Deep high-stress roadway excavation under unloading disturbance inevitably leads to damage deterioration of the surrounding rock, which poses a serious threat to its stability. To explore the energy characteristics of white sandstone damaged by peak front unloading, uniaxial compression tests were conducted on damaged rock samples. The results show that the peak strength and modulus of elasticity of the rock sample gradually decrease with increasing damage degrees. The external work input energy, releasable elastic strain energy and dissipation energy all decreased with increasing damage. Damage evolution curves and equations of the rock samples were obtained based on the damage instantiation model established by the principle of energy dissipation and release. The effects of unloading damage on the fracture characteristics of the rock samples were analysed from both macro and microscopic viewpoints, and the results showed that a micro fracture in the rock is transformed from brittle–ductile damage, while macroscopic damage occurs in the form of a "shear"-"splitting"-"mixed shear-splitting" damage process. This paper has certain research and reference value for understanding the damage evolution characteristics of rocks with peak front unloading damage.
To alleviate the environmental problems caused by scrap tire and tailings disposal, the performance of recycled tire polymer fiber (RTPF)-reinforced cemented paste backfill (CPB) was investigated. Ordinary CPB, commercial poly-propylene fiber (CPPF) and reinforced CPB were also investigated for comparison. Slump tests, unconfined compression tests and a cost–benefit analysis were conducted. The results indicate that the flowability of the RTPF-reinforced CPB decreased with the increasing fiber content. The failure strain, unconfined compressive strength, and toughness values were generally higher than that of ordinary CPB (i.e., CPB without any fiber reinforcement). However, the mechanical properties would not be improved continuously with increasing RTPF content. It was found that the inclusion of RTPFs achieved the best improvement effect with the best mechanical properties of CPB at the fiber content of 0.6%. The failure mode of the RTPF-reinforced CPB was safer than that of the ordinary CPB. Microscopic observations indicated that the bond between RTPFs and the CPB matrix could affect the mechanical properties of the RTPF-reinforced CPB. From the cost–benefit analysis, the inclusion of RTPFs to reinforce CPB could gain relatively high mechanical properties with a low material cost.
Mine tailings disposal has been a serious environmental issue for decades. The wide application of cemented tailings backfill (CTB) technology could indirectly abate tailings pollution by recycling the tailings for backfilling. CTB constitutive modeling helps with design by improving the understanding of its compressive behavior. This study focused on CTB intelligent constitutive modeling considering the coupled effects of the cement content and saturation state. An artificial intelligence model was established and utilized based on particle swarm optimization (PSO) and the support vector machine (SVM). CTB samples with different cement contents and water saturation states were prepared, and unconfined compression tests were conducted to obtain the dataset. We verified the feasibility of using integrated PSO and SVM (P-S) in the CTB constitutive model using experimental data. We analyzed model errors. The results showed that the CTB stress strain curve was complex and nonlinear and could be significantly affected by the saturation states. PSO was feasible and efficient for tuning the SVM hyperparameters. The lowest minimum MSE value of 0.0108 was achieved in the eighth iteration. The PSO and SVM modeling was indicated to be accurate in the CTB constitutive model (a high R-square value of 0.9935 and a low mean squared error value of 0.001664 were achieved on the testing set). This model may accelerate the CTB structure design process.
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