ANN, M5P-tree model, and nonlinear regression approaches to predict the compression strength of cement-based mortar modified by quicklime at various water/cement ratios and curing times

Abstract

This study’s goal is to establish systematic multiscale equations to estimate the compressive strength of cement mortar with a high volume of lime (L) and to be used by the construction industry with no theoretical restrictions. For that purpose, a wide tested data and the data gathered in the literature (a total of 392 tested cement mortar modified with lime) have been statically analyzed and modeled. The lime content ranged from 0 to 45% (by cement weight). Depending on literature data the w/c ranged from 0.3 to 0.74, the w/c of 0.5 was selected for this research. The compressive strength of lime-modified cement mortar for up to 28 days ranged from 3 to 75 MPa. The compression strength of the cement mortar reduced with an increasing percentage of lime. The linear and nonlinear regression, M5P-tree, and artificial neural network (ANN) technical approaches were used for the qualifications. In the modeling process, the most relevant parameters affecting the compression strength of cement mortar, i.e. lime (L) incorporation ratio (0–45% of cement’s mass), water-to-cement ratio (0.3–0.74), and curing ages (1 to 28 days). According to the statistical assessment such as R, MAE, and RMSE, the compression strength of cement mortar can be predicted very well in terms of water-to-cement ratio, lime content, and curing age using various simulation techniques. The maximum and minimum error between the actual test results and the outcome of the prediction using NLR and ANN (training dataset) were 0.01–21 MPa and 0.012–9 MPa, respectively, and ranged between 0.03 and 14 MPa and 0.02 and 6 MPa errors, respectively, in terms of tested data. The margin of error in using the nonlinear regression-based model (NLR) and ANN for the training dataset was 1.41 and 1.92, respectively, and it was 2.26 and 2.44 respectively in using the tested dataset. The outcomes of this paper suggest that the nonlinear regression-based model (NLR) and ANN are performing better than other applied models using training and testing datasets. The result of the sensitivity investigation was the curing period that is the highest dominating value for the prediction of the compressive strength of cement mortar.