Multiple damage detection in sandwich composite structures with lattice core using regression-based machine learning techniques DOI
Malihe Avarzamani,

Majid Ghazali,

Morteza Karamooz Mahdiabadi

и другие.

Mechanics Based Design of Structures and Machines, Год журнала: 2024, Номер unknown, С. 1 - 25

Опубликована: Окт. 28, 2024

The abstract discusses the importance of Structural Health Monitoring (SHM) in ensuring reliability and health structures. It introduces a novel approach for recognizing cracks delamination sandwich composite structures using advanced methods data analysis machine learning algorithms. research leverages artificial intelligence to accurately identify locate damage such as layers, which can significantly enhance troubleshooting performance, improve detection accuracy, reduce time cost associated with repairs. article presents technique utilizing regression lattice core, capable identifying locating multiple delamination, well simultaneous defects structure. is conducted based on structure's healthy damaged conditions Abaqus software, acceleration responses under random forces obtained through finite element method were used train various models, including algorithms like k-Nearest Neighborhood Regression (KNN), Light Gradient Boost Machine (LGBM), Decision Tree (DTR) detect location. results indicate that neighborhood, decision tree most successful functions, respectively, classification models identified its location structure, achieving an accuracy rate approximately 98.8%.

Язык: Английский

Multiple damage detection in sandwich composite structures with lattice core using regression-based machine learning techniques DOI
Malihe Avarzamani,

Majid Ghazali,

Morteza Karamooz Mahdiabadi

и другие.

Mechanics Based Design of Structures and Machines, Год журнала: 2024, Номер unknown, С. 1 - 25

Опубликована: Окт. 28, 2024

The abstract discusses the importance of Structural Health Monitoring (SHM) in ensuring reliability and health structures. It introduces a novel approach for recognizing cracks delamination sandwich composite structures using advanced methods data analysis machine learning algorithms. research leverages artificial intelligence to accurately identify locate damage such as layers, which can significantly enhance troubleshooting performance, improve detection accuracy, reduce time cost associated with repairs. article presents technique utilizing regression lattice core, capable identifying locating multiple delamination, well simultaneous defects structure. is conducted based on structure's healthy damaged conditions Abaqus software, acceleration responses under random forces obtained through finite element method were used train various models, including algorithms like k-Nearest Neighborhood Regression (KNN), Light Gradient Boost Machine (LGBM), Decision Tree (DTR) detect location. results indicate that neighborhood, decision tree most successful functions, respectively, classification models identified its location structure, achieving an accuracy rate approximately 98.8%.

Язык: Английский

Процитировано

0