Prediction of Compressive Strength of High-Volume Fly Ash Concrete Using Artificial Neural Network

Rajeshwari, Ramachandra; Mandal, S.

doi:10.1007/978-981-13-3317-0_42

Cited by 27 publications

(16 citation statements)

References 57 publications

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“…For instance, isothermal calorimetry and rheological measurements have been used as screening tools to assess the early-age behavior of HVFA mixtures and as a means of quality control to evaluate and qualify fly ashes [49]. With the aid of soft computing techniques, the artificial neural network model [50] or support vector machine model [51] can be developed to predict the compressive strength of HVFA mixtures using relevant parameters such as the content and type of fly ash as model inputs. Mohammed et al [52] demonstrated the use of neuro-swarm and neuro-imperialism models for predicting the compressive strength of fly ash concretes, using 9 mix design parameters as model inputs and 379 data points from published literature.…”

Section: Paste Fine Aggregatementioning

confidence: 99%

High‐Volume Fly Ash‐Based Cementitious Composites as Sustainable Materials: An Overview of Recent Advances

Zhao

Shi

2021

Advances in Civil Engineering

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High-volume fly ash (HVFA) cementitious composites (paste, grout, mortar, and concrete) have been widely investigated as a class of sustainable materials due to their lower carbon footprint and often better life cycle performance than conventional Portland cement mixtures. Recent years have seen increased research in HVFA-based materials, and the potential of this type of mixtures in engineering applications has significantly improved. In this context, this work reviews the renewed knowledge of HVFA mixtures, focusing on the relevant papers published over the last decade. The effects of replacing cement with a HVFA binder on the fresh properties, mechanical properties, durability performance, and environmental impact of HVFA cementitious composites are explored. Measures that can compensate for the main drawbacks that limit the wider application of HVFA mixtures are discussed in detail. At last, we summarize the research needs and remaining challenges of HVFA cementitious composites.

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Section: Paste Fine Aggregatementioning

confidence: 99%

High‐Volume Fly Ash‐Based Cementitious Composites as Sustainable Materials: An Overview of Recent Advances

Zhao

Shi

2021

Advances in Civil Engineering

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“…is the Lagrange coefficient to solve the convex optimization problem, and C is the penalty factor. Equation (7) shows the radial basis kernel function used here, and σ is a parameter of the kernel function.…”

Section: ⋅⋅⋅mentioning

confidence: 99%

“…The traditional parameter selection is often obtained through human experience and multiple experiments, which is not only time-consuming but also computationally expensive. A large number of studies [6,7] show that using a swarm intelligence optimization algorithm to select parameters of support vector machines can effectively improve the efficiency of parameter selection. Firefly Algorithm (FA) is a population intelligence algorithm proposed by Yang, a Cambridge scholar, in 2008 [8].…”

Section: Introductionmentioning

confidence: 99%

The Application of Classifier Model Based on FA-SVM Algorithm in Speech Recognition

Su¹,

Jin²,

Zhang³

et al. 2023

J. Phys.: Conf. Ser.

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In recent years, speech recognition technology has been increasingly widely used. How to improve the robustness of speech recognition systems has become the focus of researchers. This paper proposes an accent recognition model based on the Firefly algorithm to optimize the support vector machine. Firstly, a set of penalty factors and kernel parameters in the support vector machine are taken as firefly individuals, and the important parameters affecting the performance of the support vector machine are optimized by the firefly algorithm. A Firefly-Support vector machine (FA-SVM) model is established and applied to accent recognition. The example analysis shows that the FA-SVM accent recognition model has taller classification accuracy and faster running speed than the PSO-SVM and GA-SVM classifier models.

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“…Studies on foamed concrete [14], high-performance concrete [15], and steelmaking slag concrete [16] underscore the effectiveness of ML models, with outcomes shaping future applications. In addition, ANN is employed to predict the CS of concrete by mixing high volumes of fly ash (FA) [17] is tested. Likewise, in the domain of concrete containing industrial waste materials such as ground granulated blast-furnace slag (GGBFS) and FA, evolutionary learning algorithms like practical swarm optimization (PSO) and genetic algorithm (GA) were employed with the support vector regression (SVR) model as the objective function.…”

Section: Introductionmentioning

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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Prediction of Compressive Strength of High-Volume Fly Ash Concrete Using Artificial Neural Network

Cited by 27 publications

References 57 publications

High‐Volume Fly Ash‐Based Cementitious Composites as Sustainable Materials: An Overview of Recent Advances

High‐Volume Fly Ash‐Based Cementitious Composites as Sustainable Materials: An Overview of Recent Advances

The Application of Classifier Model Based on FA-SVM Algorithm in Speech Recognition

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

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