Combined effects of nano-silica and silica fume on the mechanical behavior of recycled aggregate concrete

Tang, Yunchao; Zheng, Chao; Feng, Wanhui; Nong, Yumei; Li, Cong; Chen, Jieming

doi:10.1515/ntrev-2021-0058

Cited by 77 publications

(33 citation statements)

References 44 publications

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“…The finding of their study shows that the rubber aggregates have greater unstable fracture toughness than the initial cracking toughness of recycled aggregate concrete after exposing to elevated temperature. Yunchao et al [ 32 ] studied the mutual effect silica-fume and nano-silica on the cubic compressive strength, split tensile strength and stress strain relationship of recycled aggregate concrete. A good pozzolanic activity of silica fume and nano-silica was reported and 6% silica-fume and 3% nano-silica were recommended as supplementary cementitious material for recycled aggregate concrete.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

et al. 2021

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To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming (GEP). A widespread database comprising 216 experimental records was constructed from available research. The database includes depth of wear as a response parameter and nine different explanatory variables, i.e., cement content, fly ash, water content, fine and coarse aggregate, plasticizer, air-entraining agent, age of concrete, and time of testing. The performance of the models was judged via statistical metrics. The GEP model gives better performance with R2 and ρ equals 0.9667 and 0.0501 respectively and meet with the external validation criterion suggested in the previous literature. The k-fold cross-validation also verifies the accurateness of the model by evaluating R2, RSE, MAE, and RMSE. The sensitivity analysis of GEP equation indicated that the time of testing is the influential parameter. The results of this research can help the designers, practitioners, and researchers to quickly estimate the depth of wear of fly-ash concrete thus shortening its ecological susceptibilities that push to sustainable and faster construction from the viewpoint of environmentally friendly waste management.

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Section: Introductionmentioning

confidence: 99%

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

et al. 2021

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“…Waste concrete is generated in a number of ways, including destroyed structures, abandoned precast concrete members, residual concrete in batching facilities, and concrete samples tested in laboratories [ 27 , 28 , 29 , 30 ]. Thus, incorporating recycled concrete aggregates (RCAs) in the building sector will be an economical and eco-friendly way to decrease CDW volume [ 31 , 32 ]. RAs are divided into three categories: recycled brick aggregate, RCA, and recycled mixed (bricks and concrete) aggregate.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Prediction Models Based on Machine Learning for the Compressive Strength Estimation of Recycled Aggregate Concrete

et al. 2022

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Numerous tests are used to determine the performance of concrete, but compressive strength (CS) is usually regarded as the most important. The recycled aggregate concrete (RAC) exhibits lower CS compared to natural aggregate concrete. Several variables, such as the water-cement ratio, the strength of the parent concrete, recycled aggregate replacement ratio, density, and water absorption of recycled aggregate, all impact the RAC’s CS. Many studies have been carried out to ascertain the influence of each of these elements separately. However, it is difficult to investigate their combined effect on the CS of RAC experimentally. Experimental investigations entail casting, curing, and testing samples, which require considerable work, expense, and time. It is vital to adopt novel methods to the stated aim in order to conduct research quickly and efficiently. The CS of RAC was predicted in this research utilizing machine learning techniques like decision tree, gradient boosting, and bagging regressor. The data set included eight input variables, and their effect on the CS of RAC was evaluated. Coefficient correlation (R2), the variance between predicted and experimental outcomes, statistical checks, and k-fold evaluations, were carried out to validate and compare the models. With an R2 of 0.92, the bagging regressor technique surpassed the decision tree and gradient boosting in predicting the strength of RAC. The statistical assessments also validated the superior accuracy of the bagging regressor model, yielding lower error values like mean absolute error (MAE) and root mean square error (RMSE). MAE and RMSE values for the bagging model were 4.258 and 5.693, respectively, which were lower than the other techniques employed, i.e., gradient boosting (MAE = 4.956 and RMSE = 7.046) and decision tree (MAE = 6.389 and RMSE = 8.952). Hence, the bagging regressor is the best suitable technique to predict the CS of RAC.

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“…Likewise, fine recycled concrete aggregates (FRCA) comprise the original virgin aggregate (OVA) and residual cement paste (RCP). The properties of RCA concrete made of CRCA have been extensively investigated [3,[10][11][12][13][14][15][16][17]; therefore, numerous documents such as ACI-555R [18], BS EN 1744-6:2006and BS EN 933-11:2009 along with RILEM technical reports, such as [19,20], provide guidelines and criteria on how to deal with and use CRCA for proportioning RCA concrete. Yet, although RCA's use in concrete has grown over the past years, concerns related to its mechanical properties, durability and long-term behaviour prevent its widespread use for structural purposes [6,9,14,21,22], especially due to the lack of efficient quality control and proportioning procedures.…”

Section: Introductionmentioning

confidence: 99%

Influence of the Mix Proportion and Aggregate Features on the Performance of Eco-Efficient Fine Recycled Concrete Aggregate Mixtures

et al. 2022

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Most of the previous research on recycled concrete aggregates (RCA) has focused on coarse RCA (CRCA), while much less has been accomplished on the use of fine RCA particles (FRCA). Furthermore, most RCA research disregards its unique microstructure, and thus the inferior performance of concrete incorporating RCA is often reported in the fresh and hardened states. To improve the overall behaviour of RCA concrete advanced mix design techniques such as equivalent volume (EV) or particle packing models (PPMs) may be used. However, the efficiency of these procedures to proportion eco-efficient FRCA concrete still requires further investigation. This work evaluates the overall fresh (i.e., slump and rheological characterization) and hardened states (i.e., non-destructive tests, compressive strength and microscopy) performance of sustainable FRCA mixtures proportioned through distinct techniques (i.e., direct replacement, EV and PPMs) and incorporating different types of aggregates (i.e., natural and manufactured sand) and manufacturing processes (i.e., crusher fines and fully ground). Results demonstrate that the aggregate type and crushing process may influence the FRCA particles’ features. Yet, the use of advanced mix design techniques, particularly PPMs, may provide FRCA mixes with quite suitable performance in the fresh (i.e., 49% lower yield stress) and hardened states (i.e., 53% higher compressive strength) along with a low carbon footprint.

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Combined effects of nano-silica and silica fume on the mechanical behavior of recycled aggregate concrete

Cited by 77 publications

References 44 publications

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

Comparison of Prediction Models Based on Machine Learning for the Compressive Strength Estimation of Recycled Aggregate Concrete

Influence of the Mix Proportion and Aggregate Features on the Performance of Eco-Efficient Fine Recycled Concrete Aggregate Mixtures

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