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ML-Based Computational Model to Estimate the Compressive Strength of Sustainable Concrete Integrating Silica Fume and Steel Fibers

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Key Digital Trends Shaping the Future of Information and Management Science (ISMS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 671))

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Abstract

Concrete is one of the most commonly used construction material on the earth after water. The compressive strength of concrete is an important parameter and is considered in all structural designs. Production of the cement is directly proportional to carbon emissions. The cement content in the concrete can be partially replaced with waste materials like steel fibers, silica fumes, etc. Calculating compressive strength in a laboratory takes huge amount of time, manpower, cost and produces a large amount of wastage. Apart from the constituents of concrete, the compressive strength also depends on various factors such as temperature, mixing, types of aggregate, and quality of the water. The analytical models failed to deal with difficult problems. Artificial intelligence has enough capabilities to deal with such kind of complex problems. In this work, an artificial neural network (ANN) based model has been developed to predict the compressive strength of steel fiber and silica fumes-based concrete. The R-value of the developed model is 0.9948 and the mean absolute percentage error is 5.47%. The mean absolute error and root mean square error of the proposed model is 1.73 MPa and 6.89 MPa, respectively. The developed model is easy to use and reliable to estimate the compressive strength of concrete incorporating silica fumes and steel fibers.

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Author information

Authors and Affiliations

  1. Guru Nanak Dev Engineering College (GNDEC), Ludhiana, 141006, India

    Sarvanshdeep Singh Sahota & Hardeep Singh Rai

  2. Structural Engineering Department, CSIR-Central Building Research Institute, Roorkee, 247667, India

    Harish Chandra Arora & Aman Kumar

  3. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India

    Harish Chandra Arora & Aman Kumar

  4. Department of Hydro and Renewable Energy, Indian Institute of Technology, Roorkee, 247667, India

    Krishna Kumar

Authors
  1. Sarvanshdeep Singh Sahota
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  2. Harish Chandra Arora
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  3. Aman Kumar
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  4. Krishna Kumar
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  5. Hardeep Singh Rai
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Corresponding author

Correspondence to Aman Kumar .

Editor information

Editors and Affiliations

  1. Faculty of Information and Communication Technology, University of Malta, Msida, Malta

    Lalit Garg

  2. Department of Computer Science and Engineering, National Institute of Technology Raipur, Chhatisgarh, India

    Dilip Singh Sisodia

  3. Central University of Rajasthan, Tehsil Kishangarh, Rajasthan, India

    Nishtha Kesswani

  4. CIS Department, University of Malta, Msida, Malta

    Joseph G. Vella

  5. EMLYON Business School, Écully, France

    Imene Brigui

  6. Department of Applied Data Science, Institute of Energy Technology, Halden, Norway

    Sanjay Misra

  7. Computer Science and Engineering, National Institute of Technology, Chhattisgarh, India

    Deepak Singh

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Sahota, S.S., Arora, H.C., Kumar, A., Kumar, K., Rai, H.S. (2023). ML-Based Computational Model to Estimate the Compressive Strength of Sustainable Concrete Integrating Silica Fume and Steel Fibers. In: Garg, L., et al. Key Digital Trends Shaping the Future of Information and Management Science. ISMS 2022. Lecture Notes in Networks and Systems, vol 671. Springer, Cham. https://doi.org/10.1007/978-3-031-31153-6_20

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