article · Ain Shams Engineering Journal
Recycled aggregate concrete offers an environmentally friendly alternative to traditional building materials by repurposing waste and reducing natural resource consumption. Its variable composition makes its compressive strength difficult to determine accurately. To solve this, researchers compiled a dataset of 314 concrete mixes from existing literature to train and fine-tune five gradient boosting machine learning algorithms. The models tested included Gradient Boosting Machine, LightGBM, XGBoost, Categorical Gradient Boost, and HistGradientBoosting. Model interpretability techniques were applied to identify the most significant factors affecting strength. Categorical Gradient Boost delivered the highest overall accuracy, while LightGBM achieved the lowest mean absolute error. The analysis identified cement content, water content, sand content, and the water absorption capacity of recycled aggregates as the primary drivers of compressive strength in recycled concrete mixes.
Using recycled aggregates reduces construction waste and protects natural resources, but variable material quality often makes structural strength hard to guarantee. By applying machine learning to predict performance accurately, engineers can design reliable recycled concrete mixes without conducting extensive and costly laboratory trials, supporting more sustainable building practices.
This work could enable concrete producers and civil engineers to rapidly evaluate and optimise recycled concrete mix designs through predictive software. The research is computational and applied, relying on a database of historical literature mixes rather than direct commercial site trials. The abstract indicates the analytical framework is proven on existing data, but it does not specify a direct commercial deployment pathway or real-world industrial testing.
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The construction industry is shifting towards sustainability, emphasizing the need for innovative materials. Recycled Aggregate Concrete (RAC), utilizing recycled aggregates, emerges as a promising eco-friendly solution to minimize waste and resource utilization. However, accurately predicting its compressive strength (CS) is challenging due to varying composition and properties. This study addresses this issue by employing machine learning models, specifically five gradient boosting algorithms: Gradient Boosting Machine (GBM), LightGBM, XGBoost, Categorical Gradient Boost (CGB), and HistGradientBoosting (HGB). A total of 314 mixes from relevant published literature were aggregated to train the models. These models are meticulously fine-tuned through hyperparameter optimization for optimal predictive performance. The study also introduces SHAP (SHapley Additive exPlanations) algorithms for model interpretability, elucidating feature contributions to predictions. The results revealed that among the five gradient boosting models, CGB demonstrated the highest R2 value of 92% on the testing set, while LightGBM exhibited the lowest Coefficient of Determination (R2) value of 88%. Additionally, CGB achieved the lowest Root Mean Square Error (RMSE) of approximately 4.05, whereas XGBoost showed the highest RMSE of around 4.8. Furthermore, for Mean Absolute Error (MAE), LightGBM recorded the lowest value of approximately 3.16, while HGB yielded the highest MAE of about 3.8. The SHAP analyses reveal influential features impacting RAC strength, highlighting the significance of cement, water, sand, and recycled aggregate water absorption in predicting RAC compressive strength.
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DOI: 10.1016/j.asej.2024.102975
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