Hybrid finite element and machine learning approach for covered linkway sheltering factor assessment

The proliferation of Unmanned Aerial Vehicle (UAV) technology has resulted in an increase in UAV-related incidents, raising concerns about potential collisions with covered linkways. However, the protective capabilities of these linkways against UAV impacts remain uncertain. Therefore, the objective...

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Main Author: Tok, Wei Sheng
Other Authors: Mir Feroskhan
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/177581
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1775812024-06-01T16:53:11Z Hybrid finite element and machine learning approach for covered linkway sheltering factor assessment Tok, Wei Sheng Mir Feroskhan School of Mechanical and Aerospace Engineering mir.feroskhan@ntu.edu.sg Engineering Covered linkway Sheltering factor Machine learning Finite element The proliferation of Unmanned Aerial Vehicle (UAV) technology has resulted in an increase in UAV-related incidents, raising concerns about potential collisions with covered linkways. However, the protective capabilities of these linkways against UAV impacts remain uncertain. Therefore, the objective of this study is to conduct a comprehensive evaluation of the covered linkway sheltering factor. Given the computationally intensive nature of Finite Element Analysis (FEA), this research explores the feasibility of adopting a hybrid approach that integrates FEA with machine learning techniques to enhance efficiency and accuracy in assessing sheltering factors. The research methodology is structured into four distinct phases. In Phase 1, FEA is employed to simulate UAV impacts on covered linkways, generating an initial dataset using mass and velocity inputs. In Phase 2, a Gaussian Process (GP) model is applied as a machine learning technique to predict energy absorption values based on the FEA dataset. In Phase 3, deep learning methodologies are integrated into the assessment process. An Artificial Neural Network (ANN) model is implemented to predict penetration outcomes of UAV impacts, while an ANN-Convolutional Neural Network (CNN) model is used to predict FEA deformation images resulting from these impacts. Finally, in Phase 4, spatial data analysis technique is applied to compute the overall sheltering factor within specific subzones in Singapore. The main results of this study indicate promising performance of the hybrid FEA-machine learning approach. The GP model achieved a Root Mean Squared Error (RMSE) of 953, demonstrating its ability to predict energy absorption values with reasonable accuracy. The ANN model exhibited a notable accuracy rate of 90% in predicting penetration outcomes, while the ANN-CNN model achieved a Mean Squared Error (MSE) of 0.05 in predicting deformation images resulting from UAV impacts. In conclusion, this research underscores the potential of integrating computational simulations with advanced machine learning techniques to assess the sheltering factor of covered linkways against UAV collisions. The findings provide valuable insights for engineering analysis and design, contributing to improved understanding and design optimization of protective structures in UAV-prone environments. Bachelor's degree 2024-05-30T07:10:26Z 2024-05-30T07:10:26Z 2024 Final Year Project (FYP) Tok, W. S. (2024). Hybrid finite element and machine learning approach for covered linkway sheltering factor assessment. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177581 https://hdl.handle.net/10356/177581 en C034 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Covered linkway
Sheltering factor
Machine learning
Finite element
spellingShingle Engineering
Covered linkway
Sheltering factor
Machine learning
Finite element
Tok, Wei Sheng
Hybrid finite element and machine learning approach for covered linkway sheltering factor assessment
description The proliferation of Unmanned Aerial Vehicle (UAV) technology has resulted in an increase in UAV-related incidents, raising concerns about potential collisions with covered linkways. However, the protective capabilities of these linkways against UAV impacts remain uncertain. Therefore, the objective of this study is to conduct a comprehensive evaluation of the covered linkway sheltering factor. Given the computationally intensive nature of Finite Element Analysis (FEA), this research explores the feasibility of adopting a hybrid approach that integrates FEA with machine learning techniques to enhance efficiency and accuracy in assessing sheltering factors. The research methodology is structured into four distinct phases. In Phase 1, FEA is employed to simulate UAV impacts on covered linkways, generating an initial dataset using mass and velocity inputs. In Phase 2, a Gaussian Process (GP) model is applied as a machine learning technique to predict energy absorption values based on the FEA dataset. In Phase 3, deep learning methodologies are integrated into the assessment process. An Artificial Neural Network (ANN) model is implemented to predict penetration outcomes of UAV impacts, while an ANN-Convolutional Neural Network (CNN) model is used to predict FEA deformation images resulting from these impacts. Finally, in Phase 4, spatial data analysis technique is applied to compute the overall sheltering factor within specific subzones in Singapore. The main results of this study indicate promising performance of the hybrid FEA-machine learning approach. The GP model achieved a Root Mean Squared Error (RMSE) of 953, demonstrating its ability to predict energy absorption values with reasonable accuracy. The ANN model exhibited a notable accuracy rate of 90% in predicting penetration outcomes, while the ANN-CNN model achieved a Mean Squared Error (MSE) of 0.05 in predicting deformation images resulting from UAV impacts. In conclusion, this research underscores the potential of integrating computational simulations with advanced machine learning techniques to assess the sheltering factor of covered linkways against UAV collisions. The findings provide valuable insights for engineering analysis and design, contributing to improved understanding and design optimization of protective structures in UAV-prone environments.
author2 Mir Feroskhan
author_facet Mir Feroskhan
Tok, Wei Sheng
format Final Year Project
author Tok, Wei Sheng
author_sort Tok, Wei Sheng
title Hybrid finite element and machine learning approach for covered linkway sheltering factor assessment
title_short Hybrid finite element and machine learning approach for covered linkway sheltering factor assessment
title_full Hybrid finite element and machine learning approach for covered linkway sheltering factor assessment
title_fullStr Hybrid finite element and machine learning approach for covered linkway sheltering factor assessment
title_full_unstemmed Hybrid finite element and machine learning approach for covered linkway sheltering factor assessment
title_sort hybrid finite element and machine learning approach for covered linkway sheltering factor assessment
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/177581
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