Evaluating the synergic effect of waste rubber powder and recycled concrete aggregate on mechanical properties and durability of concrete

Amiri, Mostafa; Hatami, Farzad; Golafshani, Emadaldin Mohammadi

doi:10.1016/j.cscm.2021.e00639

Cited by 27 publications

(20 citation statements)

References 33 publications

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“…It can be seen from Figure 18 that the model predicts better results for the literature [ 58 , 59 ]. The predicted curves for the literature [ 57 ], showed some deviations, but the overall trend was consistent. However, the errors are acceptable in terms of the overall results of Section 6.3 .…”

Section: Discussionsupporting

confidence: 68%

“…The model’s predictions can be judged from Figure 17 , in general agreement with the experimental results. Figure 18 represents the prediction results of the model for the literature [ 57 , 58 , 59 ]. It can be seen from Figure 18 that the model predicts better results for the literature [ 58 , 59 ].…”

Section: Discussionmentioning

confidence: 99%

“…Since the experimental data were collected from the published literature, processing is required to allow the model to learn better. This study collected 88 sets of RC mixed ratio data [ 22 , 54 , 55 , 56 , 57 , 58 , 59 ]. Three ML algorithms using nine input variables were used: (1) measurement method, (2) cement content, (3) water reducing agent content, (4) water content, (5) water to ash ratio, (6) fine aggregate content, (7) coarse aggregate content, (8) rubber size, (9) rubber content.…”

Section: Database Description and Analysis Of Variablesmentioning

confidence: 99%

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Chloride Permeability Coefficient Prediction of Rubber Concrete Based on the Improved Machine Learning Technical: Modelling and Performance Evaluation

Huang

Wang

et al. 2023

Polymers

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The addition of rubber to concrete improves resistance to chloride ion attacks. Therefore, rapidly determining the chloride permeability coefficient (DCI) of rubber concrete (RC) can contribute to promotion in coastal areas. Most current methods for determining DCI of RC are traditional, which cannot account for multi-factorial effects and suffer from low prediction accuracy. Machine learning (ML) techniques have good non-linear learning capabilities and can consider the effects of multiple factors compared with traditional methods. However, ML models easily fall into the local optimum due to their parameters’ influence. Therefore, a mixed whale optimization algorithm (MWOA) was developed in this paper to optimize ML models. The main strategies are to introduce Tent mapping to expand the search range of the algorithm, to use an adaptive t-distribution dimension-by-dimensional variation strategy to perturb the optimal fitness individual to thereby improve the algorithm’s ability to jump out of the local optimum, and to introduce adaptive weights and adaptive probability threshold values to enhance the adaptive capacity of the algorithm. For this purpose, data were collected from the published literature. Three machine learning models, Extreme Learning Machine (ELM), Random Forest (RF), and Elman Neural Network (ELMAN), were built to predict the DCI of RC, and the three models were optimized using MWOA. The calculations show that the MWOA is effective with the optimized ELM, RF, and ELMAN models improving the prediction accuracy by 54.4%, 62.9%, and 36.4% compared with the initial model. The MWOA-ELM model was found to be the optimal model after a comparative analysis. The accuracy of the multiple linear regression model (MRL) and the traditional mathematical model is calculated to be 87.15% and 85.03%, which is lower than that of the MWOA-ELM model. This indicates that the ML model that is optimized using the improved whale optimization algorithm has better predictive ability than traditional models, providing a new option for predicting the DCI of RC.

