prikaz prve stranice dokumenta Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning
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Scientific paper - Original scientific paper
Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning
Sensors, 22(8) (2022); 2888. urn:nbn:hr:237:151900
Kovačević, Meho Saša; Bačić, Mario; Librić, Lovorka; Gavin, Kenneth

Institutional repository: Repository of the Faculty of Civil Engineering, University of Zagreb

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Kovačević, M. S., Bačić, M., Librić, L. & Gavin, K. (2022). Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning. Sensors, 22(8).. Retrieved from https://urn.nsk.hr/urn:nbn:hr:237:151900

Kovačević, Meho Saša, et al. "Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning." Sensors, vol. 22(8), 2022. https://urn.nsk.hr/urn:nbn:hr:237:151900

Kovačević, Meho Saša, Mario Bačić, Lovorka Librić and Kenneth Gavin. "Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning." Sensors 22(8) (2022). https://urn.nsk.hr/urn:nbn:hr:237:151900

Kovačević, M. S., et al. (2022) 'Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning', Sensors, 22(8). Available at: https://urn.nsk.hr/urn:nbn:hr:237:151900 (Accessed 25 December 2024)

Kovačević MS, Bačić M, Librić L, Gavin K. Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning. Sensors [Internet]. 2022 [cited 2024 December 25];22(8). Available at: https://urn.nsk.hr/urn:nbn:hr:237:151900

M. S. Kovačević, M. Bačić, L. Librić and K. Gavin, "Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning", Sensors, vol. 22(8), 2022. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:237:151900. [Accessed: 25 December 2024]

Metadata
Title (english)Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning
AuthorMeho Saša Kovačević
AuthorMario Bačić
AuthorLovorka Librić
AuthorKenneth Gavin
Author's institutionUniversity of Zagreb
Faculty of Civil Engineering
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Civil Engineering
Abstract (english)
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.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleSensors
Numberingvol. 22(8), 2888
e-ISSN1424-8220
URN:NBNurn:nbn:hr:237:151900
Publication2022
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-09-04 21:36:44