Prediction of Compressive Strength in Self-compacting Concrete Containing Fly Ash and Silica Fume Using ANN and SVM

Abunassar, Nouraldin; Alas, Mustafa; Ali‬, Shaban Ismael Albrka

doi:10.1007/s13369-022-07359-3

Cited by 18 publications

(7 citation statements)

References 41 publications

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“…One way to overcome this is to use self-compacting concrete known as self-compacting concrete (SCC). SCC is a concrete that can flow to fill the formwork and tight reinforcement gaps without segregation and the concrete remains homogeneous [2,3].…”

Section: Introductionmentioning

confidence: 99%

Effect of Rice Husk Ash and Steel Fibers on Self-Compacting Concrete Properties

2023

GEOMATE

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Steel fiber waste is in the form of ductile and tough fibers, making it suitable for increasing the tensile strength and flexural strength of Self-Compacting Concrete (SCC). The use of rice husk ash (RHA) as an additive in SCC concrete is expected to improve the quality of SCC concrete while reducing the use of Portland cement as an adhesive. This study aims to examine the tensile strength and flexural performance of SCC concrete beams with rice husk ash added and steel wire scrap waste. The research was conducted by making a concrete beam specimen to examine the flexural performance, and a concrete cylinder test object to examine the tensile strength and compressive strength of concrete. The specimens were made with 4 variations; the variations used are distinguished based on the percentage of RHA and steel fiber waste. The results showed that the use of RHA in SCC concrete can increase the compressive strength of concrete. It is proven that concrete with RHA added increases compressive strength by 8.45%. The use of steel fiber waste in SCC RHA concrete can increase the tensile strength of concrete by 28.15%, increasing the flexural strength by 9.3%. So, it can be concluded that the addition of RHA and steel fiber waste to the SCC mixture can increase the strength of the SCC.

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Section: Introductionmentioning

confidence: 99%

Effect of Rice Husk Ash and Steel Fibers on Self-Compacting Concrete Properties

2023

GEOMATE

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“…In conclusion, this study adeptly harnesses a diverse array of ML models to predict concrete strength, effectively navigating challenges presented by noisy and sparse datasets, while also incorporating various waste materials. The robustness of our proposed models was further tested on an augmented dataset, seamlessly integrating our original experimental data (223 instances) with additional instances (59) from the literature [31][32][33][34][35][36][37]. Despite the inherent variations in Advances in Civil Engineering experimentation properties, our models exhibited a commendable level of performance.…”

Section: Conclusion and Future Scopementioning

confidence: 99%

Machine Learning Modeling Integrating Experimental Analysis for Predicting Compressive Strength of Concrete Containing Different Industrial Byproducts

Kalabarige,

Sridhar,

Subbaram

et al. 2024

Advances in Civil Engineering

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This study aimed to develop accurate models for estimating the compressive strength (CS) of concrete using a combination of experimental testing and different machine learning (ML) approaches: baseline regression models, boosting model, bagging model, tree-based ensemble models, and average voting regression (VR). The research utilized an extensive experimental dataset with 14 input variables, including cement, limestone powder, fly ash, granulated glass blast furnace slag, silica fume, rice husk ash, marble powder, brick powder, coarse aggregate, fine aggregate, recycled coarse aggregate, water, superplasticizer, and voids in mineral aggregate. To evaluate the performance of each ML model, five metrics were used: mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), coefficient of determination (R2-score), and relative root mean squared error (RRMSE). The comparative analysis revealed that the VR model exhibited the highest effectiveness, displaying a strong correlation between actual and estimated outcomes. The boosting, bagging, and VR models achieved impressive R2-scores in the range of 86.69%–92.43%, with MAE ranging from 3.87 to 4.87, MSE from 21.74 to 38.37, RMSE from 4.66 to 4.87, and RRMSE between 8% and 11%. Particularly, the VR model outperformed all other models with the highest R2-score (92.43%) and the lowest error rate. The developed models demonstrated excellent generalization and prediction capabilities, providing valuable tools for practitioners, researchers, and designers to efficiently evaluate the CS of concrete. By mitigating environmental vulnerabilities and associated impacts, this research can significantly contribute to enhancing the quality and sustainability of concrete construction practices.

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“…These methods, while valuable in some cases, often lack precision and reproducibility, resulting in variations in mix-design outcomes. In recent years, researchers have explored the use of ANNs to optimize the mix design of conventional concrete and provide a more systematic and data-driven approach [20,21]. Recent developments in deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), present opportunities to enhance the modeling capabilities and prediction accuracy for concrete mix-design outcomes [21,22].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Tuff Stones Content in Lightweight Concrete Using Artificial Neural Networks

Yasin,

Awwad,

Malkawi

et al. 2023

Civ Eng J

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Tuff stones are volcanic sedimentary rocks formed by the consolidation of volcanic ash. They possess unique geological properties that make them attractive for a variety of construction and architectural applications. Considerable amounts and various types of Tuff stones exist in the eastern part of Jordan. However, the use of Tuff stones often requires experimental investigations that can significantly impact the accuracy of their physical and mechanical characteristics. To ensure consistent and predictable properties in their mix design, it is essential to minimize the effects of these experimental procedures. Artificial neural networks (ANNs) have emerged as a promising tool to address such challenges, leveraging their ability to analyze complex data and optimize concrete mix design. In this research, ANNs have been used to predict the optimum content of Tuff fine aggregate to produce structural lightweight concrete with a wide range (20 to 50 MPa) of compressive strength. Three different types of Tuff aggregates, namely gray, brown, and yellow Tuff, were experimentally investigated. A set of 68 mixes was produced by varying the fine-tuff aggregate content from 0 to 50%. Concrete cubes were cast and tested for their compressive strength. These samples were then used to form the input dataset and targets for ANN. ANN was created by incorporating the recent advancements in deep learning algorithms, and then it was trained, validated using data collected from the literature, and tested. Both experimental and ANN results showed that the optimum content of the various types of used Tuff fine aggregate ranges between 20 to 25%. The results revealed that there is a clear agreement between the predicted values using ANN and the experimental ones. The use of ANNs may help to cut costs, save time, and expand the applications of Tuff aggregate in lightweight concrete production. Doi: 10.28991/CEJ-2023-09-11-013 Full Text: PDF

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Prediction of Compressive Strength in Self-compacting Concrete Containing Fly Ash and Silica Fume Using ANN and SVM

Cited by 18 publications

References 41 publications

Effect of Rice Husk Ash and Steel Fibers on Self-Compacting Concrete Properties

Effect of Rice Husk Ash and Steel Fibers on Self-Compacting Concrete Properties

Machine Learning Modeling Integrating Experimental Analysis for Predicting Compressive Strength of Concrete Containing Different Industrial Byproducts

Optimization of Tuff Stones Content in Lightweight Concrete Using Artificial Neural Networks

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