Data‐driven approach for investigating and predicting of compressive strength of fly ash–slag geopolymer concrete

Tran, Van Quan

doi:10.1002/suco.202300298

Cited by 5 publications

(1 citation statement)

References 101 publications

(152 reference statements)

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“…Alongside ANNs, convolutional neural networks (CNNs) [17] and deep neural networks (DNNs) [18] are also utilized. Additionally, tree-based algorithms including decision tree (DT) [19], random forest (RF) [20], LightGBM [20], and MP5-tree [21] are employed, showing advantages in model interpretation and handling skewed data. Similarly, recently emerged algorithms known as gene expression programming (GEP) [22] and its variants exhibit superior model interpretation capabilities compared to neural network models.…”

Section: Machine Learning Models For Predictionmentioning

confidence: 99%

Effect of Data Augmentation Using Deep Learning on Predictive Models for Geopolymer Compressive Strength

Nguyen,

Pham,

Ahn

2024

Applied Sciences

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In recent years, machine learning models have become a potential approach in accurately predicting the concrete compressive strength, which is essential for the real-world application of geopolymer concrete. However, the precursor system of geopolymer concrete is known to be more heterogeneous compared to Ordinary Portland Cement (OPC) concrete, adversely affecting the data generated and the performance of the models. To its advantage, data enrichment through deep learning can effectively enhance the performance of prediction models. Therefore, this study investigates the capability of tabular generative adversarial networks (TGANs) to generate data on mixtures and compressive strength of geopolymer concrete. It assesses the impact of using synthetic data with various models, including tree-based, support vector machines, and neural networks. For this purpose, 930 instances with 11 variables were collected from the open literature. In particular, 10 variables including content of fly ash, slag, sodium silicate, sodium hydroxide, superplasticizer, fine aggregate, coarse aggregate, added water, curing temperature, and specimen age are considered as inputs, while compressive strength is the output of the models. A TGAN was employed to generate an additional 1000 data points based on the original dataset for training new predictive models. These models were evaluated on real data test sets and compared with models trained on the original data. The results indicate that the developed models significantly improve performance, particularly neural networks, followed by tree-based models and support vector machines. Moreover, data characteristics greatly influence model performance, both before and after data augmentation.

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Section: Machine Learning Models For Predictionmentioning

confidence: 99%

Effect of Data Augmentation Using Deep Learning on Predictive Models for Geopolymer Compressive Strength

Nguyen,

Pham,

Ahn

2024

Applied Sciences

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Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete

Esmaeili‐Falak,

Sarkhani Benemaran

2024

Structural Concrete

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Concrete constructed using recycled aggregates in place of natural aggregates is an efficient approach to increase the construction sector's sustainability. To improve recycled aggregate concrete () technologies in permafrost, it is essential to certify the stability in frost‐induced conditions. The main goal of this study was to use support vector regression () methods to forecast the frost durability () of on the basis of durability agent value in cold climates. Herein, three optimization methods called Ant lion optimization (), Grey wolf optimization (), and Henry Gas Solubility Optimization () were employed for indicating optimal values of key parameters. The results depicted that all developed models have capability in predicting the of in cold regions. The values of as a comprehensive index depicted that the model has the higher value at 0.0571 as the weakest model, then at 0.0312 recognized as the second‐highest model, and finally the system at 0.0224 mentioned as outperformed model. and approaches were likewise capable of accurately forecasting the of in cold regions, but the created method outperformed them all when taking into account the explanations and justifications.

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Interpretable machine learning model for evaluating mechanical properties of concrete made with recycled concrete aggregate

Nguyen,

Phan,

Nguyen

et al. 2023

Structural Concrete

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The main objective of this paper is to use the data‐driven approach to predict and evaluate the mechanical properties of concrete made with recycled concrete aggregate (RCA), including compressive strength and elastic modulus. Using 358 data samples, including 10 input variables, 10 popular machine learning (ML) algorithms are introduced to select the best ML performance model for predicting RCA concrete's compressive strength and elastic modulus. Gradient Boosting and Categorial Boosting have the best performance in predicting the compressive strength of RCA concrete, with R2 = 0.9112, RMSE = 5.3464 MPa, MAE = 4.0845 MPa, and R2 = 0.9175, RMSE = 5.1520 MPa, MAE = 3.7567 MPa, respectively. Light Gradient Boosting and Categorial Boosting have the best performance in predicting the elastic modulus of RCA concrete, with R2 = 0.8775, RMSE = 2.3560 GPa, MAE = 1.8330 GPa, and R2 = 0.9300, RMSE = 2.3560 MPa, MAE = 1.2589 MPa, respectively. Based on the Shapley Additive Explanation analysis, the influence of main factors on compressive strength and elastic modulus of RCA concrete values has been analyzed qualitatively and quantitatively. RCA replacement level and cement/sand ratio slightly affect compressive strength but have a dominant influence on the elastic modulus of RCA concrete.

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Data‐driven approach for investigating and predicting of compressive strength of fly ash–slag geopolymer concrete

Cited by 5 publications

References 101 publications

Effect of Data Augmentation Using Deep Learning on Predictive Models for Geopolymer Compressive Strength

Effect of Data Augmentation Using Deep Learning on Predictive Models for Geopolymer Compressive Strength

Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete

Interpretable machine learning model for evaluating mechanical properties of concrete made with recycled concrete aggregate

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