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Journal articles on the topic 'TBM performance'

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Published: 9 March 2023
Last updated: 10 March 2023

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1

Schneider, Eckart, and Max John. "Prediction of TBM performance." Geomechanik und Tunnelbau 2, no. 2 (April 2009): 122. http://dx.doi.org/10.1002/geot.200990009.

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2

Tarkoy, Peter J. "Simple and practical TBM performance prediction." Geomechanik und Tunnelbau 2, no. 2 (April 2009): 128–39. http://dx.doi.org/10.1002/geot.200900017.

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3

Kwizera, Richard, Fiona V. Cresswell, Gerald Mugumya, Micheal Okirwoth, Enock Kagimu, Ananta S. Bangdiwala, Darlisha A. Williams, Joshua Rhein, David R. Boulware, and David B. Meya. "Performance of Lipoarabinomannan Assay using Cerebrospinal fluid for the diagnosis of Tuberculous meningitis among HIV patients." Wellcome Open Research 4 (August 19, 2019): 123. http://dx.doi.org/10.12688/wellcomeopenres.15389.1.

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Background: The diagnostic utility of the Mycobacteria tuberculosis lipoarabinomannan (TB-LAM) antigen lateral flow assay on cerebrospinal fluid (CSF) for the diagnosis of tuberculous meningitis (TBM) has not been extensively studied and the few published studies have conflicting results. Methods: Lumbar CSF from 59 HIV-positive patients with suspected TBM was tested with TB-LAM and Xpert MTB/Rif Ultra. The diagnostic performance of CSF TB-LAM was compared to positive CSF Xpert MTB/Rif Ultra (definite TBM) and a composite reference of probable or definite TBM according to the uniform case definition. Results: Of 59 subjects, 12 (20%) had definite TBM and five (9%) had probable TBM. With reference to definite TBM, CSF TB-LAM assay had a diagnostic sensitivity of 33% and specificity of 96%. When compared to a composite reference of definite or probable TBM, the sensitivity was 24% and specificity was 95%. There were two false positive tests with TB-LAM (3+ grade). In-hospital mortality in CSF TB-LAM positive patients was 17% compared to 0% in those with definite TBM by Xpert MTB/Rif Ultra but negative LAM. Conclusions: Lumbar CSF TB-LAM has a poor performance in diagnosing TBM. Both urine TB-LAM and Xpert Ultra should be further investigated in the diagnosis of TBM.
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Kwizera, Richard, Fiona V. Cresswell, Gerald Mugumya, Micheal Okirwoth, Enock Kagimu, Ananta S. Bangdiwala, Darlisha A. Williams, Joshua Rhein, David R. Boulware, and David B. Meya. "Performance of Lipoarabinomannan Assay using Cerebrospinal fluid for the diagnosis of Tuberculous meningitis among HIV patients." Wellcome Open Research 4 (September 30, 2019): 123. http://dx.doi.org/10.12688/wellcomeopenres.15389.2.

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Background: The diagnostic utility of the Mycobacteria tuberculosis lipoarabinomannan (TB-LAM) antigen lateral flow assay on cerebrospinal fluid (CSF) for the diagnosis of tuberculous meningitis (TBM) has not been extensively studied and the few published studies have conflicting results. Methods: Lumbar CSF from 59 HIV-positive patients with suspected TBM was tested with TB-LAM and Xpert MTB/Rif Ultra. The diagnostic performance of CSF TB-LAM was compared to positive CSF Xpert MTB/Rif Ultra (definite TBM) and a composite reference of probable or definite TBM according to the uniform case definition. Results: Of 59 subjects, 12 (20%) had definite TBM and five (9%) had probable TBM. With reference to definite TBM, CSF TB-LAM assay had a diagnostic sensitivity of 33% and specificity of 96%. When compared to a composite reference of definite or probable TBM, the sensitivity was 24% and specificity was 95%. There were two false positive tests with TB-LAM (3+ grade). In-hospital mortality in CSF TB-LAM positive patients was 17% compared to 0% in those with definite TBM by Xpert MTB/Rif Ultra but negative LAM. Conclusions: Lumbar CSF TB-LAM has a poor performance in diagnosing TBM. Both urine TB-LAM and Xpert Ultra should be further investigated in the diagnosis of TBM.
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5

Zou, Xiaoyang, Hui Zheng, and Yongzhen Mi. "Performance Evaluation of Hard Rock TBMs considering Operational and Rock Conditions." Shock and Vibration 2018 (2018): 1–17. http://dx.doi.org/10.1155/2018/8798232.

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This paper focuses on studying the correlations of the performance of hard rock tunnel boring machines (TBMs) with operational and rock conditions. Firstly, a rigid-flexible coupled multibody dynamic model of an opening hard rock TBM is established for the analysis of its vibration. Then four performance indexes including mean vibration energy dissipation rate, dynamic specific energy (DSE), disc cutter wear rate, and load sharing coefficient are introduced and formulated, respectively, for evaluating the vibration level, excavation energy efficiency, cutter’s vulnerability to wear, and load transmission performance of cutterhead driving system of the TBM. Finally, numerical simulation results of the TBM tunneling performance evaluation are obtained and validated by on-site vibration measurement and tunneling data collection. It is found that operational and rock conditions exert important impact on TBM vibration level, excavation energy efficiency, and structure damage. When the type of rock to be cut changes from soft to hard with operational parameters held constant, TBM performance evaluated by these three indexes deteriorates significantly, and both the decrease of excavation energy efficiency and the increase of cutter wear rate caused by TBM vibration are obvious. This study provides the foundation for a more comprehensive evaluation of TBM performance in actual tunneling process.
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6

Panthi, Krishna Kanta, and Jhonny Encalada. "Evaluation on the TBM Performance at a Hydropower Project in Ecuador." Hydro Nepal: Journal of Water, Energy and Environment 24 (April 10, 2019): 10–16. http://dx.doi.org/10.3126/hn.v24i0.23575.

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The aim of this manuscript is to discuss the Tunnel Boring Machine (TBM) performance along the recently constructed headrace tunnel of Minas-San Francisco Hydropower Project in Ecuador. Firstly, the manuscript briefly describes the importance of TBM tunneling and about the Minas-San HPP. Further, discussions are made on the engineering geological conditions along the headrace tunnel. Detailed evaluations are made on the performance of TBM tunneling considering influence of rock mass quality on the TBM penetration rate. The manuscript emphasizes that the knowledge of the rock mass quality parameters and cutter technology available at present are among the key factors that influence the estimation of the net penetration rate of the TBM. It has been demonstrated that the hard to very hard rock masses of high abrasivity that were encountered along the headrace tunnel alignment caused very low penetration giving slow progress, which was not predicted during planning phase design. The authors investigated a fairly good link between TBM penetration and the mechanical strength of the rock mass.
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7

Sissins, Sarah, and Chrysothemis Paraskevopoulou. "Assessing TBM performance in heterogeneous rock masses." Bulletin of Engineering Geology and the Environment 80, no. 8 (June 21, 2021): 6177–203. http://dx.doi.org/10.1007/s10064-021-02209-2.

