Estimating compressive strength of concrete containing untreated coal waste aggregates using ultrasonic pulse velocity

In recent years, the overuse and exploitation of coal resources as fuel in industry has caused many environmental problems as well as changes in the ecosystem. One way to address this issue is to recycle these materials as an alternative to aggregates in concrete. Recently, non-destructive tests have also been considered by the researchers in this field. As there is limited work on the evaluation of the compressive strength of concrete containing coal waste using non-destructive tests, the current study aims to estimate the compressive strength of concrete containing untreated coal waste aggregates using the ultrasonic pulse velocity (UPV) technique as a non-destructive testing approach. For this purpose, various concrete parameters such as the compressive strength and UPV were investigated at different ages of concrete with different volume replacements of coarse and fine aggregates with coal waste. The test results indicate that 5% volume replacement of natural aggregates with untreated coal waste improves the average compressive strength and UPV of the concrete mixes by 6 and 1.2%, respectively. However, these parameters are significantly reduced by increasing the coal waste replacement level up to 25%. Furthermore, a general exponential relationship was established between the compressive strength and the UPV associated with the entire tested concrete specimens with different volume replacement levels of coal waste at different ages. The proposed relationship demonstrates a good correlation with the experimental results

Experimental and informational modeling study on flexural strength of eco-friendly concrete incorporating coal waste

Construction activities have been a primary cause for depleting natural resources and are associated with stern environmental impact. Developing concrete mixture designs that meet project specifications is time-consuming, costly, and requires many trial batches and destructive tests that lead to material wastage. Computational intelligence can offer an eco-friendly alternative with superior accuracy and performance. In this study, coal waste was used as a recycled additive in concrete. The flexural strength of a large number of mixture designs was evaluated to create an experimental database. A hybrid artificial neural network (ANN) coupled with response surface methodology (RSM) was trained and employed to predict the flexural strength of coal waste-treated concrete. In this process, four influential parameters including the cement content, water-to-cement ratio, volume of gravel, and coal waste replacement level were specified as independent input variables. The results show that concrete incorporating 3% recycled coal waste could be a competitive and eco-efficient alternative in construction activities while attaining a superior flexural strength of 6.7 MPa. The RSM-modified ANN achieved superior predictive accuracy with an RMSE of 0.875. Based on the experimental results and model predictions, estimating the flexural strength of concrete incorporating waste coal using the RSM-modified ANN model yielded superior accuracy and can be used in engineering practice to save the effort, cost, and material wastage associated with trial batches and destructive laboratory testing while producing mixtures with enhanced flexural strength