Influence of Raw Concrete Material Quality on Selected Properties of Recycled Concrete Aggregates

Koper, Artur; Koper, Włodzimierz; Koper, Marcin

doi:10.1016/j.proeng.2017.02.063

Cited by 24 publications

(16 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…water absorption, amount of attached mortar, size, and texture) and the mechanical (i.e. parent concrete strength) characteristics of the RCAs as well as the methods adopted for the extraction process of RCAs from CDW (Koper et al, 2017; Laserna and Montero, 2016; Lotfi et al, 2015; McGinnis et al, 2017; Xiao et al, 2007). Different authors investigated different techniques to improve the mechanical performances of RACs such as treatment methods for RCA (Dimitriou et al, 2018; Ismail et al, 2017; Katkhuda and Shatarat, 2016; Kim et al, 2018), use of different mix proportions (Fathifazl et al, 2009a; Gupta et al, 2015; Leite and Santana, 2019; Rajhans et al, 2019), and inclusion of fiber reinforcements (Hanumesh et al, 2018; Wang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A review of experimental results on structural performance of reinforced recycled aggregate concrete beams and columns

Deresa

Demartino

et al. 2020

Advances in Structural Engineering

View full text Add to dashboard Cite

This article presents a comprehensive and critical review of the structural performances of reinforced recycled aggregate concrete beams and columns based on experimental results reported in the literature. Extensive data sets collected from the literature are categorized to investigate the effects on the local and global structural behavior. First, the flexural and shear response of reinforced recycled aggregate concrete beams is discussed. The structural performances are reviewed focusing on the main geometric and material variables such as the recycled concrete aggregate replacement ratio, the longitudinal reinforcement ratio, the transverse reinforcement ratio, and the shear span-to-depth ratio. Then, the behavior of reinforced recycled aggregate concrete columns under concentric and eccentric compressive loads and the seismic performance under low cyclic loading are discussed. The similarities and the differences between reinforced recycled aggregate concrete and reinforced natural aggregate concrete beams and columns are highlighted. The need for further research is pointed out at the end of the article. The results reported in this review clearly indicate that reinforced recycled aggregate concrete beams and columns with various recycled concrete aggregate replacement ratios have comparable or slightly lower structural performances to the reinforced natural aggregate concrete ones indicating the feasibility of recycled concrete aggregate for structural applications.

show abstract

Section: Introductionmentioning

confidence: 99%

A review of experimental results on structural performance of reinforced recycled aggregate concrete beams and columns

Deresa

Demartino

et al. 2020

Advances in Structural Engineering

View full text Add to dashboard Cite

show abstract

“…The chemical properties are, for example, the content of alkali, soluble sulphides, and the total representation of different building materials in the recyclate. The more types of matter commonly found in C&DW, the worse will be the achieved properties of the RCA [7][8][9][10]. RCA made from waste concrete is a problematic material for concrete production, as it has a variable portion of original cement mortar adhering to the aggregate's surface, resulting in higher water absorption.…”

Section: Introductionmentioning

confidence: 99%

Effect of Surface Modifications of Recycled Concrete Aggregate on Concrete Properties

Junak

Sičáková

2017

Buildings

View full text Add to dashboard Cite

The experiment aims to test the specific way of producing concrete with recycled concrete aggregate (RCA). To reduce its negative impact on the concrete properties, two different ways of treatment of the RCA with geopolymer slurry were applied-coating during the mixing using the specific mixing approach and coating prior to the mixing of concrete. As control samples, a mixture prepared by natural aggregate only and a mixture with RCA that was prepared by standard mixing with no coating process were tested as well. The results of density, total water absorption, and compressive strength in periods of 28, 90, 180, and 365 days of curing are presented and evaluated. Both methods of coating of the RCA with geopolymer slurry allow for the preparation of concrete with properties comparable to those of normal concrete (prepared by standard mixing with natural aggregate); thus, it seems to be a promising way to enhance the rate of RCA application. The positive effect of coating is clearly visible after a longer period of curing (180 days). When comparing the methods of RCA coating, coating directly during the mixing yields somewhat better results; it is also positive from the technological point of view, since the process is simpler in practice.

show abstract

“…The importance of water absorption of NA (r N ) and water absorption of RCA (r R ) is 7.2% and 3.2 respectively. Studies have shown that the water absorption of RAC is greater than that of NA and that less absorbent aggregates have higher strength RAC (Koper et al, 2017). The importance of the bulk density of RAC (m R ) and the bulk density of NA (m N ) is 4.1% and 7.4 respectively.…”

Section: Feature Sensitivity Analysismentioning

confidence: 99%

Prediction of compressive strength of recycled aggregate concrete using machine learning and Bayesian optimization methods

Zhang

Dai²,

Li³

et al. 2023

Front. Earth Sci.

View full text Add to dashboard Cite

With the sustainable development of the construction industry, recycled aggregate (RA) has been widely used in concrete preparation to reduce the environmental impact of construction waste. Compressive strength is an essential measure of the performance of recycled aggregate concrete (RAC). In order to understand the correspondence between relevant factors and the compressive strength of recycled concrete and accurately predict the compressive strength of RAC, this paper establishes a model for predicting the compressive strength of RAC using machine learning and hyperparameter optimization techniques. RAC experimental data from published literature as the dataset, extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), support vector machine regression Support Vector Regression (SVR), and gradient boosted decision tree (GBDT) RAC compressive strength prediction models were developed. The models were validated and compared using correlation coefficients (R2), Root Mean Square Error (RMSE), mean absolute error (MAE), and the gap between the experimental results of the predicted outcomes. In particular, The effects of different hyperparameter optimization techniques (Grid search, Random search, Bayesian optimization-Tree-structured Parzen Estimator, Bayesian optimization- Gaussian Process Regression) on model prediction efficiency and prediction accuracy were investigated. The results show that the optimal combination of hyperparameters can be searched in the shortest time using the Bayesian optimization algorithm based on TPE (Tree-structured Parzen Estimator); the BO-TPE-GBDT RAC compressive strength prediction model has higher prediction accuracy and generalisation ability. This high-performance compressive strength prediction model provides a basis for RAC’s research and practice and a new way to predict the performance of RAC.

show abstract

Influence of Raw Concrete Material Quality on Selected Properties of Recycled Concrete Aggregates

Cited by 24 publications

References 6 publications

A review of experimental results on structural performance of reinforced recycled aggregate concrete beams and columns

A review of experimental results on structural performance of reinforced recycled aggregate concrete beams and columns

Effect of Surface Modifications of Recycled Concrete Aggregate on Concrete Properties

Prediction of compressive strength of recycled aggregate concrete using machine learning and Bayesian optimization methods

Contact Info

Product

Resources

About