Predict the sealing shrinkage of high-performance building concrete using a hybrid machine learning model

Zhaofeng  Zhang; Ruifang  Gong

doi:10.6180/jase.202606_29(6).0023

Predict the sealing shrinkage of high-performance building concrete using a hybrid machine learning model

Zhaofeng Zhang¹ and Ruifang Gong²This email address is being protected from spambots. You need JavaScript enabled to view it.

¹Shijiazhuang College of Applied Technology, Shijiazhuang 050800, Hebei, China

²Civil Engineering, North China University of Technology, Shijiazhuang 050000, Hebei, China

Received: May 31, 2025
Accepted: September 21, 2025
Publication Date: November 22, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202606_29(6).0023

High-performance concrete (HPC) is widely used in modern construction due to its strength and durability, yet it is prone to sealed shrinkage that can cause early-age cracking and structural degradation. Existing studies often fall short in accurately predicting this complex phenomenon. This study introduces a novel hybrid machine learning framework that combines Gaussian Process Regression (GPR) and Categorical Boosting Regression (CATR) with two bio-inspired optimization algorithms: Manta Ray Foraging Optimization (MRFO) and Artificial Rabbit Optimization (ARO). Four hybrid models CAAO, CAMO, GPAO, and GPMO were developed and evaluated. Among them, the CAAO model demonstrated the highest predictive accuracy. The findings show that hybrid models significantly improve the reliability of shrinkage prediction in HPC by capturing nonlinear interactions among material and environmental factors. This work contributes to the advancement of predictive tools for concrete design, enabling more durable and crack-resistant HPC structures.

Keywords: Sealed shrinkage, high-performance concrete, Gaussian Process Regression, Categorical Boosting Regression, Manta Ray Foraging Optimization, Artificial Rabbit Optimization

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