prikaz prve stranice dokumenta Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning
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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

Institucijski repozitorij: Repozitorij Građevinskog fakulteta Sveučilišta u Zagrebu

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Kovačević, M. S., Bačić, M., Librić, L. i Gavin, K. (2022). Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning. Sensors, 22(8).. Preuzeto s 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 i 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). Preuzeto s: https://urn.nsk.hr/urn:nbn:hr:237:151900 (Datum pristupa: 04.10.2024.)

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. [pristupljeno 04.10.2024.];22(8). Dostupno na: https://urn.nsk.hr/urn:nbn:hr:237:151900

M. S. Kovačević, M. Bačić, L. Librić i K. Gavin, "Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning", Sensors, vol. 22(8), 2022. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:237:151900. [Citirano: 04.10.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:237:151900
Podaci o radu
Naslov (engleski)Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning
AutorMeho Saša Kovačević
AutorMario Bačić
AutorLovorka Librić
AutorKenneth Gavin
Autorova ustanovaSveučilište u Zagrebu
Građevinski fakultet
Znanstveno / umjetničko
područje, polje i grana		TEHNIČKE ZNANOSTI
Građevinarstvo
Sažetak (engleski)
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.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaSensors
Brojčani podacivol. 22(8), 2888
e-ISSN1424-8220
URN:NBNurn:nbn:hr:237:151900
Datum objave publikacije2022
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Zaštićeno autorskim pravom.
Datum i vrijeme pohrane2024-09-04 21:36:44