Journal of Applied Science and Engineering

Published by Tamkang University Press

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Civil Engineering

Yanke LIU and Lei WANGThis email address is being protected from spambots. You need JavaScript enabled to view it.

School of Civil and Architectural Engineering, Cangzhou Jiaotong College, Cangzhou City, Heibei province, 061199, China

 

Received: April 19, 2025
Accepted: September 4, 2025
Publication Date: November 12, 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).0016  


One of the most important factors in steel reinforced concrete (SRC) composite constructions is the initial slip bond stress (τs) among profiled steel and concrete. Because they were developed using limited test data sets, and overfitting problems, the schemes now in use to forecast the (τs) of profiled steel-concrete may not be reliable when used more widely. To do this, this work uses ensemble machine learning techniques to create a predictive scheme using ensemble ML of the (τs) of profiled steel-concrete. Utilizing a computational database including 177 test results from previously published studies, eight input factors (nature of the variables are geometric, material, and experimental parameters) corresponding to the initial slip bond stress of profiled steel-concrete are determined. The framework used 75% of data for training and 25% for testing. Two Chaotic Game Optimization (CGO) and Sea Horse Optimization (SHO)-were combined with the Decision Tree (DT) algorithm. The CGO is a metaheuristic inspired by fractal geometry and chaos theory to resolve enhancement issues efficiently. The SHO mimics the spiral hunting strategy of predators to explore and exploit the search domain for ideal resolutions. The research indicates that (τs) can be properly estimated by both CGO-DT and SHO-DT. In the learning and evaluation stages, the SHO-DT approach showed excellent procedural reliability, with R2 values of 0.9698 and 0.9304, and RMSE values of 0.0509, and 0.0937. With equal R2 values of 0.9870 and 0.9838, and RMSE values of 0.0398 and 0.0468, the CGO-DT method recognized superior than SHO-DT.


Keywords: Steel reinforced concrete; Bond stress; Initial slip; Evaluation; Tree algorithm


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