Shu-Zhao Chen

Water scarcity, high non-productive soil evaporation, and poor vegetation establishment are major constraints on the sustainable ecological restoration of reconstructed open-pit mine dumps in arid and semi-arid regions. Conventional surface-applied water replenishment can result in rapid evaporative loss, thereby reducing the ecological benefits obtained from limited water resources. However, whether redistributing water into deeper reconstructed soil layers can simultaneously reduce non-productive evaporation, stabilize the root-zone hydrothermal environment, and improve vegetation growth remains insufficiently verified. In this study, a deep-profile soil water replenishment (DPSWR) device was tested in reconstructed mine-dump soil columns planted with locally adapted Stipa. Surface-applied water replenishment (CK) and DPSWR were compared using a single-run simulated rainfall comparison, soil water-retention and water-loss measurements, continuous temperature and moisture monitoring at 10 and 40 cm depths, and plant growth indicators. In the rainfall-simulation comparison, DPSWR showed lower cumulative water loss across the tested rainfall intensities and improved water-retention stability; the evaporation rate under CK was approximately 1.3 times that under DPSWR, whereas final soil water-holding capacity under DPSWR was approximately 2.4 times that under CK. Root fresh weight, plant fresh weight, and seedling number were significantly higher under DPSWR than under CK (p < 0.01), and maximum plant height and root length also increased significantly (p < 0.05). Under equal water-input conditions, DPSWR reduced non-productive water loss, prolonged soil water retention, and supported vegetation establishment. These findings suggest that DPSWR may provide a more water-efficient approach to the sustainable restoration of reconstructed mine dumps in water-limited regions.

Using gels to replace a certain amount of cement in concrete is conducive to the green concrete industry, while testing the compressive strength (CS) of geopolymer concrete requires a substantial amount of substantial effort and expense. To solve the above issue, a hybrid machine learning model of a modified beetle antennae search (MBAS) algorithm and random forest (RF) algorithm was developed in this study to model the CS of geopolymer concrete, in which MBAS was employed to adjust the hyperparameters of the RF model. The performance of the MBAS was verified by the relationship between 10-fold cross-validation (10-fold CV) and root mean square error (RMSE) value, and the prediction performance of the MBAS and RF hybrid machine learning model was verified by evaluating the correlation coefficient (R) and RMSE values and comparing with other models. The results show that the MBAS can effectively tune the performance of the RF model; the hybrid machine learning model had high R values (training set R = 0.9162 and test set R = 0.9071) and low RMSE values (training set RMSE = 7.111 and test set RMSE = 7.4345) at the same time, which indicated that the prediction accuracy was high; NaOH molarity was confirmed as the most important parameter regarding the CS of geopolymer concrete, with the importance score of 3.7848, and grade 4/10 mm was confirmed as the least important parameter, with the importance score of 0.5667.