Prediction of TBM penetration rate from brittleness indexes using multiple regression analysis

Jamshidi, Amin

doi:10.1007/s40808-018-0432-2

Cited by 19 publications

(6 citation statements)

References 43 publications

(67 reference statements)

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“…Formulas (8)(9)(10)(11)(12)(13) summarize how to calculate the long term state, short term state, and output of a unit in a single instance at each time iteration.…”

Section: Surrounding Rock Class Prediction Model Based On Lstm-svmmentioning

confidence: 99%

“…6,7 Jamshidi established regression analysis prediction model of TBM cutting depth and surrounding rock brittleness index by using multiple regression analysis. 8 Yagiz et al respectively adopted intelligent methods such as fuzzy recognition, neural network, and particle swarm optimization to establish the prediction model of TBM excavation rate and rock brittleness index. 9 Qiu et al proposed an advanced classification method of surrounding rock class based on TSP203 system and genetic support vector machine.…”

Section: Introductionmentioning

confidence: 99%

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Prediction method of TBM tunnel surrounding rock classification based on LSTM-SVM

Liu,

Yang,

2024

Advances in Mechanical Engineering

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TBM tunnel surrounding rock classification is a key indicator for supporting decision-making and ensuring safe construction. And predicting the surrounding rock type accurately in advance is of great significance for TBM intelligent construction. This paper established the surrounding rock classification model based on support vector machine (LIBSVM), including preprocesses historical tunneling parameters, extracts data information that can accurately reflect the relationship between rock and machine, analyzes the correlation between different parameters and surrounding rock categories, and obtains highly relevant parameters. Based on the long short-term memory (LSTM), the prediction model of total thrust, cutter head torque, gripper pressure, cutter head rotate speed, and propulsion speed are established, which is the strongly correlated parameters with surrounding rock. Combining the parameter prediction model with the surrounding rock classification algorithm, the LSTM-SVM tunnel surrounding rock classification prediction model is established. The results showed that the coefficient of determination of the total thrust model, the cutter head torque, the gripper pressure, the cutter head speed, and the propulsion speed were 0.9825, 0.9396, 0.9974, 0.9843, and 0.9636. The overall prediction accuracy of the surrounding rock category can reach 86.0686%, which can provide a certain reference for predicting the surrounding rock condition in a short distance.

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“…Formulas (8)(9)(10)(11)(12)(13) summarize how to calculate the long term state, short term state, and output of a unit in a single instance at each time iteration.…”

Section: Surrounding Rock Class Prediction Model Based On Lstm-svmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction method of TBM tunnel surrounding rock classification based on LSTM-SVM

Liu,

Yang,

2024

Advances in Mechanical Engineering

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“…So, it was concluded that assessing the performance of rock excavation operations using a rock brittleness index depended on the practical application, e.g., brittleness index, that considered that impact strength parameters may be more suitable for the prediction of percussive drilling penetration rate [29]. Similarly, the effect of rock brittleness on the penetration rate for percussive, rotary and Down The Hole (DTM) drilling operations was investigated using literature data for different types of rocks having varying strength in [30]. Using multiple regression, three equations were proposed, one for each drilling type, to predict the penetration rate using three strength-based brittleness indices [30].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, the effect of rock brittleness on the penetration rate for percussive, rotary and Down The Hole (DTM) drilling operations was investigated using literature data for different types of rocks having varying strength in [30]. Using multiple regression, three equations were proposed, one for each drilling type, to predict the penetration rate using three strength-based brittleness indices [30].…”

Section: Introductionmentioning

confidence: 99%

Study of the Relationship between Mode I Fracture Toughness and Rock Brittleness Indices

Ameen,

Elwageeh,

Abdelaziz

et al. 2023

Applied Sciences

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Mode I fracture toughness (KIC) and rock brittleness are important properties that influence many rock engineering applications. Due to the difficulties in determining KIC experimentally, previous studies have investigated the relationship between KIC and rock brittleness indices. However, only rock brittleness indices (based on strength parameters) and KIC obtained from Chevron Bend and Short Rod test methods were considered. In this paper, regression analysis was carried out to investigate the relationship between KIC and rock brittleness using literature data collected from different rock types and core KIC test methods under level I and static test conditions. Rock brittleness was assessed using ten indices based on strength and pre-peak elastic parameters. The results showed that elastic-based indices were not good predictors of KIC, while strength-based indices correlated well with KIC. A comparison with previous studies revealed that the correlations between KIC and strength-based indices were significantly sensitive to the rock type, i.e., soft or hard, and the KIC test method. However, a brittleness index, based on both strength and pre-peak elastic parameters, was found to be the best index to predict KIC because of its lower sensitivity to the test method and rock type.

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“…Several studies noted that predicting the penetration rate is a complex and difficult task because of the interaction between the TBM and rock mass. According to Jamshidi [51], TBM penetration rate directly influences the advance rate, which represents the total distance excavated by the machine divided by the total time. While there is a high cost associated with tunneling projects and using the TBM, utilizing prediction methods, especially ML techniques for predicting the penetration rate of TBM can significantly reduce the total time and cost of the tunneling projects.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Prediction of TBM penetration rate from brittleness indexes using multiple regression analysis

Cited by 19 publications

References 43 publications

Prediction method of TBM tunnel surrounding rock classification based on LSTM-SVM

Prediction method of TBM tunnel surrounding rock classification based on LSTM-SVM

Study of the Relationship between Mode I Fracture Toughness and Rock Brittleness Indices

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

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