Prediction of freeze–thaw and thermal shock weathering on natural stones through deep learning-based algorithms

Natural stones used as construction materials in outdoor applications and the rock environment in rock engineering applications are subject to weakening such as freeze-thaw (F-T) and thermal shock (TS) due to weather conditions. Predicting the mechanical effects of F-T and TS weathering is important...

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Vydáno v:Bulletin of engineering geology and the environment Ročník 83; číslo 12; s. 485
Hlavní autor: Sakcali, A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024
Springer Nature B.V
Témata:
Algorithms
Compressive strength
Decision trees
Deep learning
Earth and Environmental Science
Earth Sciences
Elastic analysis
Elastic properties
Factories
Foundations
Freeze-thaw
Geoecology/Natural Processes
Geoengineering
Geology
Geotechnical Engineering & Applied Earth Sciences
Hydraulics
Initial conditions
Machine learning
Marble
Mechanical properties
Natural stone
Nature Conservation
Numerical models
Original Paper
P waves
Physical properties
Pore size
Porosity
Predictions
Rock
Rocks
Sediment samples
Seismic wave velocities
Stone
Stress distribution
Thermal shock
Water absorption
Wave velocity
Weather
Weather conditions
Weathering
Turkey
Deep learning
Freeze–thaw
Natural stone
AdaBoost
Thermal shock
ISSN:1435-9529, 1435-9537
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Popis
Shrnutí:Natural stones used as construction materials in outdoor applications and the rock environment in rock engineering applications are subject to weakening such as freeze-thaw (F-T) and thermal shock (TS) due to weather conditions. Predicting the mechanical effects of F-T and TS weathering is important for the design on rock engineering. While the change in mechanical properties can be determined by F-T and TS simulating with experimental studies, it can also be predicted with simple models in the literature and determining initial conditions. While the properties of weakened rock are determined from the models proposed in the literature, a rock-specific experimental study is needed and precise results cannot be obtained. Instead, the predicting of F-T and TS weathering on rocks by using deep learning-based algorithms enables better data for design. In this study, the effects of deterioration on physical and mechanical properties of natural stones after F-T and TS weathering was investigated with experimental simulation. The samples of 15 different rock type were subjected to F-T and TS process for 15, 30 and 45 cycles following standard methods in experimental study. The changes of apparent porosity, water absorption by weight, P-wave velocity, uniaxial compressive strength and elastic module of rocks after each process were investigated and analysed with different deep learning algorithms to predict these properties. It has been determined that AdaBoost is the best algorithm for predicting the properties of natural stone after F-T and TS weathering. Additionally, the stress distribution was modelled numerically to investigate the effect of F-T and TS weathering on rock samples. The study shows that deep learning-based algorithms can be used as an auxiliary tool in prediction in order to perform more precise studies.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1435-9529
1435-9537
DOI:10.1007/s10064-024-03961-x