Mechanical properties, permeability and microstructure of concrete using construction and industrial waste

Sharma, Rachit

doi:10.1108/ijsi-07-2022-0101

Cited by 6 publications

(3 citation statements)

References 67 publications

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“…Surprisingly, no long-term decline in resistance has ever been observed. In actuality, the compressive strength of the concrete sample improves with age and increases with the length of time it has been in existence [32]. In this investigation, there was no proof that older concrete containing superplasticizers lost any strength.…”

Section: Discussionmentioning

confidence: 62%

Machine Learning Modelling for Compressive Strength Prediction of Superplasticizer-Based Concrete

et al. 2023

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Superplasticizers (SPs), also known as naturally high-water reducers, are substances used to create high-strength concrete. Due to the system’s complexity, predicting concrete’s compressive strength can be difficult. In this study, a prediction model for the compressive strength with SP was developed to handle the high-dimensional complex non-linear relationship between the mixing design of SP and the compressive strength of concrete. After performing a statistical analysis of the dataset, a correlation analysis was performed and then 16 supervised machine learning regression techniques were used. Finally, by using the Extra Trees method and creating the SP variable values, it was shown that the compressive strength values of concrete increased with the addition of SP in the optimal dose. The results indicate that superplasticizers can often reduce the water content of concrete by 25 to 35 per cent and consequently resistivity increased by 50 to 75 per cent and the optimum amount of superplasticizers was up to 12 kg per cubic meter as well. From one point, the increase in superplasticizers does not lead to a rise in the concrete compressive strength, and it remains constant. According to the findings, SP additive has the most impact on concrete’s compressive strength after cement. Given the scant information now available on concrete-including superplasticizer, it is prudent to design a concrete mixing plan for future studies. It is also conceivable to investigate how concrete’s compressive strength is impacted by water reduction.

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Section: Discussionmentioning

confidence: 62%

Machine Learning Modelling for Compressive Strength Prediction of Superplasticizer-Based Concrete

et al. 2023

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“…Sharma [4] indicated that waste could be classified either as unavoidable (or natural waste) or avoidable. If the cost of waste is higher than the cost to prevent it, it is classified as avoidable waste.…”

Section: Introductionmentioning

confidence: 99%

“…If the cost of waste is higher than the cost to prevent it, it is classified as avoidable waste. If the necessary investment for its reduction is higher than the economy produced, it is classified as unavoidable waste [4]. Wu et al [5] grouped waste from the construction field into inert, non-inert, and hazardous categories.…”

Section: Introductionmentioning

confidence: 99%

Competencies Driving Waste Minimization during the Construction Phase of Buildings

et al. 2023

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It is thought that there is a low level of awareness of key competencies that drive material waste reduction at the construction stage of a project which has led to the low impact of waste minimization in the construction industry. This study, therefore, explores the key competencies for driving waste minimization at the construction stage of projects. Twenty-four (24) key factors that drive CW minimization at the construction stage of projects were derived from a thorough review of existing literature. The research adopted the quantitative approach whereby a questionnaire survey involving 53 construction professionals was conducted. Factor analysis was the statistical tool employed for the analysis of the data gathered. The findings of the study revealed four major competencies that drive CW minimization: Awareness Competency (AC), Managerial Competency (MC), Personnel Ability Competency (PAC), and Communication Competency (CC). The implementation of the key competencies from the findings of the study is highly recommended for the achievement of low waste in building projects. In view of this, it is recommended that government should enact legislative and fiscal measures to instill waste management habits in the construction industry. Several studies have investigated strategies or measures for CW minimization. However, this study focused on the knowledge and skills of construction stakeholders as the main component of waste minimization competencies.

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Mechanical characterization of the concrete mixture, including steel slag aggregate and steel fibres

Bijalwan

Senthil

2023

Materials Today: Proceedings

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Mechanical properties, permeability and microstructure of concrete using construction and industrial waste

Cited by 6 publications

References 67 publications

Machine Learning Modelling for Compressive Strength Prediction of Superplasticizer-Based Concrete

Machine Learning Modelling for Compressive Strength Prediction of Superplasticizer-Based Concrete

Competencies Driving Waste Minimization during the Construction Phase of Buildings

Mechanical characterization of the concrete mixture, including steel slag aggregate and steel fibres

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