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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Veröffentlicht in:	Bulletin of engineering geology and the environment Jg. 83; H. 12; S. 485
1. Verfasser:	Sakcali, A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
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