Machine Learning (ML) Enhances Concrete Strength Prediction

By Nidhi DhullReviewed by Susha Cheriyedath, M.Sc.Jul 30 2024

A recent article published in Scientific Reports proposed using machine learning (ML) to enhance concrete compressive strength (CS) prediction. A graphic user interface (GUI) was also designed to bridge the gap between complex computational predictions and real-world applications.

Background

Portland cement (PC) manufacturing accounts for approximately 7 % of global CO₂ emissions. Thus, incorporating waste and recycled materials into concrete is considered a viable and sustainable solution to meet the growing demand for concrete and prevent the degradation of natural resources.

Various industrial by-products such as ground granulated blast furnace slag (GGBS), granite powder, and fly ash can substitute PC in concrete. However, their impact on concrete CS needs careful evaluation. Conventional laboratory tests to evaluate CS are now considered inefficient and not cost-effective.

Recently, the artificial intelligence (AI) sector has advanced significantly through the evolution of various ML models. Predicting concrete CS using ML models allows for optimizing mix designs, ensuring the concrete meets the required performance standards without extensive mixture trials that waste time and resources. Thus, this study explored ML models to predict concrete CS and display the results through a GUI for practical applications.

Methods

In this study, the researchers employed ML models to enhance the prediction of concrete compressive strength (CS) by analyzing 1,030 experimental data points from previous research, with CS values ranging from 2.33 to 82.60 MPa. The main aim was to examine the efficacy of different ML models in predicting concrete CS.

Data from the 1,030 datasets, including components such as cement and aggregates, were collected and analyzed through histograms and heat maps. Subsequently, two types of predictive models were explored: non-ensemble and ensemble models. The non-ensemble models included regression-based, evolutionary, neural network, and fuzzy-inference-system approaches, while the ensemble models consisted of adaptive boosting, random forest, and gradient boosting techniques.

The input parameters for the ML models included cement, blast-furnace slag, aggregates (coarse and fine), fly ash, water, superplasticizer, and curing days, with CS as the output variable. The performance of the ML models was evaluated by comparing the predicted and actual values using seven performance indices: determination coefficient (R^²), Willmott index (WI), root mean square error (RMSE), scatter index (SI), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean bias error (MBE).

Additionally, k-fold cross-validation was performed to check the reliability and accuracy of the ML models. Furthermore, a sensitivity analysis using Shapley-Additive-exPlanations (SHAP) was conducted to comprehend the impact of each input variable on CS. Finally, a Python web application was developed to make the ML models accessible and usable for concrete prediction through an intuitive graphical user interface (GUI).

Results and Discussion

The performance of the ensemble and non-ensemble ML models was compared to analyze their prediction capabilities. The evaluation framework included visual methods such as scatter plots, violin boxplots, and Taylor diagrams to ensure the reliability and precision of the developed models. Among all models, CatBoost predicted the concrete CS with maximum accuracy during the testing stage. It exhibited an R^² value of 0.966, MAE of 2.27 MPa, and RMSE of 3.06 MPa.

The CatBoost, XGBoost, and Random Forest (RF) ensemble models performed well across low (2.33-25.02 MPa), medium (25.08-55.02 MPa), and high (55.06-82.60 MPa) CS ranges. Notably, CatBoost exhibited consistently superior performance in all ranges. The SHAP analysis using CatBoost helped identify the most dominant parameters influencing CS prediction. The most important parameter was the concrete age, followed by cement content. While water, coarse aggregates, and fly ash parameters exhibited moderate effects, superplasticizers and fine aggregates demonstrated minimal effects.

The user-friendly GUI developed in this study was introduced on an open-source platform like GitHub. This Python-based web application could help designers predict concrete CS rapidly and cost-effectively. Additionally, it supports real-time and precise predictive capabilities, enabling model refinement and improvement. Thus, the proposed GUI improves efficiency and resource management in the construction sector compared to conventional laboratory tests.

Conclusion

Overall, the researchers examined seven ML models from ensemble and non-ensemble categories to predict concrete CS; the CatBoost exhibited the best performance with high accuracy and generalization capability. However, the efficacy of the proposed ML models was limited by the range and quality of the input data. Thus, they might not apply to datasets or conditions extremely different from the training data. Moreover, these models did not account for all factors that could influence concrete CS, such as curing temperature and environment.

The researchers suggest expanding the dataset by considering more varied concrete designs and external factors in the future. In addition, deep learning models can enhance predictive accuracy using this vast and complex dataset. The proposed GUI can also be integrated with greater user guidance features and tutorials to expand usage. Finally, validating the models with real-world data will ensure their robustness and reliability in practical construction applications.

Journal Reference

Elshaarawy, M. K., Alsaadawi, M. M., & Hamed, A. K. (2024). Machine learning and interactive GUI for concrete compressive strength prediction. Scientific Reports, 14(1), 16694. DOI: 10.1038/s41598-024-66957-3, https://www.nature.com/articles/s41598-024-66957-3

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