Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms

Sami, Balahaha Hadi Ziyad; Sami, Balahaha Fadi Ziyad; Kumar, Pravendra; Ahmed, Ali Najah; Amieghemen, Goodnews E.; Sherif, Muhammad M.; El‐Shafie, Ahmed

doi:10.1016/j.cscm.2023.e01893

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…Today, the most signi cant component of the industry business is concrete, which has a high variability of material owing to being composed of a variety of different materials (de-Prado-Gil et al, 2022; . Concrete is a crucial construction material due to its well-established mechanical properties, e.g., compressive strength (Diptikanta Rout, Biswas, and Sinha, 2023; Ziyad Sami et al, 2023). The construction industry has witnessed signi cant utilization of cement, with approximately 3.5 billion tons being employed.…”

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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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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Prediction of split tensile strength of recycled aggregate concrete leveraging explainable hybrid XGB with optimization algorithm

Sapkota,

Saini

2024

Multiscale and Multidiscip. Model. Exp. and Des.

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Recycled aggregate-based concrete has been adopted in building construction as it can reduce concrete waste, eventually minimizing the environmental impact. However, using recycled materials can lead to compromised performance of mechanical properties like split tensile strength (STS). Several factors, including density, water absorption, and recycled aggregate proportion, play a vital role in assessments of STS. This study explores the better evaluation of STS using a hybridized machine learning algorithm. Ensemble model XGBoost with ve optimization algorithms, namely Random search (RS), Grid search (GS), Bayesian Optimization (BO), Grey Wolf optimization (GWO), and Particle Swarm Optimization (PSO) are considered for the study. The comparison shows that XGB-PSO performed very well with R 2 of 0.9988 and 0.9602 in the training and testing sets, respectively. The potential performance of GWO is also seen during the assessments. Further, the 10-fold crossvalidation used in this study ensures that the models can predict better without over tting. The model's explainability is done using Shapley Additive Explanations (SHAP) analysis. SHAP-based study reveals that Cement, Water, and size of aggregates (M-RCA) are critical elements and may enhance STS if considered. The best cement range is 300 to 500 kg/m 3 , the M-RCA size is 10 to 20 mm, and the water range is 180 to 200 kg/m 3 . SHAP interaction graphs con rm the result. This study helps engineers and researchers to understand the critical parameters for making informed decisions, thus promoting sustainable construction practices.

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Efficient compressive strength prediction of concrete incorporating industrial wastes using deep neural network

2023

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Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms

Cited by 5 publications

References 24 publications

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

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

Prediction of split tensile strength of recycled aggregate concrete leveraging explainable hybrid XGB with optimization algorithm

Efficient compressive strength prediction of concrete incorporating industrial wastes using deep neural network

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