Machine learning based models for predicting compressive strength of geopolymer concrete

Abstract

Recently, great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly properties. Much effort has been made in experimental studies to advance the understanding of geopolymer concrete, in which compressive strength is one of the most important properties. To facilitate engineering work on the material, an efficient predicting model is needed. In this study, three machine learning (ML)-based models, namely deep neural network (DNN), K-nearest neighbors (KNN), and support vector machines (SVM), are developed for forecasting the compressive strength of the geopolymer concrete. A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models. A careful procedure for data preprocessing is implemented, by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting process. The standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed, thus the generalizability of the models is ensured. The effectiveness of the models is assessed via statistical metrics including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and the recently proposed performance index (PI). The basic mean square error (MSE) is used as the loss function to be minimized during the model fitting process. The three ML-based models are successfully developed for estimating the compressive strength, for which good correlations between the predicted and the true values are obtained for DNN, KNN, and SVM. The numerical results suggest that the DNN model generally outperforms the other two models.