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AbstractA major challenge that TBM performance is requested to deal with for a successful and effective progress is tunnelling through lithologically and geomechanically heterogeneous rock masses. Such heterogeneous environments are common and recent tunnel examples in the UK include the Hinckley Point C offshore cooling tunnels being driven through interbedded carbonaceous mudstone/shales and argillaceous limestone and the Anglo American’s Woodsmith Mine Mineral Transport System tunnel in Redcar Mudstone with beds of ironstone. This inherent geological heterogeneity leads to difficult tunnelling conditions that initially stem from predicting a sound and representative ground model that can be used to preliminary assess the TBM performance. In this work, an exhaustive review of existing TBM Penetration Rate (PR) methods identified that no models address the issue of parameter selection for heterogeneous rock masses comprising layers with different rock strengths. Consequently, new approaches are required for estimating rock mass behaviour and machine performance in such environments. In the presented work the Blue Lias Formation (BLI), which is characterised by its layered rock mass, comprising very strong limestone, interbedded with weak mudstone and shales, is investigated. BLI formation is considered herein being a representative example of lithological heterogeneity. Based on the fieldwork carried out in three localities in the Bristol Channel Basin (S. Wales and Somerset), geological models are produced based on which a geotechnical model is developed, and four ground types are determined. Implications of the current findings for TBM performance are assessed, including faulting, groundwater inflow and excavation stability with a particular focus on both PR and advance rate. A modified approach using the existing empirical models is proposed, developed and presented in this paper that can be used as a guide to determine TBM performance in heterogeneous rock masses reducing the risk of cost and time overruns.
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8

Wang, Hongyuan, and Jingcheng Wang. "The multi-objective optimization of tunneling boring machine control based on geological conditions identification." Journal of Intelligent Manufacturing and Special Equipment 1, no. 1 (November 5, 2020): 87–105. http://dx.doi.org/10.1108/jimse-07-2020-0005.

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PurposeThe purpose of this paper aims to design an optimization control for tunnel boring machine (TBM) based on geological identification. For unknown geological condition, the authors need to identify them before further optimization. For fully considering multiple crucial performance of TBM, the authors establish an optimization problem for TBM so that it can be adapted to varying geology. That is, TBM can operate optimally under corresponding geology, which is called geology-adaptability.Design/methodology/approachThis paper adopted k-nearest neighbor (KNN) algorithm with modification to identify geological conditions. The modification includes adjustment of weights in voting procedure and similarity distance measurement, which at suitable for engineering and enhance accuracy of prediction. The authors also design several key performances of TBM during operation, and built a multi-objective function. Further, the multi-objective function has been transformed into a single objective function by weighted-combination. The reformulated optimization was solved by genetic algorithm in the end.FindingsThis paper provides a support for decision-making in TBM control. Through proposed optimization control, the advance speed of TBM has been enhanced dramatically in each geological condition, compared with the results before optimizing. Meanwhile, other performances are acceptable and the method is verified by in situ data.Originality/valueThis paper fulfills an optimization control of TBM considering several key performances during excavating. The optimization is conducted under different geological conditions so that TBM has geological-adaptability.
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9

Sapigni, M., M. Berti, E. Bethaz, A. Busillo, and G. Cardone. "TBM performance estimation using rock mass classifications." International Journal of Rock Mechanics and Mining Sciences 39, no. 6 (September 2002): 771–88. http://dx.doi.org/10.1016/s1365-1609(02)00069-2.

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10

Lee, S. W., S. H. Chang, K. H. Park, and C. Y. Kim. "TBM Performance and Development State in Korea." Procedia Engineering 14 (2011): 3170–75. http://dx.doi.org/10.1016/j.proeng.2011.07.400.

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11

Benardos, A. G., and D. C. Kaliampakos. "Modelling TBM performance with artificial neural networks." Tunnelling and Underground Space Technology 19, no. 6 (November 2004): 597–605. http://dx.doi.org/10.1016/j.tust.2004.02.128.

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12

Nelson, P. P., A. R. Ingraffea, and T. D. O'Rourke. "TBM performance prediction using rock fracture parameters." International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts 22, no. 3 (June 1985): 189–92. http://dx.doi.org/10.1016/0148-9062(85)93234-6.

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13

Liu, Donghai, Yunqing Zhou, and Kai Jiao. "TBM construction process simulation and performance optimization." Transactions of Tianjin University 16, no. 3 (May 19, 2010): 194–202. http://dx.doi.org/10.1007/s12209-010-0035-0.

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14

Taqa, Ala Abu, Mohamed Al-Ansari, Ramzi Taha, Ahmed Senouci, Ghaleb M. Al-Zubi, and Mohamed O. Mohsen. "Performance of Concrete Mixes Containing TBM Muck as Partial Coarse Aggregate Replacements." Materials 14, no. 21 (October 21, 2021): 6263. http://dx.doi.org/10.3390/ma14216263.

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This study investigated the potential utilization of the TBM muck obtained from the Gold Line of the Doha Metro Project as a partial replacement of coarse aggregates in concrete mixes. First, the TBM muck particles were screened to coarse aggregate standard sizes. Then, concrete mixes were prepared using 0%, 25%, 50%, and 75% TBM muck replacement of coarse aggregates. The compressive and flexural strengths were determined for all mixes at 28 and 56 days. Moreover, the results obtained were validated using EDX analysis and SEM images. A t-statistical analysis did not show a significant impact of TBM muck usage on the compressive strength results of the concrete mixes. However, another t-statistical analysis showed that TBM muck replacement of coarse aggregates had adversely affected the flexural strength results. The EDX analysis indicated the presence of Na+ ions, which can replace the Ca2+ ions in the C-S-H gel, cause discontinuities of it, and hence reduce the strength at later ages. Finally, the SEM images showed that the ettringite and carbon hydroxide (C-H) contents in the mixes with TBM muck were higher than that of the control mix, while the C-S-H gel was less in such mixes.
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15

Sharma, K., M. Sharma, M. Modi, N. Singla, A. Sharma, A. Sharma, N. Sharma, and P. Ray. "Comparative analysis of Truenat™ MTB Plus and Xpert® Ultra in diagnosing tuberculous meningitis." International Journal of Tuberculosis and Lung Disease 25, no. 8 (August 1, 2021): 626–31. http://dx.doi.org/10.5588/ijtld.21.0156.

