Estimation of the performance of the tunnel boring machine (TBM) using uniaxial compressive strength and rock mass rating classification (RMR)–A case study from the Deccan traps, India

2016 ◽  
Vol 87 (2) ◽  
pp. 145-152 ◽  
Author(s):  
P. Jain ◽  
A. K. Naithani ◽  
T. N. Singh
Keyword(s):  
Compressive Strength ◽  
Rock Mass ◽  
Uniaxial Compressive Strength ◽  
Tunnel Boring Machine ◽  
Deccan Traps ◽  
Rock Mass Rating ◽  
Tunnel Boring

scholarly journals Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate

2019 ◽  
Vol 9 (18) ◽  
pp. 3715 ◽  
Author(s):  
Hai Xu ◽  
Jian Zhou ◽  
Panagiotis G. Asteris ◽  
Danial Jahed Armaghani ◽  
Mahmood Md Tahir
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Predictive Models ◽  
Penetration Rate ◽  
Tunnel Boring Machine ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Tunnel Boring ◽  
Learning Techniques

Predicting the penetration rate is a complex and challenging task due to the interaction between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the use of empirical and theoretical techniques in predicting TBM performance. However, reliable performance prediction of TBM is of crucial importance to mining and civil projects as it can minimize the risks associated with capital costs. This study presents new applications of supervised machine learning techniques, i.e., k-nearest neighbor (KNN), chi-squared automatic interaction detection (CHAID), support vector machine (SVM), classification and regression trees (CART) and neural network (NN) in predicting the penetration rate (PR) of a TBM. To achieve this aim, an experimental database was set up, based on field observations and laboratory tests for a tunneling project in Malaysia. In the database, uniaxial compressive strength, Brazilian tensile strength, rock quality designation, weathering zone, thrust force, and revolution per minute were utilized as inputs to predict PR of TBM. Then, KNN, CHAID, SVM, CART, and NN predictive models were developed to select the best one. A simple ranking technique, as well as some performance indices, were calculated for each developed model. According to the obtained results, KNN received the highest-ranking value among all five predictive models and was selected as the best predictive model of this study. It can be concluded that KNN is able to provide high-performance capacity in predicting TBM PR. KNN model identified uniaxial compressive strength (0.2) as the most important and revolution per minutes (0.14) as the least important factor for predicting the TBM penetration rate.

scholarly journals Analisis Karakteristik Massa Batuan di Sektor Lemajung, Kalan, Kalimantan Barat

EKSPLORIUM ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 17 ◽  
Author(s):  
Heri Syaeful ◽  
Dhatu Kamajati
Keyword(s):  
Compressive Strength ◽  
Rock Mass ◽  
Uniaxial Compressive Strength ◽  
Open Pit ◽  
Rock Mechanic ◽  
Rock Mass Rating ◽  
Class Iii ◽  
Rock Quality Designation ◽  
Joint Spacing ◽  
Rock Quality

Karakterisasi massa batuan diperlukan dalam suatu rancangan bukaan batuan, dimana perhitungan sifat-sifat teknis dari massa batuan menjadi hal yang penting untuk diperhatikan. Sektor Lemajung merupakan salah satu area prospek untuk penambangan uranium di Kalan, Kalimantan Barat. Tujuan penelitian adalah mendapatkan data karakteristik massa batuan yang merupakan data dasar bagi perencanaan pengembangan teknik penambangan cebakan bahan galian. Metodologi yang digunakan adalah dengan pengambilan contoh batuan untuk analisis laboratorium mekanika batuan, pengamatan rekahan, dan pengamatan kondisi airtanah. Parameter batuan yang dianalisis meliputi uniaxial compressive strength (UCS), rock quality designation (RQD), jarak rekahan, kondisi rekahan, dan airtanah. Hasil analisis menyimpulkan bahwa metalanau sebagai litologi yang mengandung uranium di Sektor Lemajung mempunyai nilai rock mass rating (RMR) sebesar 56 atau kelas massa batuan III: fair rock pada kedalaman sekitar 60 m, dan pada kedalaman 280 m nilai RMR mencapai 82 atau kelas massa batuan I: very good rock. Data nilai RMR tersebut selanjutnya dapat digunakan dalam analisis pembuatan terowongan pada model tambang bawah tanah atau analisis kestabilan lereng pada model tambang terbuka. Rock mass characterization is required in design of rock opening, which calculation of engineering characters of rock mass become one important parameter toconsider. Lemajung sector is one of prospect area for uranium mining in Kalan, West Kalimantan. Purpose of research is to acquire rock mass characteristicsas basic data for planning the development of mining technique of ore deposit. Methodology applied is rock sampling for rock mechanic laboratory analysis, observation of joints, and observation of groundwater condition. Rock parameters analyzed includes uniaxial compressive strength (UCS), rock quality designation (RQD), joint spacing, joint condition, and groundwater. Analysis concluded that metasiltstonewhich is lithology contained uranium in Lemajung Sector has rock mass rating (RMR) value of 56 or rock mass class III: fair rock in the depth of around 60 m, and in the depth of 280 m RMR value reach 82 or rock mass class I: very good rock. RMR value data furthermore could be used for analysis of tunneling in the model of underground mine or slope stability analysis in the model of open pit mine.

scholarly journals Finite-difference based response surface methodology to optimize tailgate support systems in longwall coal mining

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Satar Mahdevari ◽  
Mohammad Hayati
Keyword(s):  
Compressive Strength ◽  
Response Surface Methodology ◽  
Rock Mass ◽  
Finite Difference ◽  
Longwall Mining ◽  
Support Systems ◽  
Rock Mass Rating ◽  
Slake Durability Index ◽  
Proposed Model ◽  
Durability Index

