Developing Estimation Equations for the Cerchar Abrasivity Index of Rocks Applicable to TBM Tunnels

被引:0
|
作者
She, Lei [1 ,2 ]
Li, Yan-long [1 ]
Zhang, She-rong [2 ]
Wang, Chao [2 ]
He, Sun-wen [3 ]
Wang, Yu-jie [4 ]
He, Ming-ming [1 ]
Wang, Sheng-le [3 ]
机构
[1] Xian Univ Technol, State Key Lab Ecohydraul Northwest Arid Reg China, 5 South Jinhua Rd, Xian 710048, Peoples R China
[2] Tianjin Univ, Sch Civil Engn, 135 Yaguan Rd, Tianjin 300072, Peoples R China
[3] Power China Co Ltd, 22 West Chegongzhuang Rd, Beijing 100048, Peoples R China
[4] China Inst Water Resources & Hydropower Res, China State Key Lab Simulat & Regulat Water Cycle, 20 West Chegongzhuang Rd, Beijing 100048, Peoples R China
基金
中国国家自然科学基金;
关键词
Cerchar Abrasivity Index (CAI); Mechanical properties; Rock mass classification parameters/system; Machine performance; Regression analysis; Estimation performance evaluation; PERFORMANCE PREDICTION; GEOMECHANICAL PROPERTIES; ABRASIVENESS INDEX; PENETRATION RATE; GRANITIC-ROCKS; GRANULAR SOIL; TOOL WEAR; CAI; REGRESSION; STRENGTH;
D O I
10.1007/s00603-024-04015-0
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
Rock abrasivity plays an important role in the machine design, construction scheduling, and budgeting of TBM projects. Establishing several faster and simpler estimation equations for the Cerchar Abrasivity Index (CAI) of rocks is, therefore, very important. This study investigated the correlation between the CAI and mechanical properties of rock, rock mass classification parameters, and machine performance. A TBM construction database including 159 tunnel sections is established. Several acceptable and practical estimation equations of CAI are developed using simple and multiple regression analysis (0.66 < R-2 < 0.76). In this process, a normalized specific energy is proposed to evaluate the machine performance. The results show that the rock compressive strength and brittleness index are the most dependent parameters to explain CAI, and the estimated rock mass strength also indicates a close correlation. In addition, the contribution of a rock mass classification system and machine performance index for estimating CAI cannot be ignored. Finally, the estimation performance of the developed equations is compared and evaluated, and a new method for selecting an optimal model based on ranking is proposed. Since the input parameters of the proposed equations can be readily available at the project planning stage, they are very practical for TBM designers, tunnel designers, and contractors.
引用
收藏
页码:8879 / 8898
页数:20
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