A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete

Bassi, Akshita; Manchanda, Aditya; Singh, Rameshwar; Patel, Mahesh

doi:10.1007/s11069-023-05998-9

Cited by 14 publications

(1 citation statement)

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“…In recent years, machine-learning techniques have emerged as promising computational approaches for predicting concrete properties and modeling . For resolving especially concrete compressive strength prediction problems, several ML algorithms are used; among them, preferred ones are linear regression (LR), arti cial neural networks (ANN), support vector machine (SVM), and random forest (RF) (Bassi et al, 2023;Farooq et al, 2021). However, several machine learning algorithms are used in material science, such as recycled aggregate concrete , silica fume concrete (Nafees et al, 2021), concrete using blast furnace slag (Boğa et al, 2013;Sarıdemir et al, 2009), or concrete using y ash .…”

Section: Introductionmentioning

confidence: 99%

A comparative study of machine learning methods for assessing the compressive strength of Pozzolanic concrete

Raju,

Rahman,

Hasan

et al. 2023

Preprint

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This study aims to enhance the prediction of compressive strength in pozzolanic concrete by leveraging machine learning techniques. Unlike conventional methods relying on costly lab tests or empirical correlations, machine learning offers a more precise and efficient approach. Three models—artificial neural networks (ANNs), random forest (RF), and gradient boosting regressor (GBR)—are harnessed to develop predictive models. The dataset, comprising 482 samples, is divided randomly into 70% (337 samples) for training and 30% (145 samples) for testing. Seven input parameters related to pozzolanic material type, proportion, and mix design are utilized for model training. Model performance assessment employs metrics such as coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Notably, the RF model outperforms others, achieving the highest R2 of 0.976 in training and 0.964 in testing, with the lowest RMSE (2.84) and MAE (2.05) in training and 7.81 and 5.89, respectively in testing, demonstrating superior predictive precision. The model’s accuracy is evaluated using the Taylor diagram. Additionally, sensitivity analysis reveals cement as the most impactful input parameter, influencing 28% of variability. The RF model’s robustness is confirmed through K-fold cross-validation, yielding an average R2 of 0.959. This study underscores the reliability and effectiveness of the RF model for forecasting pozzolanic concrete’s compressive strength, carrying implications for optimizing concrete mix and construction practices. Overall, the proposed RF model excels in efficiency and accuracy, establishing its supremacy over other algorithms in predicting pozzolanic concrete properties.

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