Performance of One-class Support Vector Machine (SVM) in Detection of Anomalies in the Bridge Data
| Main Author: | |
|---|---|
| Language: | English |
| Published: |
University of Cincinnati / OhioLINK
2017
|
| Subjects: | |
| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=ucin150478019017791 |
| id |
ndltd-OhioLink-oai-etd.ohiolink.edu-ucin150478019017791 |
|---|---|
| record_format |
oai_dc |
| spelling |
ndltd-OhioLink-oai-etd.ohiolink.edu-ucin1504780190177912021-08-03T07:04:08Z Performance of One-class Support Vector Machine (SVM) in Detection of Anomalies in the Bridge Data Dalvi, Aditi Electrical Engineering Support Vector Machine unsupervised Structural Health Monitoring anomaly detection bridge in-construction one-class SVM In a time where Structural Health Monitoring (SHM) is a topic of vital importance for safety and maintenance of critical structures such as bridges, detecting damages or anomalies as well as analyzing the normal behavior of structures has also gained significance in recent years. Data models have been increasingly used in recent years for tracking normal behavior of structures and hence detect and classify anomalies. Large numbers of machine learning algorithms were proposed by various researchers to model operational and functional changes in structures; however, a limited number of studies were applied to actual measurement data due to limited access to the long-term measurement data of structures. Structural Health Monitoring (SHM) of civil infrastructure like highway bridges, during construction or in-service use is executed at University of Cincinnati Infrastructure Institute (UCII), thus giving access to the actual measurement data of the bridges. The essence of this SHM system lies in the processing of data, where it is able to detect anomalies in the data. The current system utilizes linear regression method to detect outliers in the bridge data.This study introduces a novel anomaly detection method employing one-class Support Vector Machines (SVM) and compares the performance of SVMs with traditional regression model. This method is implemented on the measurement data of Ironton-Russell Bridge monitored by UCII, which was in-service use, and its results are compared with linear regression as a case study. The method is further implemented on Ironton-Russell Replacement Bridge which UCII has been monitoring since the construction stage. The actual construction events of the Ironton-Russell Replacement Bridge are being used as validation for the comparison. The aim is to show advantages of employing SVMs due to their abilities to classify damages even with minimum training data. The results show that using SVMs will improve the detectability and also the intermediate results help to identify the severity of an outlier. 2017 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin150478019017791 http://rave.ohiolink.edu/etdc/view?acc_num=ucin150478019017791 unrestricted This thesis or dissertation is protected by copyright: some rights reserved. It is licensed for use under a Creative Commons license. Specific terms and permissions are available from this document's record in the OhioLINK ETD Center. |
| collection |
NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Electrical Engineering Support Vector Machine unsupervised Structural Health Monitoring anomaly detection bridge in-construction one-class SVM |
| spellingShingle |
Electrical Engineering Support Vector Machine unsupervised Structural Health Monitoring anomaly detection bridge in-construction one-class SVM Dalvi, Aditi Performance of One-class Support Vector Machine (SVM) in Detection of Anomalies in the Bridge Data |
| author |
Dalvi, Aditi |
| author_facet |
Dalvi, Aditi |
| author_sort |
Dalvi, Aditi |
| title |
Performance of One-class Support Vector Machine (SVM) in Detection of Anomalies in the Bridge Data |
| title_short |
Performance of One-class Support Vector Machine (SVM) in Detection of Anomalies in the Bridge Data |
| title_full |
Performance of One-class Support Vector Machine (SVM) in Detection of Anomalies in the Bridge Data |
| title_fullStr |
Performance of One-class Support Vector Machine (SVM) in Detection of Anomalies in the Bridge Data |
| title_full_unstemmed |
Performance of One-class Support Vector Machine (SVM) in Detection of Anomalies in the Bridge Data |
| title_sort |
performance of one-class support vector machine (svm) in detection of anomalies in the bridge data |
| publisher |
University of Cincinnati / OhioLINK |
| publishDate |
2017 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=ucin150478019017791 |
| work_keys_str_mv |
AT dalviaditi performanceofoneclasssupportvectormachinesvmindetectionofanomaliesinthebridgedata |
| _version_ |
1719452904160493568 |