Eco-friendly High-Strength Concrete Engineered by Micro Crumb Rubber from Recycled Tires and Plastics for Railway Components

Kaewunruen, Sakdirat; Meesit, Ratthaphong

doi:10.1520/acem20180058

Cited by 10 publications

(6 citation statements)

References 57 publications

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“…Mountains, the principal supply of high-quality aggregates, rapidly diminish [ 17 , 18 ]. As a result, numerous nations worldwide have experienced natural disasters [ 19 ]. On the other side, many structures are demolished every year to earthquakes or because they have outlived their usefulness [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

de‐Prado‐Gil

Palencia

Jagadesh³

et al. 2022

Materials

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A considerable amount of discarded building materials are produced each year worldwide, resulting in ecosystem degradation. Self-compacting concrete (SCC) has 60–70% coarse and fine particles in its composition, so replacing this material with another waste material, such as recycled aggregate (RA), reduces the cost of SCC. This study compares novel Artificial Neural Network algorithm techniques—Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB)—to estimate the 28-day compressive strength (f’c) of SCC with RA. A total of 515 samples were collected from various published papers, randomly splitting into training, validation, and testing with percentages of 70, 10 and 20. Two statistical indicators, correlation coefficient (R) and mean squared error (MSE), were used to assess the models; the greater the R and lower the MSE, the more accurate the algorithm. The findings demonstrate the higher accuracy of the three models. The best result is achieved by BR (R = 0.91 and MSE = 43.755), while the accuracy of LM is nearly the same (R = 0.90 and MSE = 48.14). LM processes the network in a much shorter time than BR. As a result, LM and BR are the best models in forecasting the 28 days f’c of SCC having RA. The sensitivity analysis showed that cement (28.39%) and water (23.47%) are the most critical variables for predicting the 28-day compressive strength of SCC with RA, while coarse aggregate contributes the least (9.23%).

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Section: Introductionmentioning

confidence: 99%

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

de‐Prado‐Gil

Palencia

Jagadesh³

et al. 2022

Materials

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“…The primary source of well-quality aggregates, i.e., mountains, are being depleted at an alarming rate [24][25][26]. Because of this, natural catastrophes have struck many countries worldwide [27][28][29]. On the other hand, many buildings are demolished yearly due to earthquakes or after completing their service life [19,27,30].…”

Section: Introductionmentioning

confidence: 99%

“…Because of this, natural catastrophes have struck many countries worldwide [27][28][29]. On the other hand, many buildings are demolished yearly due to earthquakes or after completing their service life [19,27,30]. Therefore, a considerable amount of construction waste is generated annually.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods

et al. 2022

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The composition of self-compacting concrete (SCC) contains 60–70% coarse and fine aggregates, which are replaced by construction waste, such as recycled aggregates (RA). However, the complexity of its structure requires a time-consuming mixed design. Currently, many researchers are studying the prediction of concrete properties using soft computing techniques, which will eventually reduce environmental degradation and other material waste. There have been very limited and contradicting studies regarding prediction using different ANN algorithms. This paper aimed to predict the 28-day splitting tensile strength of SCC with RA using the artificial neural network technique by comparing the following algorithms: Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB). There have been very limited and contradicting studies regarding prediction by using and comparing different ANN algorithms, so a total of 381 samples were collected from various published journals. The input variables were cement, admixture, water, fine and coarse aggregates, and superplasticizer; the data were randomly divided into three sets—training (60%), validation (10%), and testing (30%)—with 10 neurons in the hidden layer. The models were evaluated by the mean squared error (MSE) and correlation coefficient (R). The results indicated that all three models have optimal accuracy; still, BR gave the best performance (R = 0.91 and MSE = 0.2087) compared with LM and SCG. BR was the best model for predicting TS at 28 days for SCC with RA. The sensitivity analysis indicated that cement (30.07%) was the variable that contributed the most to the prediction of TS at 28 days for SCC with RA, and water (2.39%) contributed the least.

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“…In future, due to large-scale exploitation without relevant protection measures, natural aggregates will inevitably face shortages. On the other hand, with the arrival of the useful life of the cement structure, a large number of buildings will be demolished, and a large amount of waste concrete will inevitably be produced [8,[13][14][15]. A large amount of construction waste is generated every year around the world.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, in recent years, natural disasters have occurred frequently in various countries around the world, and the resulting construction waste also needs to face practical problems such as how to be properly recycled, processed and reused [14,16]. Recycled concrete technology is one of the main measures to realise the sustainable development of building resources and environment.…”

Section: Introductionmentioning

confidence: 99%

Recycled Aggregates Concrete Compressive Strength Prediction Using Artificial Neural Networks (ANNs)

Ngamkhanong

et al. 2021

Infrastructures

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The recycled aggregate is an alternative with great potential to replace the conventional concrete alongside with other benefits such as minimising the usage of natural resources in exploitation to produce new conventional concrete. Eventually, this will lead to reducing the construction waste, carbon footprints and energy consumption. This paper aims to study the recycled aggregate concrete compressive strength using Artificial Neural Network (ANN) which has been proven to be a powerful tool for use in predicting the mechanical properties of concrete. Three different ANN models where 1 hidden layer with 50 number of neurons, 2 hidden layers with (50 10) number of neurons and 2 hidden layers (modified activation function) with (60 3) number of neurons are constructed with the aid of Levenberg-Marquardt (LM) algorithm, trained and tested using 1030 datasets collected from related literature. The 8 input parameters such as cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age are used in training the ANN models. The number of hidden layers, number of neurons and type of algorithm affect the prediction accuracy. The predicted recycled aggregates compressive strength shows the compositions of the admixtures such as binders, water–cement ratio and blast furnace–fly ash ratio greatly affect the recycled aggregates mechanical properties. The results show that the compressive strength prediction of the recycled aggregate concrete is predictable with a very high accuracy using the proposed ANN-based model. The proposed ANN-based model can be used further for optimising the proportion of waste material and other ingredients for different targets of concrete compressive strength.

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Eco-friendly High-Strength Concrete Engineered by Micro Crumb Rubber from Recycled Tires and Plastics for Railway Components

Cited by 10 publications

References 57 publications

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods

Recycled Aggregates Concrete Compressive Strength Prediction Using Artificial Neural Networks (ANNs)

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