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Employing the optimization algorithms with machine learning framework to estimate the compressive strength of ultra-high-performance concrete (UHPC)

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Abstract

Ultra-high-performance concrete (UHPC), with the highest resistance capacity against axial loads, is composed of various ingredients compared to other typical concretes, including fly ash and silica fume; eco-friendly materials also have an economical price. Such high-resistant constructional materials are seen as a suitable aggregate to use in most practical projects. The compressive strength (CS) of concrete, as one of the important variables in engineering fields, can be estimated using smart approaches based on ingredients as inputs fed to the mathematical model. Consequently, the current study modeled the CS values using a machine learning technique of Support vector regression (SVR) accompanied by the grasshopper optimization algorithm (GOA) and arithmetic optimization algorithm (AOA), tunning the SVR to appraise the CS accurately. In developing AOA-SVR and GOA-SVR frameworks, several metrics were used to assess the ability and accuracy of models. As a result of the present study, it can be concluded that the machine learning method in hybrid form has the ability to predict for saving time and energy. In general, in comparing the results of hybrid models, AOA had the most suitable combination with SVR compared to GOA, with R2 = 0.901 and RMSE = 9.986 in the training phase. In addition, AOA-SVR improved its performance by R2 = 0.917 and RMSE = 9.525 in the test phase.

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Authors and Affiliations

  1. College of Urban Construction, Hebei Normal University of Science and Technology, Qinhuangdao, 066004, China

    Yajing Zhang, Sai An & Hao Liu

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  1. Yajing Zhang
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  2. Sai An
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  3. Hao Liu
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Contributions

YZ: writing-original draft preparation. conceptualization, supervision, and project administration. SA: validation, formal analysis, methodology, language review. HL: methodology, software, writing-original draft preparation, language review.

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Correspondence to Yajing Zhang.

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Zhang, Y., An, S. & Liu, H. Employing the optimization algorithms with machine learning framework to estimate the compressive strength of ultra-high-performance concrete (UHPC). Multiscale and Multidiscip. Model. Exp. and Des. 7, 97–108 (2024). https://doi.org/10.1007/s41939-023-00187-4

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