Civil Engineering

Xianhui YanThis email address is being protected from spambots. You need JavaScript enabled to view it., Yuanyuan Hu, Hui Tang, and Daibing Cheng

Civil Engineering and Architecture Engineering College, Nanchong Vocational and Technical College, Nanchong 637131, Sichuan, China

 

Received: May 12, 2025
Accepted: July 30, 2025
Publication Date: September 13, 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.202605_29(5).0011  


The main goal of engineering training is to create secure, reliable structures that are simple to design and evaluate using straightforward analytical methods. Consequently, a standard design method assumes that one is familiar with all fundamental structural typologies and then chooses the one that best meets the presumptive requirements. Nevertheless, Advances in computer modeling enable progress, yet shear load distribution in reinforced concrete beams with stirrups remains unclear. This work used artificial intelligence (AI) models to avoid these cognitive constraints. The present study introduces a data-driven machine learning methodology utilizing Random Forests Regression (RFR) in conjunction with the Pufferfish optimization algorithm (named RFPu) and Red-tailed Hawk optimization models(named RFRe), for forecasting the shear strength (Pu) capacity of medium-to ultra-high-strength concrete beams featuring longitudinal reinforcement and vertical stirrups. A database that comprises 315 experimental data points from studies is employed for the reason of calculate the nine input elements for the Pu. This is done to accomplish the objective of computing the Pu. Given the available information, it is likely that both RFPu and RFRe will compute Pu correctly. Throughout the training and evaluation, the RFPu model’s good functional dependability was proved by the R2 (0.97309 and 0.96698). With learning and assessment values of 20.9673 and 27.9776, respectively, the RFPu produced the smallest results on the RMSEmeasure.


Keywords: Beam: Stirrup-reinforcement; Ultimate Shear strength; Ensembled models; PAWN methodology


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