An adaptive prediction method for mechanical properties deterioration of sandstone under freeze-thaw cycles: a case study of Yungang Grottoes

doi:10.1186/s40494-021-00628-8

	An adaptive prediction method for mechanical properties deterioration of sandstone under freeze-thaw cycles: a case study of Yungang Grottoes
	Liu, Chenchen 1,2; Liu, Yibiao 1,2; Ren, Weizhong 1,2; Xu, Wenhui 1,2; Cai, Simin 1,2; Wang, Junxia 2
刊名	HERITAGE SCIENCE
	2021-11-27
卷号	9 期号:1 页码:11
关键词	Freeze-thaw cycle Mechanical property parameters Yungang Grottoes Gradient boosting decision trees Sandstone
ISSN号	2050-7445
DOI	10.1186/s40494-021-00628-8
英文摘要	Due to the location of the Yungang Grottoes, freeze-thaw cycles contribute significantly to the degradation of the mechanical properties of the sandstone. The factors influencing the freeze-thaw cycle are classified into two categories: external environmental conditions and the inherent properties of the rock itself. Since the parameters of rock properties are inherent to each rock, the effect of rock properties on freeze-thaw degradation cannot be investigated by the control variates method. An adaptive multi-output gradient boosting decision trees (AMGBDT) algorithm is proposed to fit nonlinear relationships between mechanical properties and physical factors. The hyperparameters in the GBDT algorithm are set as variables, and the Sequential quadratic programming (SQP) algorithm is applied to solve the hyperparameter optimization, which means finding the maximum Score. The case study illustrates that the AMGBDT algorithm can precisely determine the effect of each independent factor on the output. The patterns of mechanical properties are similar when the number of freeze-thaw cycles and porosity are used as variables separately and when both are used simultaneously. The uniaxial compressive strength decay rate is positively correlated with the number of freeze-thaw cycles and porosity. The modulus of elasticity is negatively correlated with the number of freeze-thaw cycles and porosity. The results show that the number of freeze-thaw cycles is the main factor influencing the freeze-thaw cycling action, and the porosity is minor. In addition, the fitting accuracy of the AMGBDT algorithm is generally higher than neural networks (NN) and random forests (RF). Studying the influence of porosity and other rock properties on the freeze-thaw cycle will help to understand the failure mechanism of rock freeze-thaw cycles.
WOS研究方向	Arts & Humanities - Other Topics ; Chemistry ; Materials Science ; Spectroscopy
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000722997000002
内容类型	期刊论文
源URL	[http://119.78.100.198/handle/2S6PX9GI/28312]
专题	中科院武汉岩土力学所
通讯作者	Liu, Chenchen
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Rock & Soil Mech, Wuhan 430071, Peoples R China
推荐引用方式 GB/T 7714	Liu, Chenchen,Liu, Yibiao,Ren, Weizhong,et al. An adaptive prediction method for mechanical properties deterioration of sandstone under freeze-thaw cycles: a case study of Yungang Grottoes[J]. HERITAGE SCIENCE,2021,9(1):11.
APA	Liu, Chenchen,Liu, Yibiao,Ren, Weizhong,Xu, Wenhui,Cai, Simin,&Wang, Junxia.(2021).An adaptive prediction method for mechanical properties deterioration of sandstone under freeze-thaw cycles: a case study of Yungang Grottoes.HERITAGE SCIENCE,9(1),11.
MLA	Liu, Chenchen,et al."An adaptive prediction method for mechanical properties deterioration of sandstone under freeze-thaw cycles: a case study of Yungang Grottoes".HERITAGE SCIENCE 9.1(2021):11.