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BACKGROUND: Diagnostic delay and drug resistance not only worsen the outcomes of tuberculous meningitis (TBM), but are also important impediments to TB elimination efforts. Given the need for a near point-of-care test suitable for primary healthcare centres and simultaneous detection of resistance, Truenat™ MTB Plus assay was evaluated on a large cohort of TBM patients.METHODS: Truenat assay was performed on 148 cerebrospinal fluid specimens (76 definite TBM, 32 probable TBM and 40 non-TBM controls) and its performance was compared with Xpert® Ultra.RESULTS: The overall sensitivity of Truenat and Ultra was respectively 78.7% and 67.6% in diagnosing TBM, and respectively 85.5% and 96% in diagnosing definite TBM. Twenty-three additional cases were detected using Truenat and 11 using Ultra. Truenat missed seven cases of rifampicin (RIF) resistance and indicated false RIF resistance in four cases.CONCLUSION: Performance of Truenat was comparable to that of Ultra in diagnosing TBM and inferior to Xpert Ultra in determining RIF resistance.
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16

Pan, Liping, Fei Liu, Jinli Zhang, Xinting Yang, Shiqi Zheng, Jing Li, Hongyan Jia, Xiaoyou Chen, Mengqiu Gao, and Zongde Zhang. "Interferon-Gamma Release Assay Performance of Cerebrospinal Fluid and Peripheral Blood in Tuberculous Meningitis in China." BioMed Research International 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8198505.

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The aim of this study was to examine the performance of T-SPOT.TB on cerebrospinal fluid (CSF) and peripheral blood (PB) in diagnosis of tuberculous meningitis (TBM) in China. Of 100 patients with presumed TBM prospectively enrolled from Sep 2012 to Oct 2014, 53 were TBM (21 definite and 32 probable TBM cases) and 37 were non-TBM cases; the other 10 patients were excluded from analysis due to inconclusive diagnosis, no sufficient CSF samples, or incomplete follow-up. T-SPOT.TB on CSF and PB and routine laboratory tests of CSF were performed simultaneously. The receiver operating characteristic (ROC) curve and cut-off value of CSF T-SPOT.TB and routine CSF parameters were established between TBM and non-TBM group. The area under ROC curve (AUC) of the T-SPOT.TB on CSF and PB was 0.81 and 0.89, which was higher than that of the routine CSF parameters (AUC 0.67–0.77). Although the sensitivity of CSF T-SPOT.TB was lower than that of PB T-SPOT.TB (60.8% versus 90.6%,P<0.001), the specificity of CSF T-SPOT.TB was significantly higher than that of PB T-SPOT.TB (97.2% versus 75.7%,P=0.007). These results indicated that the diagnostic accuracies of PB and CSF T-SPOT.TB are higher than routine laboratory tests. Furthermore, the higher specificity of CSF T-SPOT.TB makes it a useful rule-in test in rapid diagnosis of TBM.
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17

Lee, Hang-Lo, Ki-Il Song, and Gye-Chun Cho. "Analysis on prediction models of TBM performance: A review." Journal of Korean Tunnelling and Underground Space Association 18, no. 2 (March 31, 2016): 245–56. http://dx.doi.org/10.9711/ktaj.2016.18.2.245.

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18

Jiang, Xing Yu, Chao Gao, Wei Xian Gao, Peng Cheng Su, and Hai Feng Zhao. "A New Integrated Performance Forecasting Model of TBM." Key Engineering Materials 693 (May 2016): 439–44. http://dx.doi.org/10.4028/www.scientific.net/kem.693.439.

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To realize accurate prediction of disk cutter performance and determine the crucial parameters in the design process of TBM (Full face tunneling boring machine), a new performance forecasting method of the circular blade cutter is presented, which integrates the parameters of breaking rock such as rock, disc cutter and construction. Furthermore, the second derivation in the process of breaking rock is considered, a new comprehensive performance forecasting model of the circular blade cutter is established based on the Coulomb-Mohr failure criterion. On the basis, the existed performance forecasting models of disc cutter such as Liner Cutting and CSM, and the performance forecasting model of disc cutter presented this paper are analyzed and calculated, according to Colorado linear cutting experiment. The calculation results indicate the prediction accuracy of the performance forecasting model presented in this paper is greatly improved, comparing with the existed models (such as CSM, linear cutting). Finally, the influences of the parameters (rock, disc cutter and construction) are analyzed, which provides for the overall design of TBM cutter and construction with some scientific basis.
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Ramanathan, K., R. A. Abdullah, A. Rahim, S. N. Jusoh, C. M. Khoo, X. Fua, Y. Xin Ban, and Q. Xie. "A Preliminary Study: Influence of Sub-Surface Ground Condition on the Tunnel Boring Machine Performance." IOP Conference Series: Earth and Environmental Science 971, no. 1 (January 1, 2022): 012020. http://dx.doi.org/10.1088/1755-1315/971/1/012020.

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Abstract Tunnelling through various ground condition affects the performance of Tunnel Boring Machine (TBM). Previous researches have conducted studies to explain on this relationship. However, a more localised influence of Malaysian Kenny Hill Formation subsurface condition towards TBM drive are yet to be carried out. This study will assess this relationship at areas nearby Museum Station during the Klang Valley Mass Rapid Transit (KVMRT) Line 1 with a length span of 540m. After the data collection from TBM and borehole report, data analysis was carried out namely using cutter head, thrust cylinder and subsurface profiling. These data were plotted based on the chainage and tunnel ring number. It is observed that subsurface profile at Ring 9 and Ring 11 required a higher thrust force due to its cohesive soil condition and inclusion of sandstone rock. However, Ring 10 is vice versa as there were more non-recovered soil samples at the specified area. The reduction of thrust force eventually increases the speed of TBM penetration. Thus, a clear relationship showing that subsurface condition highly influences the TBM drive.
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Cardu, Marilena, Ettore Catanzaro, Angelo Farinetti, Daniele Martinelli, and Carmine Todaro. "Performance Analysis of Tunnel Boring Machines for Rock Excavation." Applied Sciences 11, no. 6 (March 21, 2021): 2794. http://dx.doi.org/10.3390/app11062794.

