Statistical and Reliability Study on Shear Strength of Recycled Coarse Aggregate Reinforced Concrete Beams

Ju, Hyunjin; Yerzhanov, Meirzhan; Serik, Alina; Lee, Deuck Hang; Kim, Jong Ryeol

doi:10.3390/ma14123321

Cited by 20 publications

(4 citation statements)

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“…High-strength and high-performance construction materials are also becoming more commonly utilized in practice nowadays than ever before, and such materials are not often adequately covered in the existing codes [37]. In addition, newer materials, such as those that employ composites, are being developed to substitute for conventional materials that, past experience has demonstrated, have some deficiencies when subjected to severe environmental exposure [38]. The aforementioned issues particularly affect the statistical parameters of the resistance variables, which in turn impact the magnitude of the resistance factors corresponding to a target level of safety.…”

Section: Problem Statementmentioning

confidence: 99%

Resistance Factor Spectra for the Ultimate Limit State of the National Building Code of Canada

Tabsh,

Leblouba

2024

Buildings

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Over the years, structural engineering codes and specifications in Canada and elsewhere have moved from an allowable stress design (ASD) approach to a load and resistance factor design (LRFD) philosophy. LRFD methodology takes better account of the inherent variability in both loading and resistance by providing different factors of safety for loads of distinct natures with regard to their probability of overload, frequency of occurrences and changes in point of application. The method also results in safer structures because it considers the behavior at collapse. While resistance factors for traditional construction materials based on LRFD in the National Building Code (NBC) of Canada are available, they cannot be used for non-conventional ones. This is because the resistance of such materials due to various load effects has unique bias factors (λR) and coefficients of variation (VR), which greatly impact their reliability index (β). In this study, relationships between the resistance factor ϕ and critical load effects from the NBC load combinations at ultimate limit states are developed for a wide range of resistance bias factors and coefficients of variation. The relationships are presented in the form of charts that are useful for researchers and code-writing professionals who have expertise in the various fields of structural engineering but lack proper background in reliability theory. The developed spectra showed that for the same ϕ, β increases with an increase in the live-to-dead load (L/D) ratio until it reaches 1; thereafter, the shape of the relationship will depend on the statistics of the resistance as well as on the magnitude of ϕ. For a small ϕ and VR, β will keep increasing with an increase in the L/D ratio from 1 until 3, albeit at a lesser rate. For L/D > 3, the relationship between the critical β and applied load is just about constant. This finding is also true for load combinations involving snow and wind. Application of the method is illustrated by a practical example involving the shear strength of a corrugated web steel beam.

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Section: Problem Statementmentioning

confidence: 99%

Resistance Factor Spectra for the Ultimate Limit State of the National Building Code of Canada

Tabsh,

Leblouba

2024

Buildings

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“…Statistical methods seek to address the drawbacks of formula analysis by employing standard statistical procedures to construct empirical models tailored for optimizing mix designs [18]. Nonetheless, this technique necessitates an ample dataset to train statistical models that accurately predict concrete performance, potentially falling short of meeting the diverse objectives of speci c projects [19,20]. In addition, the statistical methods may not capture the complex relationship between the components of ternary geopolymers and their multiple objectives.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of ternary geopolymers with multiple solid wastes using machine learning and NSGA-II

Zhang,

Shang,

Huo

et al. 2024

Preprint

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The design of the mixtures of the ternary geopolymer is challenging due to the need to balance multiple objectives, including cost, strength, and carbon emissions. In order to address this multi-objective optimization (MOO) problem, machine learning models and the NSGA-II algorithm are employed in this study. To train the machine learning models, namely Artificial Neural Network (ANN), Support Vector Regressor, Extremely Randomized Tree, and Gradient Boosting Regression, 120 uniaxial compressive strength (UCS) values of ternary geopolymers with fly ash (FA), granulated blast furnace slag (GBFS) and steel slag (SS) as precursor materials were obtained from laboratory tests. Results show that the ternary geopolymer with the ratio of FA:GBFS:SS of 2:5:3 has the highest 28-d UCS of 46.8 MPa. The predictive accuracy of the ANN model is the highest with R = 0.949 and RMSE = 3.988MPa on the test set. Furthermore, the Shapley Additive Explanations analysis indicates that precursor materials exhibit the most significant influence on the UCS, particularly the content of GBFS. Based on the ANN model and NSGA-II algorithm, a multi-objective optimization (MOO) model is developed to optimize simultaneously the strength, cost and carbon emission of the ternary geopolymer. The derived MOO model can be used to design mixtures of other cementitious materials with multiple objectives.

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“…Based on the research findings, the usage of RA is limited due to the lack of structural standards for RA [5]. It may be that an insignificant amount of construction waste is recycled or used as a substitute to construct the naturally sourced materials.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review on Recycling of Construction Demolition Waste in Concrete

et al. 2023

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There have been efforts to use building demolition waste as an alternative aggregate in concrete to decrease the use of natural resources for construction. The World Green Building Council estimates that the construction industry is responsible for more than 50% of all material extracted globally and that construction and demolition waste makes up 35% of global landfills. As a result, incorporating recycled aggregate (RA) in concrete production is a prudent course of action to reduce the environmental impact. This study reviews prior research on using recycled aggregate instead of conventional ingredients in concrete. The composition and morphology of different types of RA, the behavior of RA in fresh and hardened states, keyword co-occurrence and evolution analysis, and the various additives used to enhance the inferior properties of RA are discussed. The RA showed different physical properties when compared with natural aggregate. However, the addition of pozzolanic materials and various pretreatment techniques is desirable for improving the inferior properties of RA. While building waste has been utilized as a substitute for fine and coarse aggregate, prior research has demonstrated that a modified mixing approach, an adequate mixing proportion, and the optimum replacement of cementitious materials are necessary. Based on the review, the recommendation is to use RA at a replacement level of up to 30% and the addition of precoated and pozzolanic materials as a treatment to provide concrete with adequate workability, strength, and durability for structural applications.

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Statistical and Reliability Study on Shear Strength of Recycled Coarse Aggregate Reinforced Concrete Beams

Cited by 20 publications

References 50 publications

Resistance Factor Spectra for the Ultimate Limit State of the National Building Code of Canada

Resistance Factor Spectra for the Ultimate Limit State of the National Building Code of Canada

Multi-objective optimization of ternary geopolymers with multiple solid wastes using machine learning and NSGA-II

A Comprehensive Review on Recycling of Construction Demolition Waste in Concrete

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