Development of Backfill Concrete Including Coal Gangue and Metakaolin and Prediction of Compressive Strength by Extreme Learning Machine

Abstract

The main aim of this investigation is to develop backfill concrete including coal gangue and metakaolin to reduce solid waste. For this purpose, a total of 30 concrete mixtures were designed by the inclusion of 0%, 25%, 50%, 75% and 100% coal gangue as coarse aggregates and 0%, 10% and 20% metakaolin as binder at 0.55 and 0.45 water to cement ratios. The compressive strength was tested after 3, 7 and 28 days for a total of 90 samples. Meanwhile, the influences of coal gangue and metakaolin on the elastic modulus, ultrasonic pulse velocity, rebound number and open porosity were explored. Then, the relationship between physical and mechanical properties was revealed by design code expressions and empirical models. Furthermore, an extreme learning machine was developed to predict compressive strength by concrete mixtures. The results show that the inclusion of coal gangue results in a poor performance in physical and mechanical properties of concrete. However, the drawbacks of concrete containing coal gangue can be compensated by metakaolin. The predicted results of design code expressions and empirical models are closed to the experiment results, with a 10% error. In addition, the findings reveal that the extreme learning machine offers significant potential to predict the compressive strength of concrete with high precision.