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The study takes into account different classes of tunnel boring machines (TBM), with the aim of identifying correlation models which are meant to estimate, at a preliminary design phase, the construction time of a tunnel and to evaluate the mechanical and operational parameters of the TBMs, starting from the knowledge of the tunnel length and the excavation diameter. To achieve this goal, first of all a database was created, thanks to the collection of the most meaningful technical parameters from a large number of tunnels; afterwards, it was statistically analyzed through Microsoft Excel. In a first phase, forecasting models were identified for the three types of machines investigated, separately for compact rocks (open TBM) and fractured rocks (single and double shield TBM). Then, the mechanical parameters collected through the database were analyzed, with the aim of obtaining models that take into account, in addition to the type of TBM, the geological aspect and the type of rock characterizing the rock mass. Finally, the validation of the study was proposed in a real case, represented by the Moncenisio base tunnel, a work included in the new Turin–Lyon connection line. The estimated values were compared with the real ones, in order to verify the accuracy of the experimental models identified.
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21

Xu, Hai, Jian Zhou, Panagiotis G. Asteris, Danial Jahed Armaghani, and Mahmood Md Tahir. "Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate." Applied Sciences 9, no. 18 (September 6, 2019): 3715. http://dx.doi.org/10.3390/app9183715.

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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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Chan, Lucas Kwan Lok, and Alan Kwok Lun Kwong. "Geotechnical risks, mitigation measures and performance of tunnel boring machine (TBM) tunnels for the Tuen Mun – Chek Lap Kok Link Project, Hong Kong." Special Issue with Awarded and Shortlisted Papers from the HKIE Outstanding Paper Award for Young Engineers/Researchers 2019 26, no. 4 (December 20, 2019): 175–89. http://dx.doi.org/10.33430/v26n4thie-2019-0017.

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The Tuen Mun – Chek Lap Kok Link (TM-CLKL) project implemented latest innovation designs and ground improvement techniques in large slurry tunnel boring machines (TBM) tunnelling for constructing the sub-sea TBM tunnel in Hong Kong. Face stability, ground settlement, blow out, water ingress and ovalisation are always the major geotechnical risks for TBM tunnelling in the industry. This paper summarises the design considerations in terms of ground improvement and special arrangements of TBM tunnelling to mitigate the geotechnical risks and the improvement of the overall performance of TBM tunnelling under the TM-CLKL project. Field monitoring data such as surface settlement, ovalisation of segmental lining and ground improvement technique such as barrettes are presented.
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Faritha Banu, J., S. Neelakandan, B. T. Geetha, V. Selvalakshmi, A. Umadevi, and Eric Ofori Martinson. "Artificial Intelligence Based Customer Churn Prediction Model for Business Markets." Computational Intelligence and Neuroscience 2022 (September 29, 2022): 1–14. http://dx.doi.org/10.1155/2022/1703696.

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The introduction of artificial intelligence (AI) and machine learning (ML) technologies in recent years has resulted in improved company performance. Customer churn forecast is a difficult problem in many corporate sectors, particularly the telecommunications industry. Because customer churns have a direct impact on a company's total revenue, telecommunications firms have begun to develop 76 models to reduce churns at an earlier stage. Previous research has revealed that AI and ML models are effective CCP solutions. According to this viewpoint, this study proposes a unique AI-based CCP model for Telecommunication Business Markets (AICCP-TBM). The AICCP-TBM model's purpose is to control the existence of churners and non-churners in the telecom sector. The proposed AICCP-TBM model employs a Chaotic Salp Swarm Optimization-based Feature Selection (CSSO-FS) method for the best feature assortment. In addition, a Fuzzy Rule-based Classifier(FRC) is used to distinguish between client churners and non-churners. A technique known as Quantum Behaved Particle Swarm Optimization (QPSO) is used to pick the membership functions for the FRC model in order to improve the classification performance of the FRC model. The performance of the AICCP-TBM model is validated using a benchmark CCP dataset and the experimental results are reviewed from several angles. In relations of presentation, the imitation consequences demonstrated that the AICCP-TBM model surpassed the most recent state-of-the-art CPP models. The suggested AICCP-TBM method's comparative accuracy was thoroughly tested on the three datasets used. Using datasets 1-3, this technique obtained better levels of accuracy, with the maximum attainable values being 97.25 %, 97.5 % and 94.33 %. The simulation results for the AICCP-TBM model demonstrated improved prediction performance.
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Donovan, Joseph, Fiona V. Cresswell, Nguyen Thuy Thuong Thuong, David R. Boulware, Guy E. Thwaites, Nathan C. Bahr, Rob E. Aarnoutse, et al. "Xpert MTB/RIF Ultra for the Diagnosis of Tuberculous Meningitis: A Small Step Forward." Clinical Infectious Diseases 71, no. 8 (June 16, 2020): 2002–5. http://dx.doi.org/10.1093/cid/ciaa473.

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Abstract The delayed diagnosis of tuberculous meningitis (TBM) leads to poor outcomes, yet the current diagnostic methods for identifying Mycobacterium tuberculosis in cerebrospinal fluid (CSF) are inadequate. The first comparative study of the new GeneXpert MTB/RIF Ultra (Xpert Ultra) for TBM diagnosis suggested increased sensitivity of Xpert Ultra. Two subsequent studies have shown Xpert Ultra has improved sensitivity, but has insufficient negative predictive value to exclude TBM. Collecting and processing large volumes of CSF for mycobacterial testing are important for optimal diagnostic test performance. But clinical, radiological, and laboratory parameters remain essential for TBM diagnosis and empiric therapy is often needed. We therefore caution against the use of Xpert Ultra as a single diagnostic test for TBM; it cannot be used to “rule out” TBM.
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Lee, Hang-Lo, Ki-Il Song, Chongchong Qi, Jin-Seop Kim, and Kyoung-Su Kim. "Real-Time Prediction of Operating Parameter of TBM during Tunneling." Applied Sciences 11, no. 7 (March 26, 2021): 2967. http://dx.doi.org/10.3390/app11072967.

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With the increasing use of the tunnel boring machine (TBM), attempts have been made to predict TBM operating parameters. Prediction of operating parameters is still an important step in the adaptability of the TBM for the future. In this study, we employ a walk forward (WF) prediction method based on ARIMAX, which can consider time-varying features and geological conditions. This method is applied to two different TBM projects to evaluate its performance, and is then compared with WF based on ordinary least squares (OLS). The simulation results show that the ARIMAX predictor outperforms the OLS predictor in both projects. For practical applications, an additional analysis is carried out according to the real-time prediction distance. The results show that time series-based ARIMAX provides meaningful results in 8 rings (11 m) or less of real-time prediction distance. The WF based on ARIMAX can provide reasonable TBM operating conditions with time-varying data and can be utilized in decision-making to improve excavation performance.
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26

Jakubowski, J., J. B. Stypulkowski, and F. G. Bernardeau. "Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel." Archives of Mining Sciences 62, no. 4 (December 20, 2017): 825–41. http://dx.doi.org/10.1515/amsc-2017-0057.

