An approach for predicting the compressive strength of cement-based materials exposed to sulfate attack

PLoS One. 2018 Jan 18;13(1):e0191370. doi: 10.1371/journal.pone.0191370. eCollection 2018.

Abstract

In this paper, a support vector machine (SVM) model which can be used to predict the compressive strength of mortars exposed to sulfate attack was established. An accelerated corrosion test was applied to collect compressive strength data. For predicting the compressive strength of mortars, a total of 638 data samples obtained from experiment was chosen as a dataset to establish a SVM model. The values of the coefficient of determination, the mean absolute error, the mean absolute percentage error and the root mean square error were used for evaluating the predictive accuracy. The main factors affecting the predicted compressive strength were obtained by sensitivity analysis. A SVM model was calibrated, validated, and finally established. Moreover, the performance of the SVM model was compared to an artificial neural network (ANN) model. Results show that the prediction values from the SVM model were close to the experimental values; the main factors sensitive to concrete compressive strength were exposure time, water-cement ratio and sulfate ions; the performance of the SVM model was better than the ANN model. The SVM model developed in this study can be potentially used for predicting the compressive strength of cement-based materials servicing in harsh environments.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Compressive Strength*
  • Construction Materials*
  • Materials Testing*
  • Neural Networks, Computer
  • Sulfates*
  • Support Vector Machine

Substances

  • Sulfates

Grants and funding

The authors appreciate the financial support from the National Program on Key Basic Research Project of China (Grant No. 2015CB6551002). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.