Geological and mechanical rock mass conditions for TBM performance prediction. The case of “La Maddalena” exploratory tunnel, Chiomonte (Italy)

Armetti, Giacomo; Migliazza, M.; Ferrari, Federica; Berti, Andrea; Padovese, Paolo

doi:10.1016/j.tust.2018.02.012

Cited by 42 publications

(8 citation statements)

References 23 publications

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“…Similarly, Zhang et al [62] predicted the geological conditions using TBM operational data. Armetti et al [6] assessed TBM performance data employing various intact rock and rock mass properties. Salimi et al [48] proposed a brief review of the applications of common rock mass classification systems for the performance prediction of TBMs and the development of a new model that is based on the input parameters of the RMR system for the prediction of TBM performance.…”

Section: Introductionmentioning

confidence: 99%

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Gökçeoğlu

2021

SN Appl. Sci.

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One of the most important issues in tunnels to be constructed with tunnel boring machines (TBMs) is to predict the excavation time. Excavation time directly affects tunnel costs and feasibility. For this reason, studies on the prediction of TBM performance have always been interesting for tunnel engineers. Therefore, the purpose of the study is to develop models to predict the rate of penetration (ROP) of TBMs. In accordance with the purpose of the study, a new database including 5334 cases is obtained from the longest railway tunnel of Turkey. Each case includes uniaxial compressive strength, Cerchar Abrasivity Index, α angle, weathering degree and water conditions as input or independent variables. Two multiple regression models and two ANN models are developed in the study. The performances of the ANN models are considerably better than those of the multiple regression equations. Before deep tunnel construction in a metamorphic rock medium, the ANN models developed in the study are reliable and can be used. In contrast, the performances of the multiple regression equations are promising, but they predict lower ROP values than the measured ROP values. Consequently, the prediction models for ROP are open to development depending on the new data and new prediction algorithms.

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Section: Introductionmentioning

confidence: 99%

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Gökçeoğlu

2021

SN Appl. Sci.

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“…Under geologically complicated conditions, the fault structure prediction model retains only one rule for repeated rules, while for contradictory rules. Each neuron in the rule layer represents a rule, according to the rule set, can determine the connection between the rule layer and the fuzziness layer [19].…”

Section: Comparison Of Model Prediction Effects Between Based On Arti...mentioning

confidence: 99%

Prediction Model for Geologically Complicated Fault Structure Based on Artificial Neural Network and Fuzzy Logic

Liu

Yang³

et al. 2022

Scientific Programming

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The development and distribution of geologically complicated fault structure have the characteristics of uncertainty, randomness, ambiguity, and variability. Therefore, the prediction of complicated fault structures is a typical nonlinear problem. Neither fuzzy logic method nor artificial neural network alone can solve this problem well because the fuzzy method is generally not easy to realize adaptive learning function, and the neural network method is not suitable for describing sedimentary microfacies or geophysical facies. Therefore, taking the marginal subsags in the Jiyang Depression, Eastern China, as a study case, this paper uses the method of combining artificial neural network and fuzzy logic to study geologically complicated fault structure prediction model. This paper expounds on the research status and significance of geologically complicated fault structure prediction model, elaborates the development background, current status, and future challenges of artificial neural networks and fuzzy logic, introduces the method and principle of fuzzy neural network structure and fuzzy logic analysis algorithm, conducts prediction model design and implementation based on fuzzy neural network, proposes the learning algorithm of fuzzy neural network, analyzes the programming realization of fuzzy neural network, constructs complicated fault structure prediction model based on the artificial neural network and fuzzy logic, performs the fuzzy logic system selection of complicated fault structure prediction model, carries out the artificial neural network structure design of complicated fault structure prediction model, compares the prediction effects of the geologically complicated fault structure model based on artificial neural networks and fuzzy logic, and finally discusses the system design and optimization of the prediction model for geologically complicated fault structures. The study results show that the fuzzy neural network fully integrates the advantages of artificial neural network and fuzzy logic system; based on the clear physical background of fuzzy logic system, it effectively integrates powerful knowledge expression ability and fuzzy reasoning ability into the network knowledge structure of neural network, which greatly improves the prediction accuracy of geologically complicated fault structure.

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“…As one of the most important function of TBM, muck removal performance is largely dependent on geological properties and operational parameters. During TBM excavation in complex geology, if generated muck cannot be discharged in time, many engineering problems would occur, such as excessive unbalanced force, second-wear and sticking of cutting tools (Armetti et al 2018;Fatemi et al 2016;Yagiz et al 2015). As a result, it is extremely significant to study the influence of geological properties and operational parameters on muck removal performance to guarantee the safety and efficiency of tunnel construction.…”

Section: Introductionmentioning

confidence: 99%

Influence of Geological Properties and Operational Parameters on TBM Muck Removal Performance for Yinsong Tunnel

Xia

Mei

et al. 2021

Geotech Geol Eng

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Operational parameters of a Tunnel Boring Machine (TBM) under different geological properties have significant influence on muck removal performance. To improve the muck removal performance for Yinsong Tunnel TMB, tuff and granite properties were surveyed by geological detection in the tunnel project, and the optimization of advancement and rotational speed of cutterhead was investigated. The muck model was established by discrete element method (DEM), considering the shape characteristic of muck. The whole muck removal process was simulated, then the influence of the advancement speed and rotational speed on muck removal performance in tuff and granite stratum was studied via numerical simulations. Based on simulation results, the muck removal performance evaluation system of TBM is built using fuzzy mathematics and evaluation theory. Ultimately, optimal operation parameters in the tunnel project is obtained after comprehensive evaluation. The results indicated that the optimum advancement speed should be 7 r/min in both tuff and granite stratum, while the optimal rotational speed was 6 r/min in granite and 5.5 r/min in tuff respectively. Based on TBM engineering application, the size characteristic of muck was obtained from on-site muck specimen analysis and the muck removal performance of TBM under different operation and rock conditions was compared to validate the simulation method. These findings could provide guidance for TBM operation to improve muck removal performance in different geological conditions.

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Geological and mechanical rock mass conditions for TBM performance prediction. The case of “La Maddalena” exploratory tunnel, Chiomonte (Italy)

Cited by 42 publications

References 23 publications

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Prediction Model for Geologically Complicated Fault Structure Based on Artificial Neural Network and Fuzzy Logic

Influence of Geological Properties and Operational Parameters on TBM Muck Removal Performance for Yinsong Tunnel

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