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Abstract The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.
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Zheng, Xinyuan, and Junping Wang. "A Novel Metal-Organic Framework Composite, MIL-101(Cr)@MIP, as an Efficient Sorbent in Solid-Phase Extraction Coupling with HPLC for Tribenuron-Methyl Determination." International Journal of Analytical Chemistry 2019 (June 2, 2019): 1–10. http://dx.doi.org/10.1155/2019/2547280.

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A highly efficient and selective method based on core–shell molecularly imprinted polymers (MIL@MIP) and high performance liquid chromatography (HPLC) was developed and firstly used for the trace analysis of tribenuron-methyl (TBM) in complicated matrices. The MIL@MIP was prepared by surface molecular-imprinting technique, specially using MIL-101 as core, TBM as template molecule, methacrylic acid (MAA) as functional monomer, ethylene glycol dimethacrylate (EGDMA) as cross-linker, and azobisisobutyronitrile (AIBN) as initiator. The resulting MIL@MIP showed high affinity, recognition specificity, fast mass transfer rate, and efficient adsorption performance towards TBM with the adsorption capacity reaching up to 3.217 mg/g. It also showed high cross-selectivity for TBM among its six kinds of chemical structure analogues. Furthermore, using the MIL@MIP as solid-phase extraction (SPE) materials, the recoveries of TBM determined by HPLC were 84.6-92.3%, 93.3-106.7%, and 88.9-93.3% in the spiked river water, soil, and soybean samples, respectively, with the limit of detection of 0.3 ng/L, 1.5 ng/kg, and 1.5 ng/kg, accordingly. It was proved that the developed HPLC-MISPE method was fast, accurate, and sensitive for detecting the trace TBM in river water, soil, and soybean samples.
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Li, Gang, Li Da Zhu, Jian Yu Yang, and Wan Shan Wang. "A Method to Predict Disc Cutter Specific Energy for TBM Based on CSM Model." Applied Mechanics and Materials 236-237 (November 2012): 414–17. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.414.

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By using fracture mechanics theory of rock, the rock fragmentation mechanism of tunnel boring machine (TBM) cutters is analyzed and the analysis of forces of cutter is carried out. A new method to predict disc cutter specific energy for TBM is developed in this study. By using the dynamic models of TBM cutters interaction with the rock, specific energy prediction model for TBM cutter head is developed. The data from the actual tunnel construction is analyzed by using an example of Qinling tunnel and the comparison is made with the field data. The results indicate that the model developed in this study could not only replace the experiment to disc cutter specific energy for TBM, but also provide a theoretical basis for the performance prediction and optimal design of cutter head for TBM.
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Zhang, Ye, Jinqiao Chen, Shuai Han, and Bin Li. "Big Data-Based Performance Analysis of Tunnel Boring Machine Tunneling Using Deep Learning." Buildings 12, no. 10 (September 29, 2022): 1567. http://dx.doi.org/10.3390/buildings12101567.

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In tunnel boring machine (TBM) construction, the advance rate is a crucial parameter that affects the TBM driving efficiency, project schedule, and construction cost. During the operation process, various types of indicators that are monitored in real-time can help to control the advance rate of TBM. Although some studies have already been carried out in advance rate prediction, the research is almost all based on statistical methods and shallow machine learning algorithms, thereby having difficulties in dealing with a very large amount of monitored data and in modeling the time-dependent characteristics of the parameters. To solve this problem, a deep learning model is proposed based on the CNN architecture, bidirectional Long Short-Term Memory module, and the attention mechanism, which is called the CNN-Bi-LSTM-Attention model. In the first step, the monitored data is processed, and the CNN architecture is adopted to extract features from the data sequence. Then the Bi-LSTM module is adopted to obtain the time-dependent indicators. The significant features can be addressed by the added attention mechanism. In the model training process, the rotation speed of the cutter head (N), thrust (F), torque (T), penetration rate (P), and chamber earth pressure (Soil_P) are adopted to predict the advance rate. The influence of the training periods on the model performance is also discussed. The result shows that not only the data amount, but also the data periods have an influence on the prediction. The long-term data may lead to a failure of the advance rate of TBM. The model evaluation result on the test data shows that the proposed model cannot predict the monitored data in the starting stage, which denotes that the working state of TBM in the starting stage is not stable. Especially when the TBM starts to work, the prediction error is big. The proposed model is also compared with several traditional machine methods, and the result shows the excellent performance of the proposed model.
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Traore, S., S. Ouedraogo-Kone, A. Samake, M. D. Traore, and K. Bengaly. "Effet de l’association entre le tourteau de coton et les gousses de Piliostigma reticulatum (dc) Hochst sur l’ingestion et la digestibilité de la paille de riz." International Journal of Biological and Chemical Sciences 16, no. 3 (August 27, 2022): 1071–83. http://dx.doi.org/10.4314/ijbcs.v16i3.14.

