Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill-Reference-Cited by-同舟云学术

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Developing a hybrid model of salp swarm algorithm-based support vector machine to predict the strength of fiber-reinforced cemented paste backfill

Published:2020-03-27 Issue:4 Volume:37 Page:3519-3540
ISSN:0177-0667
Container-title:Engineering with Computers
language:en
Short-container-title:Engineering with Computers

Author:

Li Enming,Zhou Jian,Shi Xiuzhi,Jahed Armaghani Danial,Yu Zhi,Chen Xin,Huang Peisheng

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,General Engineering,Modelling and Simulation,Software

Link

https://link.springer.com/content/pdf/10.1007/s00366-020-01014-x.pdf

Reference106 articles.

1. Yılmaz T, Ercikdi B (2016) Predicting the uniaxial compressive strength of cemented paste backfill from ultrasonic pulse velocity test. Nondestruct Test Eval 31(3):247–266

2. Fall M, Benzaazoua M (2005) Modeling the effect of sulphate on strength development of paste backfill and binder mixture optimization. Cem Concr Res 35(2):301–314

3. Orejarena L, Fall M (2010) Artificial neural network based modeling of the coupled effect of sulphate and temperature on the strength of cemented paste backfill. Can J Civ Eng 38(1):100–109

4. Jiang H, Fall M (2017) Yield stress and strength of saline cemented tailings in sub-zero environments: portland cement paste backfill. Int J Miner Process 160:68–75

5. Qi C, Fourie A, Chen Q (2018) Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill. Constr Build Mater 159:473–478

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