An Investigation on Mechanical and Physical Properties of Recycled Coarse Aggregate (RCA) Concrete with GGBFS

Tüfekçi, M. Mansur; Çakır, Özgür

doi:10.1007/s40999-017-0167-x

Cited by 56 publications

(23 citation statements)

References 31 publications

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“…Numerous investigations were performed to calculate GGBFS concrete mix design, including the experimental and statistical methods. Some experimental investigations have been carried out to estimate the compressive strength of GGBFS concrete [5][6][7][8][9]. However, the experimental methods are, in general, time consuming and relatively costly.…”

Section: Introductionmentioning

confidence: 99%

Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model

Thi

Nguyen

et al. 2021

Advances in Civil Engineering

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Improvement of compressive strength prediction accuracy for concrete is crucial and is considered a challenging task to reduce costly experiments and time. Particularly, the determination of compressive strength of concrete using ground granulated blast furnace slag (GGBFS) is more difficult due to the complexity of the composition mix design. In this paper, an approach using random forest (RF), which is one of the powerful machine learning algorithms, is proposed for predicting the compressive strength of concrete using GGBFS. The RF model is first evaluated to determine the best architecture, which constitutes 500 growth trees and leaf size of 1. In the next step, the evaluation of the model is conducted over 500 simulations considering the effect of random sampling. Finally, the best prediction results are given in function of statistical measures such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), respectively, which are 0.9729, 4.9585, and 3.9423 for the testing dataset. The results show that the RF algorithm is an excellent predictor and practically used for engineers to reduce experimental cost.

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Section: Introductionmentioning

confidence: 99%

Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model

Thi

Nguyen

et al. 2021

Advances in Civil Engineering

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“…To solve the complexity of the use of RA in new concrete safely, some researchers proposed new mixture prescriptions including mineral additions (i.e., silica fume (SF) [3,4], ground granulated blast furnace slag (GGBS) [5,6], metakaolin [7], fly ash (FA) [8][9][10][11], fibers with/without mineral addition (i.e., polypropylene [9], polypropylene + SF [12], steel fiber + SF [4], basalt fiber [13,14], basalt fiber + nano-silica [15], waste-plastic strip + FA [16], steel fiber + FA [17], steel fiber + GGBS [18], glass fiber + FA [19] or redefining mixing method (i.e., Equivalent Mortar Volume Method (EMV) [20,21], two stage mixing method [22]. The suggestions presented satisfactory results and gave confidence to mix designers of RAC.…”

Section: Introductionmentioning

confidence: 99%

Mineral Addition and Mixing Methods Effect on Recycled Aggregate Concrete

Dilbas

Güneş

2021

Materials

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This paper presents influence of treatment and mixing methods on recycled aggregate concretes (RAC) designed regarding various techniques. Absolute Volume Method (AVM) according to TS 802, Equivalent Mortar Volume Method (EMV), silica fume (SF) as a mineral addition were considered in the design of concretes. In total, four groups of concretes were produced in the laboratory: 1) natural aggregate concrete (NAC) designed with AVM as control concrete, 2) RAC designed with AVM as control RAC, 3) RAC with SF as a mineral addition designed with AVM as treated RAC and 4) RAC designed with EMV as treated RAC. The tests were performed at 28th days and the statistical analysis were made on the test results. According to the results, EMV and SF increased the compressive strength of concretes and this resulted an increase in the strength class of concrete. A significant statistical difference between the concretes were determined. According to multiple comparison analysis, it was found that especially there was a significant relationship among NAC, RAC and RAC-EMV. In addition, it was recommended that EMV and AVM with 5% SF could be used in the design of RAC rather than AVM only to achieve the target strength class C30/37.

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“…Siddique and Kaur [8] have concluded that 20% of cement replaced with BFS can be used appropriately in structures resistant to high temperature. Tüfekçi and Çakır [9] have shown that the compressive strength of concrete using 60% BFS content reached the highest value at 28 days. Besides, Majhi et al [10] have experienced the highest concrete compressive strength by using 40% BFS replacement.…”

Section: Introductionmentioning

confidence: 99%

Investigation of ANN Model Containing One Hidden Layer for Predicting Compressive Strength of Concrete with Blast‐Furnace Slag and Fly Ash

Thi

Nguyen

et al. 2021

Advances in Materials Science and Engineering

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The prediction accuracy of concrete compressive strength is important and considered a challenging task, aiming at reducing costly and time-consuming experiments. Moreover, compressive strength prediction of concrete using blast-furnace slag (BFS) and fly ash (FA) is more difficult due to the complex mix design of a composition. In this investigation, an approach using the artificial neuron network (ANN), one of the most powerful machine learning algorithms, is applied to predict the compressive strength of concrete containing BFS and FA. The ANN models with one hidden layer containing 13 neuron number cases are proposed to determine the best ANN structure. Under the effect of random sampling strategies and the network structures selected, Monte Carlo simulations (MCS) are introduced to statistically investigate the convergence of results. Next, the evaluation of the model is concluded over 100 simulations for the convergence analysis. The results show that ANN is a highly efficient predictor of the compressive strength using BFS and FA, with maximum values of the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) of 0.9437, 3.9474, and 2.9074, respectively, on the training part and 0.9285, 4.4266, and 3.2971, respectively, for the testing part. The best-defined structure of ANN is [8-24-1], with 24 neurons in the hidden layer. Partial Dependence Plots (PDP) are also performed to investigate the dependence of the prediction results of input variables used in the ANN model. The age of sample and cement content are found to be the two most crucial factors that affect the compressive strength of concrete using BFS and FA. The ANN algorithm is practical for engineers to reduce costly experiments.

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An Investigation on Mechanical and Physical Properties of Recycled Coarse Aggregate (RCA) Concrete with GGBFS

Cited by 56 publications

References 31 publications

Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model

Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model

Mineral Addition and Mixing Methods Effect on Recycled Aggregate Concrete

Investigation of ANN Model Containing One Hidden Layer for Predicting Compressive Strength of Concrete with Blast‐Furnace Slag and Fly Ash

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