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L’effet associatif entre les constituants d’une ration peut induire un effet positif ou negative sur les performances de production des ruminants,en function de la nature des constituants de la ration. De plus, La prediction de la valeur alimentaire de la ration sur la base de la somme des valeurs des constituants comporte beaucoup de limites. Deux essais ont été conduits pour évaluer l’effet associatif entre du tourteau de graine de coton non décortiquée (TBM) et les gousses de Piliostigma reticulatum (GP) sur l’ingestion volontaire (essai 1) et la fermentation in vitro à l’aide de la technique du gaz test (essai 2) de la paille de riz seule (PS) ou avec la mélasse et l’urée (PMU). Le TBM a été ajouté à PS ou à PMU dans la proportion: 70 / 30 (PS/TBM ou PMU / TBM). Les GP ont été ensuite substituées au TBM dans la proportion de 15%. Quatre rations ont été formulées pour l’essai 1: R1 (70% PS + 30% TBM), R2 (70% PMU + 30% TBM), R3 (70% PMU + 15% TBM + 15% GP) et R4 = R3 mais TBM et GP ont été distribués de façon séquentielle. Deux autres rations: 100% PS et 100% PMU en plus des rations précédentes, ont servi de substrats pour le gaz test. La ration R2 comparée à R1 a significativement (p < 0.05) amélioré les performances de croissance des taurillons. Par contre, l’apport de GP n’a pas eu d’effet car les GP n’ont pas été consommées tout au long de l’essai. Contrairement aux traitements PS et TBM (essai 2), les traitements PMU et TBM ont engendré une augmentation significative (p < 0.05) du taux horaire (c) de fermentation de la fraction insoluble (b), la concentration des acides gras volatiles (AGV) et la production de gaz à 12, 24, 36 et 48 h. L’interaction entre PMU, TBM et GP a été positive et significative. L’apport de la mélasse-urée en plus du tourteau et les gousses de Piliostigma a permis d’améliorer la valeur nutritive de la paille de riz. The associative effect between the constituents of a ration can positively or negatively affect ruminant animals ‘performances, depending on the nature of the constituents of the ration. Moreover, predicting the feeding value of the ration by simply adding the individual feed value of the constituents has some limitations. Two trials were conducted to assess the associative effect between unhulled cottonseed meal (TBM) and Piliostigma reticulatum pods (GP) on voluntary intake (trial 1) and in vitro fermentation using the gas production technique (trial 2) of rice straw alone (PS) or PS with molasses and urea (PMU). TBM was added to PS or PMU in the proportion: 70/30 (PS/TBM or PMU/TBM). The GPs were then substituted for the TBM in the proportion of 15%. Four rations were formulated for trial 1: R1 (70% PS + 30% TBM), R2 (70% PMU + 30% TBM), R3 (70% PMU + 15% TBM + 15% GP) and R4 = R3 but TBM and GP distributed sequentially. Two other rations: 100% PS and 100% PMU in addition to the previous rations, served as substrates for the gas test. The R2 ration compared to R1 significantly (p < 0.05) improved the growth performance of the young bulls. On the other hand, the supply of GP had no effect because the GP were not consumed throughout the trial. Unlike the PS and TBM treatments (trial 2), the PMU and TBM treatments generated a significant increase (p < 0.05) in the hourly rate (c) of fermentation of the insoluble fraction (b), the concentration of volatile fatty acids (VFAs) and gas production at 12, 24, 36 and 48 h. The interaction between PMU, TBM and GP was positive and significant. The supply of molasses and urea in addition to cottonseed cake and Piliostigma pods improved the nutritional value of rice straw.
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Hassanpour, Jafar, Jamal Rostami, Jian Zhao, and Sadegh Tarigh Azali. "TBM performance and disc cutter wear prediction based on ten years experience of TBM tunnelling in Iran." Geomechanics and Tunnelling 8, no. 3 (June 2015): 239–47. http://dx.doi.org/10.1002/geot.201500005.

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Ma, Tianhui, Yang Jin, Zheng Liu, and Yadav Kedar Prasad. "Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning." Applied Sciences 12, no. 13 (June 29, 2022): 6599. http://dx.doi.org/10.3390/app12136599.

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Based on the relevant data in the construction process of the south of the Qinling tunnel of the Hanjiang-to-Weihe River Diversion Project, this article obtains the main influencing factors of the tunnel boring machine (TBM) performance of the deep-buried tunnel. According to the characteristics of deep-buried tunnel excavation, the random forest algorithm is used to select the features of the factors affecting the TBM penetration rate, and the four factors with large influence weights including total thrust, revolutions per minute, uniaxial compressive strength and volumetric joint count, are used as TBM penetration rate prediction models input parameters, which can improve the prediction accuracy and convergence speed of the model, and enhance the engineering practicality of the prediction model. Three types of TBM penetration rate prediction models are established: multiple regression model (MR), back propagation neural network model (BPNN) and support vector regression model (SVR). The prediction accuracy of the three models is compared and analyzed. The BPNN prediction model exhibits better prediction performance and generalization ability than the multiple regression model and SVR model, which manifest higher prediction accuracy and prediction stability.
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Vergara, Isaac Madrid, and Charalampos Saroglou. "Prediction of TBM performance in mixed-face ground conditions." Tunnelling and Underground Space Technology 69 (October 2017): 116–24. http://dx.doi.org/10.1016/j.tust.2017.06.015.

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34

Angelone, M., R. Pilotti, F. Stacchi, M. Pillon, A. Klix, P. Raj, S. Loreti, and G. Pagano. "Performance test of radiation detectors developed for ITER-TBM." Fusion Engineering and Design 136 (November 2018): 1386–90. http://dx.doi.org/10.1016/j.fusengdes.2018.05.018.

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Dollinger, G. L., H. J. Handewith, and C. D. Breeds. "Use of the punch test for estimating TBM performance." Tunnelling and Underground Space Technology 13, no. 4 (October 1998): 403–8. http://dx.doi.org/10.1016/s0886-7798(98)00083-2.

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Rispoli, Andrea, Anna Maria Ferrero, and Marilena Cardu. "From Exploratory Tunnel to Base Tunnel: Hard Rock TBM Performance Prediction by Means of a Stochastic Approach." Rock Mechanics and Rock Engineering 53, no. 12 (August 17, 2020): 5473–87. http://dx.doi.org/10.1007/s00603-020-02226-9.

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AbstractTunnel boring machine (TBM) performance prediction is often a critical issue in the early stage of a tunnelling project, mainly due to the unpredictable nature of some important factors affecting the machine performance. In this regard, deterministic approaches are normally employed, providing results in terms of average values expected for the TBM performance. Stochastic approaches would offer improvement over deterministic methods, taking into account the parameter variability; however, their use is limited, since the level of information required is often not available. In this study, the data provided by the excavation of the Maddalena exploratory tunnel were used to predict the net and overall TBM performance for a 2.96 km section of the Mont Cenis base tunnel by using a stochastic approach. The preliminary design of the TBM cutterhead was carried out. A prediction model based on field penetration index, machine operating level and utilization factor was adopted. The variability of the parameters involved was analysed. A procedure to take into account the correlation between the input variables was described. The probability of occurrence of the outcomes was evaluated, and the total excavation time expected for the tunnel section analysed was calculated.
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Wang, Fengchao, Dapeng Zhou, Xin Zhou, Nanzhe Xiao, and Chuwen Guo. "Rock Breaking Performance of TBM Disc Cutter Assisted by High-Pressure Water Jet." Applied Sciences 10, no. 18 (September 10, 2020): 6294. http://dx.doi.org/10.3390/app10186294.

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A high-pressure water jet can break rock efficiently, which is of great potential to overcome the problems of a tunnel boring machine (TBM) in full-face hard rock tunnel digging, such as low digging efficiency and high disc cutter wear rate. Therefore, this paper presented a new tunneling method that is a TBM coupled with a high-pressure water jet. The rock failure mechanism under the coupled forces of a disc cutter and water jet was analyzed at first. Then, the finite element method (FEM) and smoothed particle hydrodynamics (SPH) method were used to establish a numerical model of rock broken by the disc cutter and water jet. Effects of parameters on rock breaking performance were studied based on the numerical model. Moreover, an experiment of the water jet cutting marble was carried out to verify the reliability of the numerical simulation. Results showed that the high-pressure water jet can increase the TBM digging efficiency and decrease the forces and wear rate of the disc cutter. The optimum nozzle diameter is 1.5 mm, while the optimum jet velocity is 224.5 m/s in this simulation. The results can provide theoretical guidance and data support for designing the most efficient system of a TBM with a water jet for digging a full-face hard rock tunnel.
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Park, Byungkwan, Chulho Lee, Soon-Wook Choi, Tae-Ho Kang, and Soo-Ho Chang. "Discrete-Element Analysis of the Excavation Performance of an EPB Shield TBM under Different Operating Conditions." Applied Sciences 11, no. 11 (May 31, 2021): 5119. http://dx.doi.org/10.3390/app11115119.

