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  Volume 3, Number 3, August 2021 , pages 201-220
 open access

Evaluating flexural strength of concrete with steel fibre by using machine learning techniques
Nitisha Sharma, Mohindra S. Thakur, Ankita Upadhya and Parveen Sihag

    In this study, potential of three machine learning techniques i.e., M5P, Support vector machines and Gaussian processes were evaluated to find the best algorithm for the prediction of flexural strength of concrete mix with steel fibre. The study comprises the comparison of results obtained from above-said techniques for given dataset. The dataset consists of 124 observations from past research studies and this dataset is randomly divided into two subsets namely training and testing datasets with (70-30)% proportion by weight. Cement, fine aggregates, coarse aggregates, water, super plasticizer/ high-range water reducer, steel fibre, fibre length and curing days were taken as input parameters whereas flexural strength of the concrete mix was taken as the output parameter. Performance of the techniques was checked by statistic evaluation parameters. Results show that the Gaussian process technique works better than other techniques with its minimum error bandwidth. Statistical analysis shows that the Gaussian process predicts better results with higher coefficient of correlation value (0.9138) and minimum mean absolute error (1.2954) and Root mean square error value (1.9672). Sensitivity analysis proves that steel fibre is the significant parameter among other parameters to predict the flexural strength of concrete mix. According to the shape of the fibre, the mixed type performs better for this data than the hooked shape of the steel fibre, which has a higher CC of 0.9649, which shows that the shape of fibers do effect the flexural strength of the concrete. However, the intricacy of the mixed fibres needs further investigations. For future mixes, the most favorable range for the increase in flexural strength of concrete mix found to be (1-3)%.
Key Words
    concrete; flexural Strength; gaussian processes; M5P; support vector machines
Nitisha Sharma, Mohindra S. Thakur and Ankita Upadhya: Department of Civil Engineering, Shoolini University, Solan, Himachal Pradesh, Zip Code 173229, India

Parveen Sihag: 2Department of Civil Engineering, Chandigarh University, Ajitgarh, Punjab, 140413, India

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