AI Article Synopsis

  • Cracking in concrete structures is a major issue influenced by various factors, prompting the exploration of machine learning methods to predict concrete performance efficiently, saving time and costs.
  • A study focuses on estimating the splitting-tensile strength of concrete with recycled coarse aggregate (RCA) using artificial intelligence, analyzing nine parameters across 154 mixes with different machine learning algorithms, namely support vector machine, AdaBoost, Bagging, and random forest.
  • The random forest algorithm demonstrated the best performance with a high coefficient of determination (R = 0.96) and low errors, while SHAP analysis revealed that cement content significantly boosts splitting-tensile strength, whereas excessive water negatively impacts it.

Article Abstract

Cracking is one of the main problems in concrete structures and is affected by various parameters. The step-by-step laboratory method, which includes casting specimens, curing for a certain period, and testing, remains a source of worry in terms of cost and time. Novel machine learning methods for anticipating the behavior of raw materials on the ultimate output of concrete are being introduced to address the difficulties outlined above such as the excessive consumption of time and money. This work estimates the splitting-tensile strength of concrete containing recycled coarse aggregate (RCA) using artificial intelligence methods considering nine input parameters and 154 mixes. One individual machine learning algorithm (support vector machine) and three ensembled machine learning algorithms (AdaBoost, Bagging, and random forest) are considered. Additionally, a post hoc model-agnostic method named SHapley Additive exPlanations (SHAP) was performed to study the influence of raw ingredients on the splitting-tensile strength. The model's performance was assessed using the coefficient of determination (R), root mean square error (RMSE), and mean absolute error (MAE). Then, the model's performance was validated using k-fold cross-validation. The random forest model, with an R of 0.96, outperformed the AdaBoost models. The random forest models with greater R and lower error (RMSE = 0.49) had superior performance. It was revealed from the SHAP analysis that the cement content had the highest positive influence on the splitting-tensile strength of the recycled aggregate concrete and the primary contact of cement is with water. The feature interaction plot shows that high water content has a negative impact on the recycled aggregate concrete (RAC) splitting-tensile strength, but the increased cement content had a beneficial effect.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9229192PMC
http://dx.doi.org/10.3390/ma15124194DOI Listing

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