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This study used a discrete-element analysis to predict the excavation performance of a 7.73 m-diameter earth pressure balance (EPB) shield tunnel boring machine (TBM). The simulation mainly predicted several excavation performance indicators for the machine, under different operating conditions. The number of particles in the chamber and the chamber pressure varied, as the operating conditions changed during the simulated TBM excavation. The results showed that the compressive force, torque, and driving power acting on the TBM cutterhead varied with its rotation speed, increasing as the cutterhead rotation speed rose. The overall compressive force acting on all of the disc cutters and their impact wear increased linearly as the cutterhead rotation accelerated. The position of a disc cutter on the cutterhead had a particularly strong influence, with higher compressive forces experienced by the cutters closer to the center. In contrast, the gauge disc cutters at the transition zone of the cutterhead showed more wear than those elsewhere. The muck discharge rate and the driving power of the screw conveyor rose with increasing screw conveyor and cutterhead rotation speeds. Finally, this study suggests optimal operation conditions, based on pressure balance and operational management of the TBM.
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Chin, Jerome H. "Tuberculous meningitis." Neurology: Clinical Practice 9, no. 2 (March 5, 2019): 152–54. http://dx.doi.org/10.1212/cpj.0000000000000606.

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Tuberculosis (TB) surpassed HIV as the world's leading infectious cause of death in 2014. Although billions of dollars have been invested to reduce the global burden of pulmonary TB, tuberculous meningitis (TBM), the most lethal manifestation of the disease, has remained largely neglected with a paucity of evidence-based guidelines. Research is urgently needed to obtain reliable estimates of the global incidence of TBM, develop high performance technologies to detect TBM in CSF, and evaluate drug regimens with greater penetration of the CNS.
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Pradhan, N. N., M. S. Paradkar, A. Kagal, C. Valvi, A. Kinikar, S. Khwaja, R. Dhage, et al. "Performance of Xpert® MTB/RIF and Xpert® Ultra for the diagnosis of tuberculous meningitis in children." International Journal of Tuberculosis and Lung Disease 26, no. 4 (April 1, 2022): 317–25. http://dx.doi.org/10.5588/ijtld.21.0388.

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OBJECTIVE: To assess Xpert® MTB/RIF (Xpert) and Xpert® MTB/RIF Ultra (Ultra) performance in diagnosing pediatric tuberculous meningitis (TBM).METHODS: We conducted a study among children with suspected meningoencephalitis in Pune, India. Clinical, radiological, laboratory, and treatment data were analyzed to classify disease as definite, probable, possible or no TBM, using microbiologic or composite reference standards. We tested cerebrospinal fluid (CSF) either using Xpert or Ultra and estimated test performance characteristics.RESULTS: Of 341 participants, 149 (43.7%) were tested using Ultra and 192 (56.3%) with Xpert. Ultra had higher sensitivity (50% vs. 18%), lower specificity (91% vs. 99%), poor positive predictive value (PPV) (13% vs. 75%), and higher negative predictive value (NPV) (99% vs. 93%) than Xpert using the composite reference standard, with similar results by the microbiologic reference standard. Of 10 participants with trace positivity on Ultra, none met clinical TBM definitions.CONCLUSION: This is the first study to report on diagnostic performance of Ultra in pediatric TBM, which showed higher sensitivity and NPV than Xpert. For children presenting with nonspecific clinical features, Ultra is a promising diagnostic test. Further studies are required to define its optimal clinical use, including interpretation of trace positive results.
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Seong, Joo-Hyun, Yu-Seok Lee, Eun-Soo Hong, and Yo-Seph Byun. "Development of performance assessment criterion for structures of shield TBM tunnel." Journal of Korean Tunnelling and Underground Space Association 17, no. 5 (September 30, 2015): 553–61. http://dx.doi.org/10.9711/ktaj.2015.17.5.553.

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Le, Huy Ngoc, Hutcha Sriplung, Virasakdi Chongsuvivatwong, Nhung Viet Nguyen, and Tri Huu Nguyen. "The accuracy of tuberculous meningitis diagnostic tests using Bayesian latent class analysis." Journal of Infection in Developing Countries 14, no. 05 (May 31, 2020): 479–87. http://dx.doi.org/10.3855/jidc.11862.

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Introduction: Tuberculous meningitis (TBM) is the most dangerous form of tuberculosis with high mortality and disability rates. However, the delayed diagnostic process is often due to the absence of the gold standard tests leading to a lack of information about the sensitivity and specificity of diagnostic tests. This study aims to estimate the prevalence of TBM and determine the performance of four diagnostic procedures: the mycobacteria growth culture test, Gene Xpert assay, and analysis of protein levels and leukocyte count taken from cerebrospinal fluid. Methodology: We used a Bayesian latent class analysis to estimate the prevalence of TBM with 95% credible interval (CI), and the specificity and sensitivity of the four diagnostic procedures. The area under the receiver operating characteristic curve (AUC) of the cerebrospinal protein levels and leukocyte count were also compared and estimated using different thresholds. Results: A total of 1,213 patients suspected of having TBM were included. The estimated TBM prevalence was 34.8 % (95% CI: 28.8 – 41.3). The sensitivity of culture test and Gene Xpert assay was 62.7% (95% CI: 52.5 – 74.0), and 57.5% (95% CI: 51.0 – 64.0), and the specificity of Gene-Xpert was 95. 9% (95% CI: 92.0 – 99.8). The AUC for leukocyte count was 76.0%, and for protein level was 73.4%. Conclusions: This study provided better information about the performance of four routine diagnostic tests and the prevalence of TBM which can enhance disease control and improve treatment outcomes.
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Shi, Fangyu, Xia Qiu, Mingjing Yu, and Yan Huang. "Tuberculosis-specific antigen stimulated and unstimulated interferon-γ for tuberculous meningitis diagnosis: A systematic review and meta-analysis." PLOS ONE 17, no. 8 (August 30, 2022): e0273834. http://dx.doi.org/10.1371/journal.pone.0273834.