AbstractDesigning a suitable support system is of great importance in longwall mining to ensure the safe and stable working conditions over the entire life of the mine. In high-speed mechanized longwall mining, the most vulnerable zones to failure are roof strata in the vicinity of the tailgate roadway and T-junctions. Severe roof displacements are occurred in the tailgate roadway due to the high-stress concentrations around the exposed roof span. In this respect, Response Surface Methodology (RSM) was utilized to optimize tailgate support systems in the Tabas longwall coal mine, northeast of Iran. The nine geomechanical parameters were obtained through the field and laboratory studies including density, uniaxial compressive strength, angle of internal friction, cohesion, shear strength, tensile strength, Young’s modulus, slake durability index, and rock mass rating. A design of experiment was developed through considering a Central Composite Design (CCD) on the independent variables. The 149 experiments are resulted based on the output of CCD, and were introduced to a software package of finite difference numerical method to calculate the maximum roof displacements (dmax) in each experiment as the response of design. Therefore, the geomechanical variables are merged and consolidated into a modified quadratic equation for prediction of the dmax. The proposed model was executed in four approaches of linear, two-factor interaction, quadratic, and cubic. The best squared correlation coefficient was obtained as 0.96. The prediction capability of the model was examined by testing on some unseen real data that were monitored at the mine. The proposed model appears to give a high goodness of fit with the accuracy of 0.90. These results indicate the accuracy and reliability of the developed model, which may be considered as a reliable tool for optimizing or redesigning the support systems in longwall tailgates. Analysis of variance (ANOVA) was performed to identify the key variables affecting the dmax, and to recognize their pairwise interaction effects. The key parameters influencing the dmax are respectively found to be slake durability index, Young’s modulus, uniaxial compressive strength, and rock mass rating.

scholarly journals Determination of Mechanical Properties of Altered Dacite by Laboratory Methods

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 813
Author(s):  
Veljko Rupar ◽  
Vladimir Čebašek ◽  
Vladimir Milisavljević ◽  
Dejan Stevanović ◽  
Nikola Živanović
Keyword(s):  
Mechanical Properties ◽  
Compressive Strength ◽  
Rock Mass ◽  
Uniaxial Compressive Strength ◽  
Rock Material ◽  
Strength Parameter ◽  
Gradual Transition ◽  
Strength Parameters ◽  
Triaxial Compressive Strength ◽  
Heterogeneous Rock

This paper presents a methodology for determining the uniaxial and triaxial compressive strength of heterogeneous material composed of dacite (D) and altered dacite (AD). A zone of gradual transition from altered dacite to dacite was observed in the rock mass. The mechanical properties of the rock material in that zone were determined by laboratory tests of composite samples that consisted of rock material discs. However, the functional dependence on the strength parameter alteration of the rock material (UCS, intact UCS of the rock material, and mi) with an increase in the participation of “weaker” rock material was determined based on the test results of uniaxial and triaxial compressive strength. The participation of altered dacite directly affects the mode and mechanism of failure during testing. Uniaxial compressive strength (σciUCS) and intact uniaxial compressive strength (σciTX) decrease exponentially with increased AD volumetric participation. The critical ratio at which the uniaxial compressive strength of the composite sample equals the strength of the uniform AD sample was at a percentage of 30% AD. Comparison of the obtained exponential equation with practical suggestions shows a good correspondence. The suggested methodology for determining heterogeneous rock mass strength parameters allows us to determine the influence of rock material heterogeneity on the values σciUCS, σciTX, and constant mi. Obtained σciTX and constant mi dependences define more reliable rock material strength parameter values, which can be used, along with rock mass classification systems, as a basis for assessing rock mass parameters. Therefore, it is possible to predict the strength parameters of the heterogeneous rock mass at the transition of hard (D) and weak rock (AD) based on all calculated strength parameters for different participation of AD.

scholarly journals A Case Study on TBM Cutterhead Temperature Monitoring and Mud Cake Formation Discrimination Method

2021 ◽  
Author(s):  
Jie Fu ◽  
Yimin Xia ◽  
Hao Lan ◽  
Dun Wu ◽  
Laikuang lin
Keyword(s):  
Temperature Variation ◽  
Tunnel Boring Machine ◽  
Soil Disturbance ◽  
Tunnel Boring ◽  
Mud Cake ◽  
Cake Formation ◽  
Slurry Shield ◽  
Construction Efficiency ◽  
Discrimination Method

Abstract The mud cake is easily formed during the tunnel boring machine (TBM) excavation in clay soils or rocks containing clay minerals. Mud cake will lead to soil disturbance, clogging cutterhead and even affect the construction efficiency and personnel safety. In this study, a mud cake formation discrimination method based on cutterhead temperature was proposed. An online monitoring system was designed and installed on the slurry shield TBM. The results show that: (a) the cutterhead temperature data can be reliably detected and transmitted by the system; (b) in a tunneling ring, the temperature at some positions of the cutterhead will increase first and then decrease; (c) during the field test, the temperature variation is around 2.5℃ under the normal condition, but the temperature variation will increase more than 50℃ due to the mud cake or geological change; (d) compared with the cooling rate, mud cake formation can be accurately discriminated.

scholarly journals Rock mass quality effected by lineament using rock mass rating (rmr)-case study from former quarry site

2017 ◽  
Vol 1 (2) ◽  
pp. 13-16 ◽  
Author(s):  
Hamzah Hussin ◽  
Nurhazren bt Fauzi ◽  
Tajul Anuar Jamaluddin ◽  
Mohd Hariri Arifin
Keyword(s):  
Rock Mass ◽  
Rock Mass Rating ◽  
Rock Mass Quality
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