MACHINE LEARNING BASED PREDICTION OF COMPRESSIVE STRENGTH IN CONCRETE INCORPORATING SYNHTHETIC FIBERS
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Different types of fibers are added to the concrete mixture to improve its behavior under different loading cases. This study intends to investigate the compressive strength of concrete cubic samples in which synthetic macro fibers are added in different amounts. For this purpose, a total of 72 cubic samples are produced in the experimental program. Axial pressure test is applied to cubic samples and 7 and 28 days compressive strength values are obtained in the end. However, a lot of effort has been spent to complete the time-consuming laboratory tests. To overcome this situation, four machine learning methods-Xgboost, Random Forest, Decision Tree, and Multiple Linear Regression-are adapted for efficient compressive strength forecasting. Moreover, four metrics are employed for a more meaningful evaluation of models: R2, RMSE, MAE, and MAPE. Remarkably, all models achieved R2 values exceeding 90%, with Xgboost notably reaching an impressive R2 value of 97%. This highlights the effectiveness of integrating machine learning in predicting compressive strength, offering a viable alternative to traditional laboratory tests. Incorporating the Shapley Additive exPlanation (SHAP) method, the study provides a detailed analysis of the models' interpretability. SHAP analysis revealed that Day and Fiber have been identified as crucial features influencing compressive strength predictions. Localized SHAP analyses for specific samples further enhanced the understanding of individual predictions, emphasizing the practicality and transparency of machine learning in structural engineering. The promising results of this study indicate the potential for further advancements in enhancing performance, utilizing machine learning insights.