Prediction of the Compressive Strength of Concrete Admixed with Metakaolin Using Gene Expression Programming

Akin, Oluwatobi O.; Ocholi, Amana; Abejide, Olugbenga Samuel; Obari, Johnson A.

doi:10.1155/2020/8883412

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…The database of FA concrete was collected from Shah et al (2021b) while the databases of MK concrete were gathered from different research papers (Akin et al, 2020;Dinakar, Sahoo, & Sriram, 2013;R. Ferreira, Castro-Gomes, Costa, & Malheiro, 2016;Güneyisi, Gesoğlu, & Mermerdaş, 2008;Joshaghani, Moeini, & Balapour, 2017;Khatib, 2008;Lenka & Panda, 2017;Madandoust & Mousavi, 2012;Meddah, Ismail, El-Gamal, & Fitriani, 2018;Chi-Sun Poon, Kou, & Lam, 2006;Ramezanianpour & Jovein, 2012) and shown in Table 1 in the supplementary document.…”

Section: Data Collectionmentioning

confidence: 99%

“…The developed model has adequate accuracy, however, complex nonlinear equations generated by GEP may be difficult to use for practical purposes. Similarly, Akin et al (Akin, Ocholi, Abejide, & Obari, 2020) also employed GEP for the estimation of CS of concrete with MK. In addition to construction materials, machine learning techniques have been in other civil engineering sub-field such as they were used for seismic vulnerability assessment of existing reinforced concrete buildings (Kumari et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Predicting compressive strength of concrete with fly ash, metakaolin and silica fume by using machine learning techniques

Majd

2022

Lat. Am. j. solids struct.

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The compressive strength (CS) is the most important parameter in the design codes of reinforced concrete structures. The development of simple mathematical equations for the prediction of CS of concrete can have many practical advantages such as it save cost and time in experiments needed for suitable design data. Due to environmental concerns with the production of cement, different supplementary cementitious materials are often used as partial replacements for cement such as fly ash (FA), metakaolin (MK), and silica fume (SF). However, little work has been done for developing simple mathematical equations for the prediction of CS with FA, MK and SF by using the M5P algorithm. Moreover, the M5P algorithm is not compared with other modelling techniques such as linear regression analysis, gene expression programming (GEP) and response surface methodology. It is established that, for concrete with FA and SF, M5P showed superior prediction capability as compared with other modelling techniques, however, GEP gave the best performance for concrete with MK: CS decrease by increasing FA content, while it increases by increasing MK and SF content.

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Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting compressive strength of concrete with fly ash, metakaolin and silica fume by using machine learning techniques

Majd

2022

Lat. Am. j. solids struct.

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“…Our aim was to collect a large database and, therefore, all test results of mechanical properties of concrete with MK conducted based on international standards were collected for use in this study. This database was collected from 45 published studies [ 4 , 15 , 16 , 17 , 18 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 ] and is shown in the supplementary document . In the supplementary document, Tables S1–S3 contain the information of input parameters and output for

…”

Section: Data Collectionmentioning

confidence: 99%

“…Table S3: Experimental database of FS of concrete with metakaolin. References [ 4 , 15 , 16 , 17 , 18 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 72 , 73 ] are cited in the supplementary materials.…”

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confidence: 99%

Application of Machine Learning Techniques for Predicting Compressive, Splitting Tensile, and Flexural Strengths of Concrete with Metakaolin

et al. 2022

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The mechanical properties of concrete are the important parameters in a design code. The amount of laboratory trial batches and experiments required to produce useful design data can be decreased by using robust prediction models for the mechanical properties of concrete, which can save time and money. Portland cement is frequently substituted with metakaolin (MK) because of its technical and environmental advantages. In this study, three mechanical properties of concrete with MK, i.e., compressive strength (f′c), splitting tensile strength (fst), and flexural strength (FS) were modelled by using four machine learning (ML) techniques: gene expression programming (GEP), artificial neural network (ANN), M5P model tree algorithm, and random forest (RF). For this purpose, a comprehensive database containing detail of concrete mixture proportions and values of f′c, fst, and FS at different ages was gathered from peer-reviewed published documents. Various statistical metrics were used to compare the predictive and generalization capability of the ML techniques. The comparative study of ML techniques revealed that RF has better predictive and generalization capability as compared with GEP, ANN, and M5P model tree algorithm. Moreover, the sensitivity and parametric analysis (PA) was carried out. The PA showed that the most suitable proportions of MK as partial cement replacement were 10% for FS and 15% for both f′c and fst.

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“…Asteris et al [9] analyzed the strength using multivariate adaptive regression splines and an M5P model tree. Oluwatobi et al [10] analyzed strength development based on the method of gene expression programming. Ayobami [11] evaluated the strength of self-compacting metakaolin composite concrete using response surface analysis.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Methods to Produce Greener Metakaolin Composite Concrete

et al. 2021

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Metakaolin is reactive and is widely used in the modern concrete industry. This study presents an integrated strength–sustainability evaluation framework, which we employed in the context of metakaolin content in concrete. First, a composite hydration model was employed to calculate reactivity of metakaolin and cement. Furthermore, a hydration-based linear equation was designed to evaluate the compressive strength development of metakaolin composite concrete. The coefficients of the strength evaluation model are constants for different mixtures and ages. Second, the sustainability factors—CO2 emissions, resource consumption, and energy consumption—were determined based on concrete mixtures. Moreover, the sustainability factors normalized for unit strength were obtained based on the ratios of total CO2 emissions, energy consumption, and resource consumption to concrete strength. The results of our analysis showed the following: (1) As the metakaolin content increased, the normalized CO2 emissions and resource consumption decreased, and the normalized energy first decreased and then slightly increased. (2) As the concrete aged from 28 days to three months, the normalized CO2 emissions, resource consumption, and energy consumption decreased. (3) As the water/binder ratio decreased, the normalized CO2 emissions, resource consumption, and energy consumption decreased. Summarily, the proposed integrated strength–sustainability evaluation framework is useful for finding greener metakaolin composite concrete.

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Prediction of the Compressive Strength of Concrete Admixed with Metakaolin Using Gene Expression Programming

Cited by 8 publications

References 14 publications

Predicting compressive strength of concrete with fly ash, metakaolin and silica fume by using machine learning techniques

Predicting compressive strength of concrete with fly ash, metakaolin and silica fume by using machine learning techniques

Application of Machine Learning Techniques for Predicting Compressive, Splitting Tensile, and Flexural Strengths of Concrete with Metakaolin

Model-Based Methods to Produce Greener Metakaolin Composite Concrete

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