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A platform for research: civil engineering, architecture and urbanism
Bridge Damage Detection Using Passing-By Vehicles and CNN-LSTM Autoencoder
https://orcid.org/http://orcid.org/0000-0001-5916-8981
https://orcid.org/http://orcid.org/0000-0003-1409-3562
This paper presents an autoencoder network designed to detect the severity of bridge damage using vibration signals obtained from passing vehicles. While one signal from a single vehicle may only contain limited information about the structures, a large dataset with hundreds or thousands of signals can provide a substantial amount of information. The network is composed of an encoder with a convolutional layer and a long short-term memory (LSTM) layer and a decoder constructed with a fully connected (FC) layer to regenerate the initial input. In our approach, the particular novelty that sets it apart is the fact that we highlight the utilization of solely acceleration signals from intact bridges for training data, as intentionally damaging bridges to obtain signals for damaged conditions is infeasible and unethical in real-world scenarios. To evaluate the results, a metric called the variance-weighted coefficient of determination (R2) is used to measure the goodness of the reconstruction of input signals and determine the damage severity of the bridge. The testing results indicate a strong linear relationship between the damage level and R2. It should be noted that the vibration signals were collected from closed-form equations and numerical models with different combinations of bridge damage levels and vehicle properties. However, with the rapid growth of smartphones and data transmission technologies, the work can be extended to large amounts of real bridge data using such devices.
Bridge Damage Detection Using Passing-By Vehicles and CNN-LSTM Autoencoder
https://orcid.org/http://orcid.org/0000-0001-5916-8981
https://orcid.org/http://orcid.org/0000-0003-1409-3562
This paper presents an autoencoder network designed to detect the severity of bridge damage using vibration signals obtained from passing vehicles. While one signal from a single vehicle may only contain limited information about the structures, a large dataset with hundreds or thousands of signals can provide a substantial amount of information. The network is composed of an encoder with a convolutional layer and a long short-term memory (LSTM) layer and a decoder constructed with a fully connected (FC) layer to regenerate the initial input. In our approach, the particular novelty that sets it apart is the fact that we highlight the utilization of solely acceleration signals from intact bridges for training data, as intentionally damaging bridges to obtain signals for damaged conditions is infeasible and unethical in real-world scenarios. To evaluate the results, a metric called the variance-weighted coefficient of determination (R2) is used to measure the goodness of the reconstruction of input signals and determine the damage severity of the bridge. The testing results indicate a strong linear relationship between the damage level and R2. It should be noted that the vibration signals were collected from closed-form equations and numerical models with different combinations of bridge damage levels and vehicle properties. However, with the rapid growth of smartphones and data transmission technologies, the work can be extended to large amounts of real bridge data using such devices.
Bridge Damage Detection Using Passing-By Vehicles and CNN-LSTM Autoencoder
https://orcid.org/http://orcid.org/0000-0001-5916-8981
https://orcid.org/http://orcid.org/0000-0003-1409-3562
This paper presents an autoencoder network designed to detect the severity of bridge damage using vibration signals obtained from passing vehicles. While one signal from a single vehicle may only contain limited information about the structures, a large dataset with hundreds or thousands of signals can provide a substantial amount of information. The network is composed of an encoder with a convolutional layer and a long short-term memory (LSTM) layer and a decoder constructed with a fully connected (FC) layer to regenerate the initial input. In our approach, the particular novelty that sets it apart is the fact that we highlight the utilization of solely acceleration signals from intact bridges for training data, as intentionally damaging bridges to obtain signals for damaged conditions is infeasible and unethical in real-world scenarios. To evaluate the results, a metric called the variance-weighted coefficient of determination (R2) is used to measure the goodness of the reconstruction of input signals and determine the damage severity of the bridge. The testing results indicate a strong linear relationship between the damage level and R2. It should be noted that the vibration signals were collected from closed-form equations and numerical models with different combinations of bridge damage levels and vehicle properties. However, with the rapid growth of smartphones and data transmission technologies, the work can be extended to large amounts of real bridge data using such devices.
Bridge Damage Detection Using Passing-By Vehicles and CNN-LSTM Autoencoder
Lecture Notes in Civil Engineering
Desjardins, Serge (editor) / Poitras, Gérard J. (editor) / El Damatty, Ashraf (editor) / Elshaer, Ahmed (editor) / Zeng, Jiangyu (author) / Gül, Mustafa (author) / Mei, Qipei (author)
Canadian Society of Civil Engineering Annual Conference ; 2023 ; Moncton, NB, Canada
Proceedings of the Canadian Society for Civil Engineering Annual Conference 2023, Volume 13 ; Chapter: 26 ; 327-335
2024-09-03
9 pages
Article/Chapter (Book)
Electronic Resource
English
| Springer Verlag | 2024
| Springer Verlag | 2024