Compressive strength of nano concrete materials under elevated temperatures using machine learning.

Autor: Zeyad AM; Civil and Architectural Engineering Department, College of Engineering and Computer Sciences, Jazan University, Jazan 45142, Saudi Arabia., Jazan University, Jazan, Kingdom of Saudi Arabia. azmohsen@jazanu.edu.sa., Mahmoud AA; Civil Engineering Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt., El-Sayed AA; Civil Engineering Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt., Aboraya AM; Construction and Building Engineering Department, Higher Institute of Engineering, Culture & Science City, Giza, Egypt., Fathy IN; Civil Engineering Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt.; Construction and Building Engineering Department, October High Institute for Engineering & Technology, Giza, Egypt., Zygouris N; Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, 15122, Greece., Asteris PG; Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, 15122, Greece., Agwa IS; Department of Civil and Architectural Constructions, Faculty of Technology and Education, Suez University, P.O.Box: 43221, Suez, Egypt.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Oct 16; Vol. 14 (1), pp. 24246. Date of Electronic Publication: 2024 Oct 16.
DOI: 10.1038/s41598-024-73713-0
Abstrakt: In this study, four Artificial intelligence (AI) - based machine learning models were developed to estimate the Residual compressive strength (RCS) value of concrete supported with nano additives of Nanocarbon tubes (NCTs) and Nano alumina (NAl), after exposure to elevated temperatures ranging from 200 to 800 degrees. These models were developed via adapting meta- heuristic models including the Water cycle algorithm (WCA), Genetic algorithm (GA), and classical AI models of Artificial neural networks (ANNs), Fuzzy logic models (FLM), in addition to the statistical method of Multiple linear regression (MLR). 156 post heating experimental results available as a literature data (represents four input parameters of temperature change, heat exposure duration, nanomaterial type, and replacement proportion) are used to achieve the study's objective. Results of the developed models demonstrated that ANN and FLM have strong potential in predicting RCS. However, it is often infeasible to generate practical equations that relate input and output variables from these models. Upon analysing the results of the WCA and GA, it was found that WCA yielded the most accurate predictions based on all performance indicators. Furthermore, RCS prediction equations with superior accuracy were derived utilizing the meta-heuristic AI models of WCA and GA, with Mean absolute errors (MAEs) of 3.09 kg/cm² and 3.53 kg/cm² for the training, 1.91 kg/cm² and 2.72 kg/cm² for the validation, and 1.91 kg/cm² and 2.72 kg/cm² for the testing data sets, respectively. Additionally, sensitivity analysis via neural networks weights and SHAP investigation were performed to reveals the impact and relationship of the input variables with the output variables. Both techniques reveal that temperature degree and time of exposure had the highest positive impact on RCS value, followed by NAl and NCTs, in order.
(© 2024. The Author(s).)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje