Abstract:To reduce the difficulty of obtaining the unconfined compressive strength (UCS) value of fiber-reinforced cemented paste backfill (CPB) and analyze the comprehensive impact of conventional and fiber variables on the compressive property, a new artificial intelligence model was proposed by combining a newly invented meta-heuristics algorithm (salp swarm algorithm, SSA) and extreme learning machine (ELM) technology. Aiming to test the reliability of that model, 720 UCS tests with different cement-to-tailing mass ratio, solid mass concentration, fiber content, fiber length, and curing time were carried out, and a strength evaluation database was collected. The obtained results show that the optimized SSA-ELM model can accurately predict the uniaxial compressive strength of the fiber-reinforced CPB, and the model performance of SSA-ELM model is better than ANN, SVR and ELM models. Variable sensitivity analysis indicates that fiber content and fiber length have a significant effect on the UCS of fiber-reinforced CPB.

Key words: fiber-reinforced cemented paste backfill; unconfined compressive strength; prediction; extreme learning machine; salp swarm algorithm