Developing a forecasting model of concrete compressive strength using relevance vector machines

  • Authors

    • Mohammad Awwad Al-Balqa University
    2014-04-29
    https://doi.org/10.14419/ijet.v3i2.2011

  • We analyze results of two experiments that tested effect of adding Silica on the compressive strength of concrete at early stage and after long period. The two experiments evaluated different silica/cement ratios for different mixing periods. Adding Silica to concrete mix produce high early strength material which is highly desirable in airports and highways.

    More than 90 samples of different silica/cement ratios are tested for compressive strength at 3 and 28 days. Test results showed high early up to 60 MPa. Strength increase is proportional with the increase of silica/cement ratio and mixing time with maximum at ratio of 15/100 and 30 minutes mixing time.

    A relevance Vector Machine (RVM) model is developed to predict concrete compressive strength using concrete mixture inputs information. RVM model predictions matched experimental data closely.  The developed model can be used to predict compressive strength in future periods based on initial information related to cement mixture.

     

    Keywords: Relevance Vector Machine, Silicate Percent, Prediction Model, Milling Time, Compressive Strength, Concrete.

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  • How to Cite

    Awwad, M. (2014). Developing a forecasting model of concrete compressive strength using relevance vector machines. International Journal of Engineering & Technology, 3(2), 224-229. https://doi.org/10.14419/ijet.v3i2.2011