The Durability of Concrete Modified by Waste Limestone Powder in the Chemically Aggressive Environment

Micro-pore structure has a decisive effect on the physical and mechanical properties of porous materials. To further improve the composition of rock-like materials, the internal relationship between microscopic characteristics (porosity, pore size distribution) and macroscopic mechanical properties of materials needs to be studied. This study selects portland cement, quartz sand, silica fume, and water-reducing agent as raw materials to simulate sandstone. Based on the Nuclear magnetic resonance (NMR) theory and fractal theory, the study explores the internal relationship between pore structure and mechanical properties of sandstone-like materials, building a compressive strength prediction model by adopting the proportion of macropores and the dimension of macropore pore size as dependent variables. Test results show that internal pores of the material are mainly macropores, and micropores account for the least. The aperture fractal dimension, the correlation coefficient of mesopores and macropores are quite different from those of micropores. Fractal characteristics of mesopores and macropores are obvious. The macropore pore volume ratio has a good linear correlation with fractal dimension and strength, and it has a higher correlation coefficient with pore volume ratio, pore fractal dimension and other variable factors. The compressive strength increases with the growth of pore size fractal dimension, but decreases with the growth of macropore pore volume ratio. The strength prediction model has a high correlation coefficient, credibility and prediction accuracy, and the predicted strength is basically close to the measured strength.

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“…If MAPE is smaller, it means that the model fitting effect is better and the model prediction accuracy is higher. Kepniak [48,49]…”

Section: Establishment Of Strength Prediction Modelmentioning

confidence: 99%

Research on Strength Prediction Model of Sand-like Material Based on Nuclear Magnetic Resonance and Fractal Theory

Deng

Tian

et al. 2020

Applied Sciences

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“…The relative increase in compressive strength is greater for the waste-modified concretes. The above-shown analysis also justifies the beneficial effect of the addition of applied waste mineral powder on the strength and chemical resistance of the tested concretes [ 21 ].…”

Section: Resultsmentioning

confidence: 79%

“…The focus of the research presented in this paper is to investigate the effect of waste limestone powder generated during the preparation of aggregate for hot mixture asphalt (HMA) mixtures. There are only a few publications which shows the effect of using waste limestone powder on durability and strength [ 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Transition Zone Enhancement with Waste Limestone Powder as a Reason for Concrete Compressive Strength Increase

2021

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Modification of concrete with waste materials is an increasingly common process, and they are primarily used as a partial substitution for cement. In the case of inert or nearly inert additions according to EN 206, the effectiveness of such a modification mainly concerns ecological aspects and, only to a small extent, mechanical properties. This article analyses the effect of modifying cement concrete with waste limestone powder as a partial substitution for fine aggregate. The analysed waste arises as a result of the accumulation of dust produced during the initial preparation of aggregate for the production of hot mix asphalt (HMA). In order to analyse the effect of waste on compressive strength, an experimental design was prepared with variable substitution levels and variable water/cement ratios. Compressive strength tests were performed after 28 to 90 days. Statistical analysis of the results was performed. Microscopic evaluation of the fractures of the samples was carried out to clarify the mechanism of transition zone enhancement, which resulted in an increase of compressive strength of the composite.

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“…Several features characterize concrete performance, such as plasticity, durability, compressive strength, and modulus of elasticity. The properties mentioned above have different priorities at different times; for example, adequate compressive strength is essential from the point of view of designed ultimate limit state, while adequate durability is essential in an aggressive environment [ 19 , 20 , 21 , 22 ]. Designing a mix with the improper specification can have many serious consequences; therefore, concrete mix producers fearing failure to meet the appropriate criteria often deliberately augment its parameters beyond designed values.…”

Section: Concrete MIX Design and Machine Learningmentioning

confidence: 99%

Model-Based Adaptive Machine Learning Approach in Concrete Mix Design

2021

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Concrete mix design is one of the most critical issues in concrete technology. This process aims to create a concrete mix which helps deliver concrete with desired features and quality. Contemporary requirements for concrete concern not only its structural properties, but also increasingly its production process and environmental friendliness, forcing concrete producers to use both chemically and technologically complex concrete mixtures. The concrete mix design methods currently used in engineering practice are joint analytical and laboratory procedures derived from the Three Equation Method and do not perform well enough for the needs of modern concrete technology. This often causes difficulties in predicting the final properties of the designed mix and leads to precautionary oversizing of concrete properties for fear of not providing the required parameters. A new approach that would make it possible to predict the newly designed concrete mix properties is highly desirable. The answer to this challenge can be methods based on machine learning, which have been intensively developed in recent years, especially in predicting concrete compressive strength. Machine learning-based methods have been more or less successful in predicting concrete compressive strength, but they do not reflect well the variability that characterises the currently used concrete mixes. A new adaptive solution that allows estimating concrete compressive strength on the basis of the concrete mix main ingredient composition by including two observations for a given batch of concrete is proposed herein. In presented study, a machine learning model was built with a deep neural network architecture, trained on an extensive database of concrete recipes, and translated into a mathematical formula. Testing on four concrete mix recipes was performed, which were calculated according to contemporary design methods (Bolomey and Fuller method), and a comparative analysis was conducted. It was found out that the new algorithm performs significantly better than that without adaptive features trained on the same dataset. The presented algorithm can be used as a concrete strength checking tool for the concrete mix design process.

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The Durability of Concrete Modified by Waste Limestone Powder in the Chemically Aggressive Environment

Cited by 24 publications

References 31 publications

Research on Strength Prediction Model of Sand-like Material Based on Nuclear Magnetic Resonance and Fractal Theory

Research on Strength Prediction Model of Sand-like Material Based on Nuclear Magnetic Resonance and Fractal Theory

Transition Zone Enhancement with Waste Limestone Powder as a Reason for Concrete Compressive Strength Increase

Model-Based Adaptive Machine Learning Approach in Concrete Mix Design

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