Mechanical properties of cemented tailings and waste-rock backfill (CTWB) materials: Laboratory tests and deep learning modeling

Cemented tailings and waste-rock backfill (CTWB) is an effective way to solve the problem of mine solid waste. Waste-rock content is an important factor affecting UCS, but fully understanding the mechanism of waste-rock action on UCS would require extensive indoor testing, which would be tedious and expensive. Therefore, the mechanical properties of CTWB were investigated using a combination of laboratory testing and deep learning. Long short-term memory (LSTM) network prediction model based on genetic algorithm (GA) optimization was developed with cement content (290 -330 kg/m3), solid content (75 -81%), waste-rock content (40 -70%), and curing age as input variables and unconfined compressive strength (UCS) as output variables. Among them, the population size of GA was set to 40, and the crossover rate and mutation rate were set to 0.75 and 0.01, respectively. The study evaluated the models using RMSE, R2, and MAE and plotted normalized Taylor diagrams for different prediction models. The results show that the GA algorithm is effective for the parameter rectification of the LSTM model. The representative GA-LSTM model has an R2 of 0.9956 in the training set and an R2 of 0.9763 in the test set, indicating a good prediction effect. Deep learning models exhibit higher accuracy than machine learning models. Compared with GA-SVM, LSTM, and PSO-LSTM models, R2 increases from 0.9483, 0.8474, and 0.9611 to 0.9968, respectively, indicating that the model has good robustness and generalization ability. Moreover, the correlation analysis shows that the UCS reaches the maximum value when the waste rock content is 50%, and the hydration products can effectively fill the pores and form a more stable internal structure. The research results will provide technical support for safe, clean, and efficient recovery.

Automated summary

Cemented tailings and waste-rock backfill (CTWB) was studied using a combination of laboratory testing and deep learning. A long short-term memory (LSTM) network prediction model based on genetic algorithm (GA) optimization was developed to predict the unconfined compressive strength (UCS). The GA-LSTM model showed good prediction effect with an R2 of 0.9956 in the training set and 0.9763 in the test set. Deep learning models exhibited higher accuracy than machine learning models.