Machine Learning-Based Method for Predicting Compressive Strength of Concrete

Li, Daihong; Tang, Zhili; Kang, Qian; Zhang, Xiaoyu; Li, Youhua

doi:10.3390/pr11020390

Cited by 36 publications

(10 citation statements)

References 63 publications

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“…Our predictive model for 28-days-compressive strength, trained with 19,294 data instances, achieved R 2 = 0.79 and RMSE = 5.1 MPa in a 10-fold-cross-validation. In the study outlined in [27], where 1030 sets of concrete compressive strength were analyzed using 5-fold-cross-validation, a R 2 = 0.9 and a RMSE = 4.8 MPa were obtained. Our predictive model, trained with a significantly larger dataset, achieved similar performance in RMSE but lower in R 2 .…”

Section: Discussionmentioning

confidence: 99%

“…With this in mind, the following performance goals for predictive models can be defined: for compressive strength, an MAE less than 10 MPa; for flexural strength, an MAE less than 1.2 MPa; and for slump estimation, an MAE less than 20 mm. The literature review allowed us to define performance goals, as in the case of compressive strength prediction, where [27] concludes that a model with R 2 = 0.92 has a high level of accuracy. However, we consider that the results of other research are not comparable to ours due to the number of samples used to train and evaluate the models; whereas most studies barely reach a thousand samples, our study contains more than 20,000.…”

Section: Business Understandingmentioning

confidence: 99%

“…The fundamental concept behind this algorithm is that each tree is trained using the errors (residuals) from all the preceding trees. Additionally, the negative gradient of the loss function in the current model is employed as an estimate for these errors in the boosted tree algorithm, which helps adjust the regression or classification tree [27]. • Extreme Gradient Boosting Regressor (XGBoost): It is a technique based on decision trees that takes into account data dispersion for approximate tree learning.…”

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confidence: 99%

See 2 more Smart Citations

Machine-Learning-Based Predictive Models for Compressive Strength, Flexural Strength, and Slump of Concrete

Vargas,

Oviedo,

Ortega

et al. 2024

Applied Sciences

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The process of concrete production involves mixing cement, water, and other materials. The quantity of each of these materials results in a performance that is particularly estimated in terms of compressive or flexural strength. It has been observed that the final performance of concrete has a high variance and that traditional formulation methods do not guarantee consistent results. Consequently, designs tend to be over-designed, generating higher costs than required, to ensure the performance committed to the client. This study proposes the construction of predictive machine learning models to estimate compressive or flexural strength and concrete slump. The study was carried out following the Team Data Science Process (TDSP) methodology, using a dataset generated by the Colombian Ready Mix (RMX) company Cementos Argos S.A. over five years, containing the quantity of materials used for different concrete mixes, as well as performance metrics measured in the laboratory. Predictive models such as XGBoost and neural networks were trained, and hyperparameter tuning was performed using advanced techniques such as genetic algorithms to obtain three models with high performance for estimating compressive strength, flexural strength, and slump. This study concludes that it is possible to use machine learning techniques to design reliable concrete mixes that, when combined with traditional analytical methods, could reduce costs and minimize over-designed concrete mixes.

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Section: Discussionmentioning

confidence: 99%

Section: Business Understandingmentioning

confidence: 99%

mentioning

confidence: 99%

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Machine-Learning-Based Predictive Models for Compressive Strength, Flexural Strength, and Slump of Concrete

Vargas,

Oviedo,

Ortega

et al. 2024

Applied Sciences

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“…As a result of developments in technology over the last several decades [29][30][31], the use of AI has become more widespread across almost all technical fields. Predictions of solar radiation data were made using a variety of artificial intelligence techniques, including support vector regression (SVR), kernel nearest neighbors (k-NNs), deep learning (DL), and ANNs [32,33].…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems

2023

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Solar power is an excellent alternative power source that can significantly cut our dependency on nonrenewable and destructive fossil fuels. Solar radiation (SR) can be predicted with great precision, and it may be possible to drastically minimize the impact cost associated with the development of solar energy. To successfully implement solar power, all projects using solar energy must have access to reliable sun radiation data. However, the deployment, administration, and performance of photovoltaic or thermal systems may be severely impacted by the lack of access to and the ambiguity of this data. Methods for estimating and predicting solar radiation can help solve these problems. Prediction techniques can be put to use in the real world to, for example, keep the power grid functioning smoothly and ensure that the supply of electricity exactly matches the demand at all times. Recently developed forecasting methods include the deep learning convolutional neural networks combined with long short-term memory (CNN-LSTM) model. This study provides a comprehensive examination of meteorological data, along with the CNN-LSTM methods, in order to design and train the most accurate SR forecasting artificial neural network model possible. Weather data was collected from a NASA meteorological station that included details such as the current temperature, the relative humidity, and the speed of the wind. This research revealed that SR is highly correlated with both temperature and radiation. Furthermore, the findings demonstrated that the CNN-LSTM algorithm outperformed the other algorithm-trained models, as evidenced by the performance score of the respective models, with a maximum coefficient determination (R²) > 95% and a minimum mean square error (MSE) of 0.000987 at the testing step. In comparison with different existing artificial intelligence models, the CNN-LSTM model outperformed the other models. These scenarios demonstrated that a basic implementation of CNN-LSTM can be used to supplement conventional methods for predicting SR, provide possibilities to monitor radiation at a low cost, and encourage the adoption of data-driven management.

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“…These measurements re ect the level of linear correlation between the predicted and actual data values. (Li et al, 2023b). Generally, the predicted accuracy will be the highest value among models when its coe cient of determination R 2 value is near 1 (Badarloo, Kari, and Jafari, 2018).…”

Section: Performance Assessmentmentioning

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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Machine Learning-Based Method for Predicting Compressive Strength of Concrete

Cited by 36 publications

References 63 publications

Machine-Learning-Based Predictive Models for Compressive Strength, Flexural Strength, and Slump of Concrete

Machine-Learning-Based Predictive Models for Compressive Strength, Flexural Strength, and Slump of Concrete

Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems

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

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