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Concrete strength and durability prediction through deep learning and artificial neural networks

Frontiers of Structural and Civil Engineering 2024年第10期18卷 1540-1555页

作者： Maedeh HOSSEINZADEH hojjat samadvand Alireza HOSSEINZADEH Seyed Sina MOUSAVI Mehdi DEHESTANI Faculty of Civil Engineering Babol Noshirvani University of TechnologyBabol 47148-71167Iran Department of Civil Engineering Urmia UniversityUrmia 57561-51818Iran

The mechanical and durability characteristics of concrete are crucial for designing and evaluating concrete structures throughout their entire operational *** main objective of this research is to use the deep learning(DL)method along with an artificial neural network(ANN)to predict the chloride migration coefficient and concrete compressive *** expansive experimental database of nearly 1100 data points was gathered from existing scientific *** forecast models were created,utilizing between 10 and 12 input *** ANN was used to address the missing data gaps in the literature.A comprehensive pre-processing approach was then implemented to identify outliers and encode data *** use of mean absolute error(MAE)as an evaluation metric for regression tasks and the employment of a confusion matrix for classification tasks were found to produce accurate ***,both the compressive strength and chloride migration coefficient exhibit a high level of accuracy,above 0.85,in both regression and classification ***,a user-friendly web application was successfully developed in the present study using the Python programming language,improving the ability to integrate smoothly with the user’s device.

关键词： chloride migration coefficient compressive strength concrete artificial neural network deep learning

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