Deep learning-based construction activity classification
Construction activities often produce excessive and prolonged vibrations that can be detrimental to adjacent infrastructure, equipment, as well as people. To mitigate the negative effects of construction-induced vibrations, vibration monitoring is usually implemented to analyse the impacts of the vi...
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Nanyang Technological University
2024
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sg-ntu-dr.10356-1772902024-05-24T15:34:55Z Deep learning-based construction activity classification Lian, Si Hui Fu Yuguang School of Civil and Environmental Engineering yuguang.fu@ntu.edu.sg Engineering Civil engineering Machine learning Deep learning CNN Construction activities often produce excessive and prolonged vibrations that can be detrimental to adjacent infrastructure, equipment, as well as people. To mitigate the negative effects of construction-induced vibrations, vibration monitoring is usually implemented to analyse the impacts of the vibrations. However, these vibration data collected were mostly not fully utilised due to the lack of information such as labelling of data. Therefore, this study aims to collect vibration data on various construction activities followed by developing a deep learning (DL) algorithm to recognise the different construction activities. The classification of construction activity was performed by adopting a convolutional neural network. Bachelor's degree 2024-05-24T07:20:52Z 2024-05-24T07:20:52Z 2024 Final Year Project (FYP) Lian, S. H. (2024). Deep learning-based construction activity classification. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177290 https://hdl.handle.net/10356/177290 en CT-02 application/pdf Nanyang Technological University |
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Engineering Civil engineering Machine learning Deep learning CNN Lian, Si Hui Deep learning-based construction activity classification |
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Construction activities often produce excessive and prolonged vibrations that can be detrimental to adjacent infrastructure, equipment, as well as people. To mitigate the negative effects of construction-induced vibrations, vibration monitoring is usually implemented to analyse the impacts of the vibrations. However, these vibration data collected were mostly not fully utilised due to the lack of information such as labelling of data. Therefore, this study aims to collect vibration data on various construction activities followed by developing a deep learning (DL) algorithm to recognise the different construction activities. The classification of construction activity was performed by adopting a convolutional neural network. |
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Fu Yuguang |
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Fu Yuguang Lian, Si Hui |
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Final Year Project |
author |
Lian, Si Hui |
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Lian, Si Hui |
title |
Deep learning-based construction activity classification |
title_short |
Deep learning-based construction activity classification |
title_full |
Deep learning-based construction activity classification |
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Deep learning-based construction activity classification |
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Deep learning-based construction activity classification |
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deep learning-based construction activity classification |
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Nanyang Technological University |
publishDate |
2024 |
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https://hdl.handle.net/10356/177290 |
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