Civil Engineering

Mengyao Pang1, Gongxing Yan This email address is being protected from spambots. You need JavaScript enabled to view it.1, Jie Li1, and Minggui Zhou2

1School of Architectural Engineering, Chongqing Creation Vocational College, Yongchuan 402160, Chongqing, China
2School of Intelligent Construction, Luzhou vocational and technical college, Luzhou 646000, Sichuan, China

 

Received: June 29, 2022
Accepted: December 12, 2022
Publication Date: February 9, 2023

 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.202310_26(10).0008  


ABSTRACT

High-performance concrete (HPC) is a concrete model with high compressive strength (CS). The problem of compressive strength in concrete is of great importance to civil engineers, and HPC has been able to meet this demand. The employed of this type of concrete model has significant efficiency and durability. In concrete, other components are added to components containing water, cement, and aggregates. Pneumatic ash and Micro-silica are components added to this concrete to reduce the water to cement ratio and increase the compressive strength of concrete. The HPC concrete modeling in this study is done with the Radial Basis Function Neural Network (RBFNN) model of Artificial Intelligence models (AI), and this model uses a combination of two optimizers, Grasshopper Optimization Algorithm (GOA) and Marine Predators Algorithm (MPA), both algorithms are used and belong to a new initiative. The combination of the above model and the algorithms in the context of RBF-MPA and RBF-GOA gave the desired results. The maximum values of the RF parameter combination models RBFMPA and RBF-GOA are 97.4% and 97%, and the difference is 0.4%, which is significantly different and close to each other. The OBJs calculated by the RBF-MPA model and the RBF-GOA model are 2.4 and 2.61, respectively. The maximum mathematical SI parameters for each model are 0.0402 and 0.0424, which are provided as output for the training section of each section. The calculated errors in both hybrid models are acceptable and do not differ significantly from each other.


Keywords: High-Performance Concrete; Radial Basis Function; Grasshopper Optimization Algorithm; Marine Predators Algorithm; Compressive Strength


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