Mechanical and durability properties of recycled aggregate concrete with ternary binder system and optimized mix proportion

Babalola, Olusola Emmanuel; Awoyera, Paul O.; Tran, Minh-Phung; Le, Duc-Hien; Olalusi, Oladimeji B.; Viloria, Amelec; Ovallos-Gazabon, David

doi:10.1016/j.jmrt.2020.04.038

Cited by 48 publications

(10 citation statements)

References 39 publications

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“…For Family V, the control SCC compressive strength at 28 days lies in the range of 30 MPa to 40 MPa and their corresponding split tensile strengths at 28 days are tabulated in Table 5. Apart from Aslani et al, 2018 [85] and Babaloa et al, 2020 [95], there is no such drastic increase in W/C or W/B ratio when compared to the other literature in Family V. However, when compared to family IV there is an increase in W/C and W/B ratio. Bahrami et al, 2020b [62], Sun et al [96], 2020, and Surendar et al, 2021 [97] used a constant TA/C ratio, FA/CA ratio, and W/S ratio in percentage and reported that there was a slight variation in the strength requirement for minimum modification in aggregates.…”

Section: Design Parameters From Literaturementioning

confidence: 80%

Effect of Design Parameters on Compressive and Split Tensile Strength of Self-Compacting Concrete with Recycled Aggregate: An Overview

Jagadesh¹,

Juan-Valdés

Guerra‐Romero

et al. 2021

Applied Sciences

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One of the prime objectives of this review is to understand the role of design parameters on the mechanical properties (Compressive and split tensile strength) of Self-Compacting Concrete (SCC) with recycled aggregates (Recycled Coarse Aggregates (RCA) and Recycled Fine Aggregates (RFA)). The design parameters considered for review are Water to Cement (W/C) ratio, Water to Binder (W/B) ratio, Total Aggregates to Cement (TA/C) ratio, Fine Aggregate to Coarse Aggregate (FA/CA) ratio, Water to Solid (W/S) ratio in percentage, superplasticizer (SP) content (kg/cu.m), replacement percentage of RCA, and replacement percentage of RFA. It is observed that with respect to different grades of SCC, designed parameters affect the mechanical properties of SCC with recycled aggregates.

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Section: Design Parameters From Literaturementioning

confidence: 80%

Effect of Design Parameters on Compressive and Split Tensile Strength of Self-Compacting Concrete with Recycled Aggregate: An Overview

Jagadesh¹,

Juan-Valdés

Guerra‐Romero

et al. 2021

Applied Sciences

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“…Family V consists of eight articles from literature with reference SCC compressive strength at 28 days lying in the range of 30 MPa to 40 MPa and corresponding split tensile strength lying in the range of 2.28 MPa to 4.12 MPa at 28 days as tabulated in Table 5. Apart from Aslani et al, 2018 [54] and Babalola et al, 2020 [67], there are no drastic increases shown in W/C or W/B ratio when compared to other literature in Family V. Compared to previous families, there is an increase in W/C and W/B ratio observed for family V. Requirement of SP is reduced to 1.90 kg to 6.10 kg for family V when compared to other families due to increase in water content of the mix. Due to an increase in water content, there is an increase in the W/S ratio for family V observed.…”

Section: Design Parameters From Literaturementioning

confidence: 99%

Impact of Design Parameters on the Ratio of Compressive to Split Tensile Strength of Self-Compacting Concrete with Recycled Aggregate

Martínez‐García

Jagadesh²,

Búrdalo

et al. 2021

Materials

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Most concrete studies are concentrated on mechanical properties especially strength properties either directly or indirectly (fresh and durability properties). Hence, the ratio of split tensile strength to compressive strength plays a vital role in defining the concrete properties. In this review, the impact of design parameters on the strength ratio of various grades of Self-Compacting Concrete (SCC) with recycled aggregate is assessed. The design parameters considered for the study are Water to Cement (W/C) ratio, Water to Binder (W/B) ratio, Total Aggregates to Cement (TA/C) ratio, Fine Aggregate to Coarse Aggregate (FA/CA) ratio, Water to Solid (W/S) ratio in percentage, superplasticizer (SP) content (kg/cu.m), replacement percentage of recycled coarse aggregates (RCA), replacement percentage of recycled fine aggregates (RFA), fresh density and loading area of the specimen. It is observed that the strength ratio of SCC with recycled aggregates is affected by design parameters.

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“…Thus, results in the degrades in the concrete quality in terms of mechanical and durability properties. A drop in the concrete compressive strength of about 15% minimum is noted in the literature [ 6 – 8 ]. The effect of the recycled aggregates on the SCC fresh concrete properties is reported by various literatures [ 8 – 11 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete

Jagadesh,

Khan,

Priya

et al. 2024

PLoS ONE

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This research study aims to understand the application of Artificial Neural Networks (ANNs) to forecast the Self-Compacting Recycled Coarse Aggregate Concrete (SCRCAC) compressive strength. From different literature, 602 available data sets from SCRCAC mix designs are collected, and the data are rearranged, reconstructed, trained and tested for the ANN model development. The models were established using seven input variables: the mass of cementitious content, water, natural coarse aggregate content, natural fine aggregate content, recycled coarse aggregate content, chemical admixture and mineral admixture used in the SCRCAC mix designs. Two normalization techniques are used for data normalization to visualize the data distribution. For each normalization technique, three transfer functions are used for modelling. In total, six different types of models were run in MATLAB and used to estimate the 28th day SCRCAC compressive strength. Normalization technique 2 performs better than 1 and TANSING is the best transfer function. The best k-fold cross-validation fold is k = 7. The coefficient of determination for predicted and actual compressive strength is 0.78 for training and 0.86 for testing. The impact of the number of neurons and layers on the model was performed. Inputs from standards are used to forecast the 28th day compressive strength. Apart from ANN, Machine Learning (ML) techniques like random forest, extra trees, extreme boosting and light gradient boosting techniques are adopted to predict the 28th day compressive strength of SCRCAC. Compared to ML, ANN prediction shows better results in terms of sensitive analysis. The study also extended to determine 28th day compressive strength from experimental work and compared it with 28th day compressive strength from ANN best model. Standard and ANN mix designs have similar fresh and hardened properties. The average compressive strength from ANN model and experimental results are 39.067 and 38.36 MPa, respectively with correlation coefficient is 1. It appears that ANN can validly predict the compressive strength of concrete.

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Mechanical and durability properties of recycled aggregate concrete with ternary binder system and optimized mix proportion

Cited by 48 publications

References 39 publications

Effect of Design Parameters on Compressive and Split Tensile Strength of Self-Compacting Concrete with Recycled Aggregate: An Overview

Effect of Design Parameters on Compressive and Split Tensile Strength of Self-Compacting Concrete with Recycled Aggregate: An Overview

Impact of Design Parameters on the Ratio of Compressive to Split Tensile Strength of Self-Compacting Concrete with Recycled Aggregate

Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete

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