Novel digital-twin modelling for smart building inspection
Buildings are everywhere in modern society. They make up the bulk of the structures in modern civilization. As time goes on, buildings get increasingly taller. Since buildings take up most of the space in our cities, there is a necessity to maintain the structural integrity of these buildings. St...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/177523 |
| _version_ | 1811683400346501120 |
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| author | Mui, Jia Duan |
| author2 | Fu Yuguang |
| author_facet | Fu Yuguang Mui, Jia Duan |
| author_sort | Mui, Jia Duan |
| collection | NTU |
| description | Buildings are everywhere in modern society. They make up the bulk of the structures in modern
civilization. As time goes on, buildings get increasingly taller. Since buildings take up most of the
space in our cities, there is a necessity to maintain the structural integrity of these buildings. Structural
Health Monitoring is a process where various key data points are used to evaluate the overall health
of the structure. These data points such as strain, acceleration and displacement are measured and
recorded through sensors placed throughout the structure. With these data points, Structural Health
Monitoring aims to detect and make decisions about the structure’s condition either in real time or
over a certain duration. This decisions can help improve the safety of the structure, prevent any
potential minor or major failures and help prolong the structure’s lifespan. This Final Year Project
aims to explore combining this aspect of Structural Health Monitoring with deep learning neural
networks in hopes to provide real time predictions to a building’s structural responses. Three different
architectures of neural networks will be used in this Final Year Project. The three different model
architectures used are MLP, CNN and LSTM. Each of these models were trained using actual recorded
building responses as data point inputs and evaluated by comparing the predicted data point outputs
against the actual measured data points. The data points recorded were strain, acceleration and
displacement on various floors and columns of a proposed physical laboratory model of a building. A
total of 6 batches of data points were collected. In the first batch, the 3 different data points of each
floor of a 6 story laboratory model were recorded. In subsequent rounds, the top most floor was
removed from the model. In the final batch, the data points of a 1 story laboratory model were recorded.
The strain and acceleration data points were used as the training inputs while all 3 data points were
used as the predictive output of the different neural network models. The MLP, CNN and LSTM
models were trained and evaluated for 4 different prediction rounds. After a comparison of the different
neural network architectures, the CNN model was selected as most adept at predicting any of the 3
datasets given different strain and acceleration inputs from various floors. The results of this project
hope to be combined with a real time Structural Health Monitoring program where an actual life sized
building may become the intended proposed structure. The predictive prowess of the neural networks
may be used to create and update the parameters for a digital structural model with the end goal of
creating a digital twin of the life sized building. |
| first_indexed | 2024-10-01T04:12:08Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/177523 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:12:08Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1775232024-05-31T15:35:04Z Novel digital-twin modelling for smart building inspection Mui, Jia Duan Fu Yuguang School of Civil and Environmental Engineering yuguang.fu@ntu.edu.sg Engineering Neural networks Structural health monitoring Building responses Digital twin Machine learning Buildings are everywhere in modern society. They make up the bulk of the structures in modern civilization. As time goes on, buildings get increasingly taller. Since buildings take up most of the space in our cities, there is a necessity to maintain the structural integrity of these buildings. Structural Health Monitoring is a process where various key data points are used to evaluate the overall health of the structure. These data points such as strain, acceleration and displacement are measured and recorded through sensors placed throughout the structure. With these data points, Structural Health Monitoring aims to detect and make decisions about the structure’s condition either in real time or over a certain duration. This decisions can help improve the safety of the structure, prevent any potential minor or major failures and help prolong the structure’s lifespan. This Final Year Project aims to explore combining this aspect of Structural Health Monitoring with deep learning neural networks in hopes to provide real time predictions to a building’s structural responses. Three different architectures of neural networks will be used in this Final Year Project. The three different model architectures used are MLP, CNN and LSTM. Each of these models were trained using actual recorded building responses as data point inputs and evaluated by comparing the predicted data point outputs against the actual measured data points. The data points recorded were strain, acceleration and displacement on various floors and columns of a proposed physical laboratory model of a building. A total of 6 batches of data points were collected. In the first batch, the 3 different data points of each floor of a 6 story laboratory model were recorded. In subsequent rounds, the top most floor was removed from the model. In the final batch, the data points of a 1 story laboratory model were recorded. The strain and acceleration data points were used as the training inputs while all 3 data points were used as the predictive output of the different neural network models. The MLP, CNN and LSTM models were trained and evaluated for 4 different prediction rounds. After a comparison of the different neural network architectures, the CNN model was selected as most adept at predicting any of the 3 datasets given different strain and acceleration inputs from various floors. The results of this project hope to be combined with a real time Structural Health Monitoring program where an actual life sized building may become the intended proposed structure. The predictive prowess of the neural networks may be used to create and update the parameters for a digital structural model with the end goal of creating a digital twin of the life sized building. Bachelor's degree 2024-05-28T08:02:32Z 2024-05-28T08:02:32Z 2024 Final Year Project (FYP) Mui, J. D. (2024). Novel digital-twin modelling for smart building inspection. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177523 https://hdl.handle.net/10356/177523 en ST-02 application/pdf Nanyang Technological University |
| spellingShingle | Engineering Neural networks Structural health monitoring Building responses Digital twin Machine learning Mui, Jia Duan Novel digital-twin modelling for smart building inspection |
| title | Novel digital-twin modelling for smart building inspection |
| title_full | Novel digital-twin modelling for smart building inspection |
| title_fullStr | Novel digital-twin modelling for smart building inspection |
| title_full_unstemmed | Novel digital-twin modelling for smart building inspection |
| title_short | Novel digital-twin modelling for smart building inspection |
| title_sort | novel digital twin modelling for smart building inspection |
| topic | Engineering Neural networks Structural health monitoring Building responses Digital twin Machine learning |
| url | https://hdl.handle.net/10356/177523 |
| work_keys_str_mv | AT muijiaduan noveldigitaltwinmodellingforsmartbuildinginspection |