Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning
To identify the unknown values of the parameters of Burger’s constitutive law, commonly used for the evaluation of the creep behavior of the soft soils, this paper demonstrates a procedure relying on the data obtained from multiple sensors, where each sensor is used to its best advantage. The geophy...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/8/2888 |
| _version_ | 1797409416740339712 |
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| author | Meho Saša Kovačević Mario Bačić Lovorka Librić Kenneth Gavin |
| author_facet | Meho Saša Kovačević Mario Bačić Lovorka Librić Kenneth Gavin |
| author_sort | Meho Saša Kovačević |
| collection | DOAJ |
| description | To identify the unknown values of the parameters of Burger’s constitutive law, commonly used for the evaluation of the creep behavior of the soft soils, this paper demonstrates a procedure relying on the data obtained from multiple sensors, where each sensor is used to its best advantage. The geophysical, geotechnical, and unmanned aerial vehicle data are used for the development of a numerical model whose results feed into the custom-architecture neural network, which then provides information about on the complex relationships between the creep characteristics and soil displacements. By utilizing InSAR and GPS monitoring data, particle swarm algorithm identifies the most probable set of Burger’s creep parameters, eventually providing a reliable estimation of the long-term behavior of soft soils. The validation of methodology is conducted for the Oostmolendijk embankment in the Netherlands, constructed on the soft clay and peat layers. The validation results show that the application of the proposed methodology, which relies on multisensor data, can overcome the high cost and long duration issues of laboratory tests for the determination of the creep parameters and can provide reliable estimates of the long-term behavior of geotechnical structures constructed on soft soils. |
| first_indexed | 2024-03-09T04:14:18Z |
| format | Article |
| id | doaj.art-b3e567ecbce149399ac25b1afbc8944a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:14:18Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b3e567ecbce149399ac25b1afbc8944a2023-12-03T13:56:42ZengMDPI AGSensors1424-82202022-04-01228288810.3390/s22082888Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine LearningMeho Saša Kovačević0Mario Bačić1Lovorka Librić2Kenneth Gavin3Faculty of Civil Engineering, University of Zagreb, 10000 Zagreb, CroatiaFaculty of Civil Engineering, University of Zagreb, 10000 Zagreb, CroatiaFaculty of Civil Engineering, University of Zagreb, 10000 Zagreb, CroatiaFaculty of Civil Engineering and Geosciences, TU Delft, 2628 CN Delft, The NetherlandsTo identify the unknown values of the parameters of Burger’s constitutive law, commonly used for the evaluation of the creep behavior of the soft soils, this paper demonstrates a procedure relying on the data obtained from multiple sensors, where each sensor is used to its best advantage. The geophysical, geotechnical, and unmanned aerial vehicle data are used for the development of a numerical model whose results feed into the custom-architecture neural network, which then provides information about on the complex relationships between the creep characteristics and soil displacements. By utilizing InSAR and GPS monitoring data, particle swarm algorithm identifies the most probable set of Burger’s creep parameters, eventually providing a reliable estimation of the long-term behavior of soft soils. The validation of methodology is conducted for the Oostmolendijk embankment in the Netherlands, constructed on the soft clay and peat layers. The validation results show that the application of the proposed methodology, which relies on multisensor data, can overcome the high cost and long duration issues of laboratory tests for the determination of the creep parameters and can provide reliable estimates of the long-term behavior of geotechnical structures constructed on soft soils.https://www.mdpi.com/1424-8220/22/8/2888soft soil creepBurger’s modelneural networkparticle swarm optimizationremote sensingnondestructive testing |
| spellingShingle | Meho Saša Kovačević Mario Bačić Lovorka Librić Kenneth Gavin Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning Sensors soft soil creep Burger’s model neural network particle swarm optimization remote sensing nondestructive testing |
| title | Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning |
| title_full | Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning |
| title_fullStr | Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning |
| title_full_unstemmed | Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning |
| title_short | Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning |
| title_sort | evaluation of creep behavior of soft soils by utilizing multisensor data combined with machine learning |
| topic | soft soil creep Burger’s model neural network particle swarm optimization remote sensing nondestructive testing |
| url | https://www.mdpi.com/1424-8220/22/8/2888 |
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