{"title":"Prediction of freeze–thaw and thermal shock weathering on natural stones through deep learning-based algorithms","authors":"A. Sakcali","doi":"10.1007/s10064-024-03961-x","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":500,"journal":{"name":"Bulletin of Engineering Geology and the Environment","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bulletin of Engineering Geology and the Environment","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s10064-024-03961-x","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Engineering geology is defined in the statutes of the IAEG as the science devoted to the investigation, study and solution of engineering and environmental problems which may arise as the result of the interaction between geology and the works or activities of man, as well as of the prediction of and development of measures for the prevention or remediation of geological hazards. Engineering geology embraces:
• the applications/implications of the geomorphology, structural geology, and hydrogeological conditions of geological formations;
• the characterisation of the mineralogical, physico-geomechanical, chemical and hydraulic properties of all earth materials involved in construction, resource recovery and environmental change;
• the assessment of the mechanical and hydrological behaviour of soil and rock masses;
• the prediction of changes to the above properties with time;
• the determination of the parameters to be considered in the stability analysis of engineering works and earth masses.