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Objective Tuberculous meningitis (TBM) is one of the most devastating TB. Accurate identification of TBM is helpful to eliminate TB. Therefore, we assessed the performance of TBAg stimulated IFN-γ (IGRA) and unstimulated IFN-γ in blood and cerebrospinal fluid (CSF) for diagnosing TBM. Methods We searched Web of Science, PubMed, Embase and the Cochrane Library databases until March 2022. Bivariate and hierarchical summary receiver operating characteristic models were employed to compute summary estimates for diagnostic accuracy parameters of IGRA and unstimulated IFN-γ in blood and CSF for diagnosing TBM. Results 28 studies including 1,978 participants and 2,641 samples met the inclusion criteria. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUROC) of blood IGRA were separately as 0.73, 0.83, 4.32, 0.33, 13.22 and 0.86, indicating a good diagnostic accuracy of blood IGRA for detecting TBM. The summary sensitivity, specificity, PLR, NLR, DOR and AUROC of CSF IGRA were separately as 0.77, 0.91, 8.82, 0.25, 34.59 and 0.93, indicating good diagnostic accuracy of CSF IGRA for detecting TBM. The summary sensitivity, specificity, PLR, NLR, DOR and AUROC of CSF IFN-γ were separately as 0.86, 0.92, 10.27, 0.16, 65.26 and 0.95, suggesting CSF IFN-γ provided excellent accuracy for diagnosing TBM. Conclusions For differentiating TBM from non-TBM individuals, blood and CSF IGRA are good assays and unstimulated CSF IFN-γ is an auxiliary excellent marker.
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Othman, Burkan. "Predicting Tbm Performance for Pila Spi Formation in Qara Dagh Anticline, NE Iraq." Iraqi Geological Journal 54, no. 1D (April 30, 2021): 105–13. http://dx.doi.org/10.46717/igj.54.1d.9ms-2021-04-29.

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There are a few studies on the engineering properties of the Pila Spi Formations in scattered areas of Northern Iraq, but there is no study on predicting the performance of Tunnel Boring Machines (TBM), which is of great importance in future large engineering projects in Iraq. The study area is located on the Takya about 50 km south the center of Sulaimania City. Field and laboratory studies were conducted on five (5) selected sites situated in the southwestern flank of the Qara Dagh anticline. The axis of the anticline extends from NW-SE of Iraq with a length of roughly 116 km. The engineering and field results of the samples collected from different locations indicate that the compressive strength of the rocks having a fine texture and slightly weathered characteristics ranges between strong to very strong with high dry density and low moisture content. In addition, the average Schmidt hammer rebound and weight losses percentages of Lose Angeles values are 48 and 22%, respectively the sizes of the blocks range from large to very large, the spacing of the discontinuities is medium to wide. The performance of TBM in terms rate of advance is generally determined based on these geological rock materials and masses. To estimate the rate advance of boring machine (TBM) in this study, the method depends on the total hardness. After applying the total hardness method, it was estimated that the predicted rate of advancement of the TBM would be very slow (approximately 1.57 -1.86 m/hr) due to the strong hardness of the rocks of Pila Spi Formation.
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de Almeida, Sergio M., Lucas B. Santana, Gilberto Golin, Gislene B. Kussen, and Keite Nogueira. "Real-time Polymerase Chain Reaction for Mycobacterium tuberculosis Meningitis is More Sensitive in Patients with HIV Co-Infection." Current HIV Research 18, no. 4 (September 8, 2020): 267–76. http://dx.doi.org/10.2174/1570162x18666200505083728.

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Background: Tuberculous meningitis (TbM) is the most severe complication of extra pulmonary tuberculosis (Tb). There is a higher frequency of positive cerebrospinal fluid (CSF) cultures for Mycobacterium tuberculosis (MTb) in samples from human immunodeficiency virus (HIV) co-infected patients than in those from HIV-negative patients. We hypothesized that real time PCR assays for MTb (MTb qPCR) using CSF would be more sensitive in HIV co-infected patients owing to a greater MTb burden. The present study aimed to verify the diagnostic performance of MTb qPCR in CSF of TbM patients who either were co-infected with HIV or were HIVnegative. Methods: A total of 334 consecutive participants with suspected TbM were divided into two groups: HIV co-infected and HIV-negative; each group was categorized into definite TbM, probable TbM, possible TbM, and TbM-negative subgroups based on clinical, laboratory and imaging data. We evaluated the diagnostic characteristics of MTb qPCR analysis to detect TbM in CSF by comparing the results to those obtained for definite TbM (i.e., positive MTb culture) and/or probable TbM in CSF, as gold standard. Results: The sensitivity of MTb qPCR in the definite and probable subgroups of the HIV coinfected participants (n = 14) was 35.7%, with a specificity of 93.8%, negative predictive value (NPV) of 94.4%, and negative clinical utility index (CUI−) of 0.89. Results of the HIV-negative group (n = 7) showed lower sensitivity (14.3%) and similar specificity, NPV, and CUI−. Conclusion: The findings confirmed our hypothesis, despite the low sensitivity. MTb qPCR may significantly contribute to diagnosis when associated with clinical criteria and complementary examinations.
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Newman, Tim, Matthew Bellhouse, John Corcoran, Richard Sutherden, and Rafik Karaouzene. "TBM performance through the engineering geology of the Lee Tunnel." Proceedings of the Institution of Civil Engineers - Geotechnical Engineering 169, no. 3 (June 2016): 299–313. http://dx.doi.org/10.1680/jgeen.15.00133.

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ISAHAI, Hisao, and Practical Application Sub-Group to. "On the Relation between In-Tunnel HSP and TBM Performance." Journal of the Japan Society of Engineering Geology 38, no. 3 (1997): 104–17. http://dx.doi.org/10.5110/jjseg.38.104.

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48

Zhang, Qianli, Weifei Hu, Zhenyu Liu, and Jianrong Tan. "TBM performance prediction with Bayesian optimization and automated machine learning." Tunnelling and Underground Space Technology 103 (September 2020): 103493. http://dx.doi.org/10.1016/j.tust.2020.103493.

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49

Geng, Qi, Zhengying Wei, Hao Meng, and Francisco Javier Macias. "Mechanical performance of TBM cutterhead in mixed rock ground conditions." Tunnelling and Underground Space Technology 57 (August 2016): 76–84. http://dx.doi.org/10.1016/j.tust.2016.02.012.

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50

Rostami, Jamal. "Hard Rock TBM Cutterhead Modeling for Design and Performance Prediction." Geomechanik und Tunnelbau 1, no. 1 (February 2008): 18–28. http://dx.doi.org/10.1002/geot.200800002.

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