期刊
GEOMECHANICS AND GEOPHYSICS FOR GEO-ENERGY AND GEO-RESOURCES
卷 9, 期 1, 页码 -出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s40948-023-00628-x
关键词
Rock cuttability; Excavation parameters; Regression analysis; UCS prediction model; Rock cuttability classification model; Intelligence algorithms
In this study, relevant excavation parameters of Zagros, Kerman and Bazideraz tunnels were collected to evaluate rock cuttability. Regression analyses were carried out to develop two-dimensional and three-dimensional models. The results showed that the three-dimensional regression models had better prediction performance than the two-dimensional models. Furthermore, prediction models for uniaxial compressive strength and rock cuttability classification were established based on specific energy.
Rock cuttability has great influence on the rock excavation efficiency of TBM (tunnel boring machine). In order to evaluate rock cuttability in real time, quickly, accurately and efficiently during TBM excavating, the relevant excavation parameters of Zagros, Kerman and Bazideraz tunnels were first collected. Then, the regression analyses between excavation parameters and rock cuttability were carried out. The two-dimensional regression analyses studied the relationship between operating parameters (thrust F and rotation speed RPM) and the characterization parameters (torque T and penetration rate PR). The three-dimensional regression analyses were utilized to create the PR and specific energy SE models based on operating parameters. The result shows that the established three-dimensional regression models have good prediction performance, and its performance is superior to two-dimensional models. Moreover, the prediction model of uniaxial compressive strength UCS and the classification model of rock cuttability were founded based on SE. The rock cuttability is divided into three levels, namely, easy (level 1), medium (level 2), and poor (level 3), in which the corresponding SE ranges are 0 to 6, 6 to 10 and exceeds 10 kWh & BULL;m(-3), respectively. Finally, the intelligent algorithms, combined with excavation parameters, were introduced to establish UCS prediction model and rock cuttability classification model, and the good prediction performance was achieved. The above studies can provide necessary references and ideas for real-time, rapid, accurate and effective evaluation of rock cuttability based on TBM excavation parameters, and has certain guiding significance for engineering application.
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