Artificial intelligence model for studying unconfined compressive performance of fiber-reinforced cemented paste backfill
Zhi Yu, Xiuzhi Shi, Xin Chen, Jian Zhou, Chongchong Qi, Qiusong Chen, D. Ganeshwar Rao
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.