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

IF 3.7 2区 工程技术 Q3 ENGINEERING, ENVIRONMENTAL Bulletin of Engineering Geology and the Environment Pub Date : 2024-11-09 DOI:10.1007/s10064-024-03961-x
A. Sakcali
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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.

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通过基于深度学习的算法预测天然石材的冻融和热冲击风化现象
在户外应用中作为建筑材料使用的天然石材以及在岩石工程应用中的岩石环境都会因天气条件而受到冻融(F-T)和热冲击(TS)等削弱。预测冻融(F-T)和热冲击(TS)风化的机械效应对于岩石工程设计非常重要。虽然力学性能的变化可以通过 F-T 和 TS 模拟实验研究来确定,但也可以通过文献中的简单模型和确定初始条件来预测。虽然根据文献中提出的模型可以确定弱化岩石的特性,但需要针对具体岩石进行实验研究,因此无法获得精确的结果。相反,利用基于深度学习的算法预测岩石的 F-T 和 TS 风化,可以为设计提供更好的数据。在本研究中,通过实验模拟研究了 F-T 和 TS 风化后对天然石材物理和机械性能劣化的影响。按照实验研究的标准方法,对 15 种不同类型的岩石样本进行了 15、30 和 45 个循环的 F-T 和 TS 过程。采用不同的深度学习算法来预测岩石的表观孔隙率、重量吸水率、P 波速度、单轴抗压强度和弹性模量的变化。结果表明,AdaBoost 是预测 F-T 和 TS 风化后天然石材属性的最佳算法。此外,还对应力分布进行了数值模拟,以研究 F-T 和 TS 风化对岩石样本的影响。研究表明,基于深度学习的算法可用作预测的辅助工具,以进行更精确的研究。
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来源期刊
Bulletin of Engineering Geology and the Environment
Bulletin of Engineering Geology and the Environment 工程技术-地球科学综合
CiteScore
7.10
自引率
11.90%
发文量
445
审稿时长
4.1 months
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.
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