Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams

Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams

Shear Strength of Fiber Reinforced Recycled Aggregate Concrete

Automated analysis and design for recycled aggregate reinforced concrete beams

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