Shear strength of longitudinally reinforced recycled aggregate concrete beams

Rahal, Khaldoun N.; Alrefaei, Yazan

doi:10.1016/j.engstruct.2017.05.028

Cited by 81 publications

(57 citation statements)

References 9 publications

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“…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%

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: Introductionmentioning

confidence: 99%

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

Yong

Zhao

et al. 2020

Materials

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“…Research efforts have been dedicated to characterizing the properties of RRC beams and columns in static loading conditions. The influence of RCAs on the monotonic shear [11][12][13][14][15][16][17][18][19][20][21][22][23] or flexural [24][25][26][27][28][29][30][31][32][33][34][35] behaviors of RRC beams has also been extensively experimentally investigated, with the replacement ratio (i.e., r) varied from 0% to 100%. The experimental outcomes indicate that: (i) in either shear or flexural failure, the presence of various RCA replacement ratios only has a limited influence on the damage process and failure pattern of RRC beams; (ii) an increased replacement ratio appears to reduce the load-carrying capacity of RRC beams.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Reinforced Recycled Aggregate Concrete Columns under Cyclic Loadings

et al. 2019

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Recycled concrete aggregates (RCAs) generated from construction and demolition activities have been recognized as a feasible alternative to natural aggregates (NAs). Naturally, the columns fabricated with reinforced recycled concrete (RRC) have been proposed and investigated to promote the structural use of recycled aggregate concrete (RAC). There is still, however, very limited modeling research available to reproduce, accurately and efficiently, the seismic response of RRC columns under lateral cyclic loading; proper evaluations are also lacking on addressing the columns’ seismic behaviors. To fill some of those research gaps, a fiber-based numerical model is developed in this study and then validated with the experimental results published in the literature. Subsequently, the numerical model justified is applied to carry out a comprehensive parametric study to examine the effects of a range of variables on the hysteretic characteristics of RRC columns. Furthermore, a grey relational analysis is conducted to establish quantifiable evidence of key variable sensitivities. The evaluation results imply that the use of the additional water method (AWM) for manufacturing RAC is likely to reduce the lateral load-carrying capacity of the RRC columns (up to 10%), whereas the opposite would occur if a conventional mixing procedure is adopted. Moreover, compared with other factors such as steel area ratio, the content of RCA replacement has a less remarkable effect on the seismic performance of the RRC columns. In general, the RRC columns possess acceptable seismic-resistant properties, and they can be used in earthquake-prone regions with confidence.

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“…The shear strength of RAC beams can be conservatively predicted by codes with similar reliability as for the corresponding beams with natural aggregate. Rahal and Alrefaei [10] used experimental testing to determine the effects of the using RCA on the shear strength of 13 reinforced concrete beams tested in a two-point loading scheme at a shear span to depth ratio of 3. Based on the experimental results and available data from the published literature, it was determined that the "square root of the compressive strength of the concrete" term does not adequately account for the detrimental effects that the shearing strength undergoes when RCA are used.…”

Section: Literature Searchmentioning

confidence: 99%

Shear Strength of Reinforced Concrete Beams Made with Recycled Aggregate

Tabsh¹,

Yehia²

2018

World Congress on Civil, Structural, and Environmental Engineering

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Using recycled concrete aggregate in new concrete mixes saves landfill space, reduces the need for gravel mining, and decreases pollution from transporting concrete to and from worksites. Recycled concrete aggregate may come from a variety of sources but the most common source is the demolition waste of old concrete structures. In this study, the recycled aggregate was obtained locally from Bee'ah's facility in Sharjah, UAE, and the percentage of coarse aggregate replacement in the concrete mix was either 50% or 100%, in addition to the control mix which included 100% natural aggregate. The target 28-day compressive strength based on 150mmx300mm cylinder was within the range 30-35MPa. The first phase of the study addressed the recycled aggregate tests and concrete mix design while the second phase involved shear strength of three half-scale reinforced concrete beams made with the recycled and natural aggregate. In the later part, the simply supported beams were tested under a single-point loading scheme inside a Universal Test Machine at a shear span-to-depth ratio of 1.5 to determine the load-deflection performance up to failure. Results of the study showed that the elastic stiffness, ultimate shear capacity and residual strength of steel reinforced beams made with 50% and 100% recycled coarse aggregate were comparable to those made entirely with natural aggregate and can be conservatively predicted by the theoretical equations of the ACI 318 code.

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Shear strength of longitudinally reinforced recycled aggregate concrete beams

Cited by 81 publications

References 9 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

Performance Evaluation of Reinforced Recycled Aggregate Concrete Columns under Cyclic Loadings

Shear Strength of Reinforced Concrete Beams Made with Recycled Aggregate

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