Predicting Depth of Concrete Carbonation through Machine Learning and Optimization: Investigation of Influential Factors.
Publish place: The first international conference on the exchange of scientific information in the field of concrete materials and structures
Publish Year: 1403
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICCNC01_048
تاریخ نمایه سازی: 19 خرداد 1403
Abstract:
Accurate prediction of the depth of concrete carbonation is paramount in safeguarding againstdetrimental outcomes like cracking and corrosion. Nevertheless, due to the intricacies of the process andthe multitude of variables at play, discerning the critical parameters that hold the utmost significance inmodeling concrete carbonation depth poses formidable challenges. This paper presents the development ofa novel feature selection method called MOEA/D-ANN. The objective of this method is to identify the mostcrucial variables that contribute to achieving the highest prediction accuracy. The proposed approachcombines the MOEA/D optimization algorithm with Artificial Neural Networks (ANN) to effectively solvethe feature selection problem by leveraging the power of both optimization and machine learningtechniques. To evaluate the performance of the introduced technique, a conventional feature selectionmethod, Regression Relief Feature Selection (RReliefF), was also employed. The ANN method wasemployed to predict the depth of concrete carbonation, and separate models were developed using theinfluential variables identified by the MOEA/D-ANN and RReliefF methods. The findings reveal that themodel created using the MOEA/D-ANN approach, incorporating variables determined by it, demonstratessignificantly reduced errors. Moreover, the model attains an impressive R۲ value of ۰.۹۹, underscoring itsexceptional precision in forecasting the depth of concrete carbonation and validating the meticulousselection of influential variables.
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Authors
Fereidoon Moghadas Nejad
Department of Civil & Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic),Tehran, Iran,
Mehrdad Ehsani
Department of Civil & Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic),Tehran, Iran