Prediction of Cover–Subsidence Sinkhole Volume Using Fibre Bragg Grating Strain Sensor Data
Sinkholes are geohazards that commonly form in karstifiable terrain and are an ever-present danger to infrastructure and human life. This paper aims to answer the question: Can a cover–subsidence sinkhole’s volume be determined using fibre Bragg grating sensor strain data and machine-learning techni...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/7/2272 |
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| Summary: | Sinkholes are geohazards that commonly form in karstifiable terrain and are an ever-present danger to infrastructure and human life. This paper aims to answer the question: Can a cover–subsidence sinkhole’s volume be determined using fibre Bragg grating sensor strain data and machine-learning techniques? Exploratory data analysis was conducted on fibre Bragg grating sensor strain data collected from an experimental test rig whereby a cover–subsidence sinkhole was formed. It was found that statistical techniques and machine-learning algorithms that assume normality are inappropriate when performing phase classification and volume regression tasks on the cover–subsidence sinkhole when given fibre Bragg grating sensor’s strain data. Weighted Least Squares regression, Support Vector Regression, and eXtreme Gradient Boosting were implemented on the data during phase two of the cover–subsidence sinkhole formation to determine the volume of the sinkhole. Weighted Least Squares regression obtained the lowest <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> values for training and testing. Support Vector Regression had significantly improved results over Weighted Least Squares regression, while eXtreme Gradient Boosting obtained the highest <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> values for training and testing. The highest <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> values for eXtreme Gradient Boosting obtained were <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.00</mn></mrow></semantics></math></inline-formula> for training and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.97</mn></mrow></semantics></math></inline-formula> for testing. In addition, eXtreme Gradient Boosting had the lowest root mean squared errors compared to Weighted Least Squares regression and Support Vector Regression. It was found that eXtreme Gradient Boosting is a strong candidate for determining the volume of the C–S sinkhole when using fibre Bragg grating strain data. |
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| ISSN: | 1424-8220 |