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

    36 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

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    Ointments
    Particle swarm optimization (PSO)
    Compressive strength
    Neural networks
    Tailings
    Network architecture
    Tuning
    Excavation
    Curing
    Hazards
    Cements

    Cite this

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    title = "Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill",
    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.",
    keywords = "Artificial neural network, Cemented paste backfill, Particle swarm optimization, Unconfined compressive strength",
    author = "Chongchong Qi and Andy Fourie and Qiusong Chen",
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    AU - Fourie, Andy

    AU - Chen, Qiusong

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    KW - Particle swarm optimization

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