Effect of Soorh Metakaolin on Concrete Compressive Strength and Durability

Saand, Abdullah; Keerio, Manthar Ali; Bangwar, D. K.

doi:10.48084/etasr.1494

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…The availability of cementitious materials varies with the locality, e.g. sorah [43] is one of the promising cementitious materials locally available in Pakistan which may be tried in SCC. Thus, it may be concluded that there is always need of understanding the behavior of SCC with materials and fibers obtained from local resources.…”

Section: Discussionmentioning

confidence: 99%

A Review on Self Compacting Concrete with Cementitious Materials and Fibers

Memon

Lakho

et al. 2018

Eng. Technol. Appl. Sci. Res.

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Self-compacted concrete (SCC) is cast in the formwork without compaction and it fulfills the formwork due to its own weight. SCC is considered to have many advantages in comparison with conventional concrete like improved construction quality, faster construction activity, reduced cost etc. SCC is produced with the same ingredients of normal concrete. However, cementitious materials are also adopted to replace the cement content in SCC in order to use waste materials from industries and agricultural products. To further enhance the performance of SCC, different types of fibers are tried in order to produce fiber reinforced SCC. The fibers in the concrete bridge the cracks and diffuse the crack propagation which improves mechanical properties. In developed countries SCC has reasonable acceptance in construction industry but in developing countries like Pakistan has not gained acceptance. This paper is focused on undertaking a review of SCC with cement replacement and fiber reinforcement materials. The main objective of this paper is to compile the literature in order to understand the various properties of SCC in fresh and hardened state when these cement replacement materials and fibers are used.

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Section: Discussionmentioning

confidence: 99%

A Review on Self Compacting Concrete with Cementitious Materials and Fibers

Memon

Lakho

et al. 2018

Eng. Technol. Appl. Sci. Res.

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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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“…In [6], an in-depth study was presented on the use of the concrete diffusion coefficient. The durability properties of fuel ash concrete and metakaolin concrete were studied in [13] and [14] respectively. This study aims to develop a framework for the chloride diffusion in concrete, considering the loading effect using semi-probabilistic and probabilistic analysis.…”

Section: Imentioning

confidence: 99%

A Theoretical Framework for Service Life Prediction of Reinforced Concrete Structures in Chloride Environment using Load Factors

Pathan

Khaskheli

Qureshi

2021

Eng. Technol. Appl. Sci. Res.

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Service life modeling of reinforced concrete structures in a chloride environment is mainly performed without considering the loading effects. Different loading effects can produce different service life results. This study presents a theoretical framework for the modeling of the service life of reinforced concrete structures in a chloride environment using loading factors, showing that, depending on the loading nature (either compression or tension), different diffusion results could be obtained. This paper also highlights various approaches to service life modeling, such as the deterministic, probabilistic, and semi-probabilistic, which consider different ways to estimate the service life of reinforced concrete structures in chloride environments. The importance of various distributions for the input parameters in the chloride ingress modeling was examined. The proposed framework includes a procedure to estimate the probability of concrete failure in chloride environments.

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Effect of Soorh Metakaolin on Concrete Compressive Strength and Durability

Cited by 11 publications

References 18 publications

A Review on Self Compacting Concrete with Cementitious Materials and Fibers

A Review on Self Compacting Concrete with Cementitious Materials and Fibers

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

A Theoretical Framework for Service Life Prediction of Reinforced Concrete Structures in Chloride Environment using Load Factors

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