WITHDRAWN: Modelling compressive strength, flexural strength and chloride ion permeability of high strength concrete incorporating metakaolin and fly ash

Gehlot, Tarun; Sankhla, Suresh Singh; Parihar, Sangeeta

doi:10.1016/j.matpr.2021.01.789

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…In this paper, 367 pieces of experimental datasets associated with the CS and EC of concrete containing cement replacement materials were collected from the open literature [2,6,7,[18][19][20][21][22][23][24][25][26][27][28][29][30][31]. The datasets were collected from a large number of credible and published sources to ensure that the datasets were as accurate and robust as possible.…”

Section: Data Collectionmentioning

confidence: 99%

Machine Learning Application to Eco-Friendly Concrete Design for Decarbonisation

2021

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Cement replacement materials can not only benefit the workability of the concrete but can also improve its compressive strength. Reducing the cement content of concrete can also lower CO2 emissions to mitigate the impact of the construction industry on the environment and improve energy consumption. This paper aims to predict the compressive strength (CS) and embodied carbon (EC) of cement replacement concrete using machine learning (ML) algorithms, i.e., deep neural network (DNN), support vector regression (SVR), gradient boosting regression (GBR), random forest (RF), k-nearest neighbors (kNN), and decision tree regression (DTR). Not only is producing an optimal ML model helpful for predicting accurate results, but it also saves time, energy, and costs, compared to conducting experiments. Firstly, 367 pieces of experimental datasets from the open literature were collected, in which cement was replaced with any of the cementitious materials. Secondly, the datasets were imported into the ML models, whose parameters were tuned by the grid search algorithm (GSA). Then, the prediction performance, the coefficient of determination (R2), the prediction accuracy, and the root mean square error (RMSE) were employed to indicate the prediction ability of the ML models. The results demonstrate that the GBR models perform the best prediction of the CS and EC. The R2 of the GBR models for predicting the CS and EC are 0.946 and 0.999, respectively. Thus, it can be concluded that the GBR models have promising abilities for design assistance in cement replacement concrete. Finally, a sensitivity analysis (SA) was conducted in this paper to analyse the effects of the inputs on the CS and EC of the cement replacement concrete. Pulverised fuel ash (PFA), blast-furnace slag (GGBS), Expanded perlite (EP), and Silica fume (SF) were noticed to affect the CS and EC of cement replacement concrete significantly.

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Section: Data Collectionmentioning

confidence: 99%

Machine Learning Application to Eco-Friendly Concrete Design for Decarbonisation

2021

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“…Moreover, numerous experimental studies have been conducted to determine the BFS or Fly Ash substitution proportion in the concrete mixture. The strength of concrete has been studied by Gehlot [14] in which samples made with a combination of BFS or F. Ash at six various BFS/ Fly Ash wt % ratios (i.e., 0/0, 10/20, 20/10, 30/0, and 0/30). It was observed that the more the BFS to Fly Ash wt %, the better the CS of the concrete.…”

Section: Introductionmentioning

confidence: 99%

Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches

Shah¹,

Azab

ElDin

et al. 2022

Buildings

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The utilization of waste industrial materials such as Blast Furnace Slag (BFS) and Fly Ash (F. Ash) will provide an effective alternative strategy for producing eco-friendly and sustainable concrete production. However, testing is a time-consuming process, and the use of soft machine learning (ML) techniques to predict concrete strength can help speed up the procedure. In this study, artificial neural networks (ANNs) and decision trees (DTs) were used for predicting the compressive strength of the concrete. A total of 1030 datasets with eight factors (OPC, F. Ash, BFS, water, days, SP, FA, and CA) were used as input variables for the prediction of concrete compressive strength (response) with the help of training and testing individual models. The reliability and accuracy of the developed models are evaluated in terms of statistical analysis such as R2, RMSE, MAD and SSE. Both models showed a strong correlation and high accuracy between predicted and actual Compressive Strength (CS) along with the eight factors. The DT model gave a significant relation to the CS with R2 values of 0.943 and 0.836, respectively. Hence, the ANNs and DT models can be utilized to predict and train the compressive strength of high-performance concrete and to achieve long-term sustainability. This study will help in the development of prediction models for composite materials for buildings.

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“…The addition of MK as cementitious material in concrete shows excellent properties against penetration of various aggressive agents [4,5]. The accumulation of MK enhances the strength and durability of concrete [6,7,8]. The SCMs provide a variety of advantages in concrete, including improved ultimate strength, durability, reduced excessive surface cracking, cost savings, and greater sustainability.…”

Section: Introductionmentioning

confidence: 99%

Evaluating properties of high performance concrete containing metakaolin as cementitious material

Dubey

Deo

Ramtekkar

2022

IRASE

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High performance concrete is extensively used for construction works in recent era. For the preparation of high performance concrete (HPC) mineral and chemical admixtures are used. The addition of mineral admixtures minimizes the utilization of cement and makes concrete more sustainable. The addition of metakaolin as a substitute to cement enhances the properties of concrete. There is need to study the mechanical and micro-structural properties of concrete containing metakaolin as cementitious material. In this work an endeavour has been made to study the properties of HPC employing matakaolin as an alternative for cement. The cement has been replaced with metakaolin by 5%, 10%, 15%, 20%, and 25% respectively for 0.25, 0.3, and 0.35 w/c ratios. The strength and electrical resistivity tests are conducted for all concrete mixes on triplicate. Results confirm that the accumulation of metakaolin increases the properties of HPC. A maximum of 49% increase in compressive strength in concrete was observed by the accumulation of 15% of metakaolin in concrete as substitute to cement for 0.25 w/c ratio in comparison to standard concrete. The development of secondary calcium silicate hydrates and minimal Ca(OH)2 components was revealed by X-ray spectroscopy, indicating that the concrete was denser. The results of this study revealed that metakaolin has a considerable impact on high-performance concrete, particularly in terms of compressive and flexural strength.

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WITHDRAWN: Modelling compressive strength, flexural strength and chloride ion permeability of high strength concrete incorporating metakaolin and fly ash

Cited by 5 publications

References 2 publications

Machine Learning Application to Eco-Friendly Concrete Design for Decarbonisation

Machine Learning Application to Eco-Friendly Concrete Design for Decarbonisation

Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches

Evaluating properties of high performance concrete containing metakaolin as cementitious material

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