Uncertainty of shear resistance models: Influence of recycled concrete aggregate on beams with and without shear reinforcement

Pacheco, João; Brito, Jorge de; Chastre, Carlos; Evangelista, Luís

doi:10.1016/j.engstruct.2019.109905

Cited by 36 publications

(32 citation statements)

References 42 publications

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“…Note that in the previous study by Pacheco et al [ 40 ] beams with a shear span ratio less than 2.5 were excluded for properly evaluating the applicability of existing shear design provisions for RAC beams. Additionally, Pacheco et al [ 40 ] found that a high longitudinal reinforcement ratio (approaching 4.0%) may influence the code validation. In this study, these test data were retained because we intend to develop a unified ML-based predictive approach that adapts to various parameters with a wide range.…”

Section: Shear Test Database and Existing Prediction Methodsmentioning

confidence: 99%

“…The major contributing factors to the inadequacy of the evaluated methods may include: (1) the existing methods are established primarily for conventional concrete beams, and thus cannot convey the particular material characteristics of RAC; (2) due to the brittleness feature of shear failure, judicious and prudent considerations must be paid when developing shear equations [ 73 , 74 , 75 ]. This inevitably leads to a conservatism (more or less) for concrete beams in shear, irrespective of their concrete type; and (3) the detrimental impact of incorporating RA becomes more obvious with increasing concrete strength [ 40 ], consequently posing significant safety challenges when applying existing methods to these cases without any modification. As such, establishing an accurate shear resistance prediction for RAC beams is indeed a strenuous task and necessitates more effort.…”

Section: Shear Test Database and Existing Prediction Methodsmentioning

confidence: 99%

“…Though the ANN and RF models agree with the average trend line (the existing design methods nonetheless underestimate it), the uncertainty is still a big problem. In this sense, it does require more experimental results to reinforce the conclusion [ 40 ].…”

Section: Parametric Study With Reference To the Shear Test Databasmentioning

confidence: 99%

“…Overall, it is safe to say that, within the scope of the current test database, the ML approaches provide statistically accurate shear strength predictions, whether the replacement ratio is 0 or 100%. In addition, the above explanations justify that, in order to reduce the uncertainty and to achieve a similar safety margin between RAC and NAC beams, design-oriented treatments such as using partial factors calibrated to account for the detrimental influence of RA inclusion are reasonable and can be applied when determining the shear strength of RAC beams (as investigated in [ 40 ]).…”

Section: Parametric Study With Reference To the Shear Test Databasmentioning

confidence: 99%

“…This is not surprising, since a recent study by Pacheco et al [ 40 ] has confirmed that, after a strict filtering of previous test data, only eighteen shear-reinforced RAC beams are considered suitable for the examination of code provisions, indicating the very scarcity of such tests. It also turned out that the code-based predictions for RAC beams with stirrups are more conservative and show higher variability than those for RAC beams without stirrups [ 40 ]. Such a finding seemingly contradicts one’s initial intuition, but just highlights the complex nature of RAC beams in shear.…”

Section: Introductionmentioning

confidence: 97%

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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: Shear Test Database and Existing Prediction Methodsmentioning

confidence: 99%

Section: Parametric Study With Reference To the Shear Test Databasmentioning

confidence: 99%

Section: Parametric Study With Reference To the Shear Test Databasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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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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Bauen mit R‐Beton – Auswirkungen auf die Bemessung und Konstruktion von Stahlbetonbauteilen

Sinning,

Adam,

Claßen

et al. 2024

Beton und Stahlbetonbau

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Neben der drastischen Reduktion von CO2‐Emissionen bei der Zementherstellung ist ein effizienter Umgang mit knapper werdenden natürlichen Ressourcen wie Sand und Kies unumgänglich, um die Betonindustrie umweltfreundlicher zu gestalten. In diesem Zusammenhang erscheint die Verwendung rezyklierter Gesteinskörnung als sinnvolle Alternative zu natürlicher Gesteinskörnung in herkömmlichem Beton. Seit 1998 ist der Einsatz rezyklierter Gesteinskörnung in Konstruktionsbeton in Deutschland nach der Richtlinie des Deutschen Ausschusses für Stahlbeton (DAfStb) möglich. In der Neuauflage des Eurocode 2 (FprEC2) werden erstmals Regelungen für die Verwendung von Beton mit rezyklierter Gesteinskörnung (R‐Beton) aufgenommen. In diesem Beitrag wird zunächst auf die Eigenschaften von R‐Beton im Vergleich zu Beton mit natürlicher Gesteinskörnung eingegangen. Anschließend werden die aktuellen Regelungen zur Bemessung einander gegenübergestellt und ein Ausblick auf zukünftige Forschung gegeben, die nicht nur den Ressourcenschutz, sondern auch die wirksame Reduktion der CO2‐Emissionen in der Betonproduktion im Fokus hat.

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Toward a codified design of recycled aggregate concrete structures: Background for the new fib Model Code 2020 and Eurocode 2

Tošić

Torrenti

Sedran

et al. 2020

Structural Concrete

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The use of recycled aggregate (RA) to produce recycled aggregate concrete (RAC) is a proven way of decreasing the consumption of natural aggregate (NA) and the landfilling of construction and demolition waste. However, adoption of codes for the design of RAC and RAC structures has been lacking. Within the framework of the new fib Model Code 2020 and Eurocode 2, provisions for RAC can be adopted. Therefore, in this study, a comprehensive and critical review of literature on RAC is performed as well as own meta‐analyses of results. Material properties of RAC and structural behavior of reinforced and prestressed RAC members are analyzed, and based on the findings, code adjustments for RAC are proposed. The results show that, in order to incorporate RAC into design codes, changes are necessary in expressions for physical–mechanical properties (volumetric mass, modulus of elasticity, tensile strength, fracture energy, peak and ultimate strains, shrinkage strain and creep coefficient), durability‐related properties (minimum concrete cover for durability) and structural behavior (shear strength of members not requiring shear reinforcement and deflections). The recommendations are formulated in terms of the total mass substitution ratio of RA that can be classified as Type A according to standard EN 206 for concrete. The results and findings presented herein can provide an important contribution toward the codification of RAC use and the wider utilization of RA in construction.

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Uncertainty of shear resistance models: Influence of recycled concrete aggregate on beams with and without shear reinforcement

Cited by 36 publications

References 42 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

Bauen mit R‐Beton – Auswirkungen auf die Bemessung und Konstruktion von Stahlbetonbauteilen

Toward a codified design of recycled aggregate concrete structures: Background for the new fib Model Code 2020 and Eurocode 2

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