Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill

Chongchong Qi, Andy Fourie, Qiusong Chen

    Research output: Contribution to journalArticle

    92 Citations (Scopus)

    Abstract

    Cemented paste backfill (CPB) has been widely used to prevent and mitigate hazards produced during the excavation of underground stopes. In practice, the strength of CPB is often an essential parameter for successful stope design. We propose an intelligent technique in this study for predicting the unconfined compressive strength (UCS) of CPB. This intelligent technique is a combination of the artificial neural network (ANN) and particle swarm optimization (PSO). The ANN was used for non-linear relationships modelling and PSO was used for the ANN architecture-tuning. Inputs of the ANN were selected to be the tailings type, the cement-tailings ratio, the solids content, and the curing time. A total of 396 CPB specimens under different combination of influencing variables were tested for the preparation of the dataset. The results showed that PSO was efficient for the ANN architecture-tuning. Also, comparison of the predicted UCS values with experimental values showed that the optimum ANN model was very accurate at predicting CPB strength.

    Original languageEnglish
    Pages (from-to)473-478
    Number of pages6
    JournalConstruction and Building Materials
    Volume159
    DOIs
    Publication statusPublished - 20 Jan 2018

    Fingerprint Dive into the research topics of 'Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill'. Together they form a unique fingerprint.

  • Cite this