Akademik Veri Yönetim Sistemi
Engineering Applications of Artificial Intelligence, cilt.156, 2025 (SCI-Expanded)
This study reveals the unprecedented potential of artificial intelligence (AI) models in accurately predicting the mechanical properties of microwave-cured geopolymer mortars, thereby addressing a critical gap in the integration of AI and materials science. Furthermore, applying advanced algorithmic structure and machine learning is an unexplored area in extant literature. Four parameters: conventional curing period (time- NH), conventional curing temperature (temp-NH), microwave power (W), and microwave curing period (time-MW) are considered to generate a comprehensive dataset to predict compressive strength (CS) and flexural strength (FS). Four AI models have been adopted and rigorously compared: deep learning neural network (DL-NN), probabilistic neural network (PNN), radial basis function neural network (RBF-NN), and support vector machine (SVM). The performance was evaluated using various statistical matrices and visualization graphs. The findings showed that the DL-NN model performs exceptionally well in predicting compressive and flexural strengths, with correlation coefficient (R) values of 0.966 and 0.931, mean absolute error (MAE) of 3.544 MPa and 0.990 MPa, and root mean square error (RMSE) of 5.856 MPa and 1.442 MPa, respectively. These results demonstrate the model's ability to handle the complex, non-linear relationships inherent in the data. Meanwhile, the PNN model ranked second, with R values of 0.930 and 0.833, MAE values of 5.151 MPa and 1.566 MPa, and RMSE values of 7.947 MPa and 2.089 MPa, respectively. Furthermore, the carbon dioxide (CO2) emissions and embodied energy were investigated. Finally, a sensitivity analysis was conducted to assess the relative importance of each parameter on the mechanical properties.