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Compressive strength prediction of high-performance concrete with utilization of automated least square support vector regression-based algorithm

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Abstract

High-performance concrete (HPC) is extensively employed in the construction sector owing to its exceptional strength and durability. The mechanical characteristics of HPC, particularly its compressive and tensile strength, are vital indicators. Precise forecasts of concrete strength are essential for enhancing the design and performance of concrete structures. This study proposes a novel technique for predicting the strength of HPC. It involves utilizing the least square support vector regression (LSSVR) algorithm in combination with three optimizers: Gold Rush Optimizer, Political Optimizer, and Golden Sine Algorithm. LSSVR algorithm is a robust machine-learning method that has demonstrated favorable outcomes in numerous forecasting tasks. The use of LSSVR enables the accurate modeling and prediction of the intricate connection between the strongest attributes of HPC and the variables that impact it. To achieve this goal, a dataset containing 344 samples of high-performance concrete was gathered and utilized to train and assess the LSSVR algorithm. However, selecting appropriate optimization algorithms is critical for improving prediction accuracy and accelerating convergence speed. The proposed framework's performance is evaluated using actual strength data from HPC by conducting thorough experimentation and comparative analysis. The findings reveal that integrating LSSVR with the mentioned optimizers surpasses conventional optimization methods in prediction convergence speed and accuracy. The proposed framework offers a dependable and efficient solution for precisely predicting HPC strength, allowing engineers and researchers to optimize the design and construction of high-performance concrete structures.

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  1. Zhengzhou Urban Construction Vocational College, Zhengzhou, 451263, Henan, China

    Caoli Lu

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  1. Caoli Lu
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CL: Writing—Original draft preparation, Conceptualization, Supervision, Project administration.

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Lu, C. Compressive strength prediction of high-performance concrete with utilization of automated least square support vector regression-based algorithm. Multiscale and Multidiscip. Model. Exp. and Des. (2023). https://doi.org/10.1007/s41939-023-00312-3

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