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Section: Discussionsupporting

confidence: 68%

Section: Discussionmentioning

confidence: 99%

Section: Database Description and Analysis Of Variablesmentioning

confidence: 99%

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Chloride Permeability Coefficient Prediction of Rubber Concrete Based on the Improved Machine Learning Technical: Modelling and Performance Evaluation

Huang

Wang

et al. 2023

Polymers

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“…Wang and Du [14] experimentally verified the thermal and sound insulation and noise reduction advantages of the incorporation of CR in concrete mix. Recently, Amiri et al [15] experimentally investigated the influence of the simultaneous replacements of both CR and recycled concrete on the mechanical properties and durability of concrete.…”

Section: Introductionmentioning

confidence: 99%

Eco-Friendly Optimum Structural Concrete Mix Design

et al. 2022

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Concrete, the most consumed man-made material worldwide, has shaped the environment and the modern world. Even though concrete is a major contributor to the carbon footprint, it is indispensable for building the sustainable world of tomorrow. Researchers have been exploring ways to reduce the carbon footprint and to implement strategical waste management plans in which wastes are repurposed. Pollution has been a challenge for almost all countries, especially with the increase in the release of greenhouse gases in the atmosphere and the emissions resulting from wastes in unmanaged landfills. Additionally, the areas available for landfills have become scarce. Daily all around the world, generated are wastes such as wood ash, waste glass, used tires, construction debris, and demolition wastes. These wastes usually accumulate in landfills for years, as they are mostly nondecomposable. This research explores a solution to this twofold problem in which concrete components are replaced by wastes and by-products, which in return reduces the need for raw materials that have a significant carbon footprint and repurposes wastes as part of a circular economy. In this research, wood ash is used as a partial replacement of cement and sand, fine crushed glass and crumb rubber as partial replacements of sand, and crushed glass and recycled concrete aggregates as partial replacements of gravel. The optimum eco-friendly structural concrete mix was determined to be the combined mix consisting of 5% wood ash as a partial replacement of cement; 20% wood ash, 20% fine crushed glass, and 2% crumb rubber as partial replacements of sand; and 5% crushed glass and 50% recycled concrete aggregates as partial replacements of coarse aggregates. By mass, the recycled waste materials constituted 32% of the mix, translating into 34% of its volume. Additionally, identified were mixes that may be used for structural applications.

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“…More than 20 billion car tires are scrapped worldwide annually and the most common disposal approach for end-oflife tires is to bury them [1]. Such methods not only pollute the environment but also endanger human health.…”

Section: Introductionmentioning

confidence: 99%

Compressive Toughness Loss Rate and Softening Characteristic Analysis of Pasture Fiber-Rubber Powder Concrete

Wang

Yang

2022

Advances in Materials Science and Engineering

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To develop the utilization of pasture fibers and waste tire rubber powder, the effect of different blending levels of modified pasture fibers (2 kg/m3, 3 kg/m3, and 4 kg/m3) on the loss rate of compressive toughness and softening characteristics of rubber powder concrete was studied. In addition, RapidAir457 system imaging analysis and microscopic electron microscope photo analysis were carried out to analyze the distribution pattern of modified pasture fibers and rubber powder in concrete. The results show that the peak load of concrete is higher than other pasture fiber dosing test groups when 3 kg/m3 is incorporated in 20 mesh and 60 mesh rubber powder. With the increase of pasture fiber content, the compressive toughness loss of MC60 group and MC80 group increased first and then decreased. The softening curve analysis showed that MC-3 was more capable of absorbing damage energy in the strain range of 0.0005–0.0009 than the MC-2 group. According to the RapidAir457 system imaging analysis, the pasture fibers form a web-like organization inside the concrete when the pasture fiber is mixed at 3 kg/m3.

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Evaluating the synergic effect of waste rubber powder and recycled concrete aggregate on mechanical properties and durability of concrete

Cited by 27 publications

References 33 publications

Chloride Permeability Coefficient Prediction of Rubber Concrete Based on the Improved Machine Learning Technical: Modelling and Performance Evaluation

Chloride Permeability Coefficient Prediction of Rubber Concrete Based on the Improved Machine Learning Technical: Modelling and Performance Evaluation

Eco-Friendly Optimum Structural Concrete Mix Design

Compressive Toughness Loss Rate and Softening Characteristic Analysis of Pasture Fiber-Rubber Powder Concrete

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