Experimental research on regression coefficients in recycled concrete Bolomey formula

Zhang, Xuebing; Shouchang, Deng; Deng, Xiaomin; Qin, Yawei

doi:10.1007/s11771-007-0272-0

Cited by 6 publications

(4 citation statements)

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“…On the other hand, in the United States of America, Bolomey, Fuller, and the 0.45 power gradation chart methods are the most popular. Most of these methods are derived from the so-called “Three Equations Method”, a merged experimental–analytical approach [ 11 , 25 , 26 ]. The experimental–analytical approach means that we need to calculate the volume of ingredients needed by an analytical method and validate it using destructive laboratory tests.…”

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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Section: Concrete MIX Design and Machine Learningmentioning

confidence: 99%

Model-Based Adaptive Machine Learning Approach in Concrete Mix Design

2021

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“…These methods are the Bukowski method, the Eyman and Klaus method, and the Paszkowski method. All the solutions mentioned above are derived from the so-called “Three Equations Method”, or Bolomey Method, which is a mixed experimental-analytical procedure [10,23,24]. It means that collected laboratory data should confirm that mathematical approach.…”

Section: The Contemporary Concrete MIX Design and Machine Learningmentioning

confidence: 99%

“…The first equation is the compressive strength equation or Bolomey formula (Equation (1)), which expresses the experimentally determined dependence of the compressive strength of hardened concrete on the grade of cement used, the type of aggregate used, and the water-cement ratio characterising the cement paste [23,24]. In this method, the concrete grade is assumed as input data.…”

Section: The Contemporary Concrete MIX Design and Machine Learningmentioning

confidence: 99%

Machine Learning Techniques in Concrete Mix Design

2019

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Concrete mix design is a complex and multistage process in which we try to find the best composition of ingredients to create good performing concrete. In contemporary literature, as well as in state-of-the-art corporate practice, there are some methods of concrete mix design, from which the most popular are methods derived from The Three Equation Method. One of the most important features of concrete is compressive strength, which determines the concrete class. Predictable compressive strength of concrete is essential for concrete structure utilisation and is the main feature of its safety and durability. Recently, machine learning is gaining significant attention and future predictions for this technology are even more promising. Data mining on large sets of data attracts attention since machine learning algorithms have achieved a level in which they can recognise patterns which are difficult to recognise by human cognitive skills. In our paper, we would like to utilise state-of-the-art achievements in machine learning techniques for concrete mix design. In our research, we prepared an extensive database of concrete recipes with the according destructive laboratory tests, which we used to feed the selected optimal architecture of an artificial neural network. We have translated the architecture of the artificial neural network into a mathematical equation that can be used in practical applications.

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“…It has very important guiding action for the recycled concrete mix design and optimization to clear the main factors and fundamental law influencing the recycled concrete strength [1][2][3]. The substitute ratio of recycled aggregate and water cement ratio are the key factors influencing the strength of the recycled concrete.…”

Section: Introductionmentioning

confidence: 99%

Effect of some Key Factors on the Strength of Recycled Concrete

Zhang

Fang

2012

AMR

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Because there exists a used cement mortar layer outside the recycled aggregate, there are more interfaces inside recycled concrete, which would result in recycled concrete has different properties from ordinary concrete. In this paper, the effect of such key factors for recycled concrete mix ratio as water-cement ratio, cement strength, sand rate, maximum granule diameter and gradation on the properties of concrete with recycled coarse aggregate was studied by experiment, and the some useful suggestion for mix ratio of recycled concrete was provided.

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Experimental research on regression coefficients in recycled concrete Bolomey formula

Cited by 6 publications

References 0 publications

Model-Based Adaptive Machine Learning Approach in Concrete Mix Design

Model-Based Adaptive Machine Learning Approach in Concrete Mix Design

Machine Learning Techniques in Concrete Mix Design

Effect of some Key Factors on the Strength of Recycled Concrete

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