[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks; DOI: 10.1186/s13640-018-0332-4; license: cc-by-sa.

[Assistant] 5 Conclusions This paper has proposed a two-phase multi-model deep learning-based system for brain tumour detection and localization from MRIs. The main goals of this study are to classify MRIs into normal and abnormal images accord- ing to the absence or presence of a brain tumour and to accurately localize the tumour within the abnormal MRIs. The first system phase employed CNN and ECOC- SVM approaches for feature extraction and classification, respectively. A five-layer R-CNN was used for tumour localization in the second system phase. The tumour detection phase was evaluated using 349 MRIs extracted from the RIDER Neuro MRI database. The empirical work has shown that the method achieved an accuracy of 99.55%. The tumour localization phase was evaluated using the BraTS 2013 database, and a DICE score of 0.87 has been achieved using 804 3D MRIs. The accom- plished results proved the superiority of the proposed deep learning-based method for tumour detection and demonstrate the superiority of the whole system’s perfor- mance concerning both tumour detection and localization measures. Abbreviations ANN: Artificial neural network; BPN: Back propagation neural network; BraTS: Brain tumour segmentation; CAD: Computer-aided diagnosis; CNN: Convolutional neural network; DNN: Deep neural network; DWTs: Discrete wavelet transforms; ECOC: Error-correcting output codes; ECOC-SVM: Error-correcting output codes support vector machine; FCM: Fuzzy C-means; FCNN: Fully convolutional neural network; FCR-NN: Fully convolutional residual neural network; GLCM: Gray-level co-occurrence matrix; HADA: Hybrid abnormality detection algorithm; HG: High grade; ICA: Independent component analysis; k-NN: K-nearest neighbours; KSVM: Kernel support vector machines; LG: Low grade; MRIs: Magnetic resonance images; NPV: Negative predictive value; PCA: Principal component analysis; PNN: Probabilistic neural network; PPV: Positive predictive value; RBFN: Radial basis function neural network; R-CNN: Region-based convolutional neural network; ReLU: Rectified linear unit; RIDER: Reference Image Database to Evaluate Response; SOMNN: Self organizing map neural network; VGG: Visual Geometry Group. Acknowledgements The authors are very thankful to the editor and the anonymous reviewers for their valuable comments and suggestions. Availability of data and materials Not applicable. The magnetic resonance images used in this study are public databases that are cited within the text. Authors’ contributions MA was the main contributor to the ANNs design, implementation, and evaluation. He worked on the MRI databases preparation and the manuscript writing as well. AA focused on the technical results and developed comparisons with the state-of-the-art. He also added valuable efforts into preparing the manuscript. AK worked on the mathematical models of ANNs and offered help in the empirical work done. He also provided a strong support to the manuscript writing process. HH supervised the research outcomes and gave help in the manuscript writing and editing processes. HH provided feedback on the whole structure of the manuscript as well. All authors read and approved the final manuscript. Ethics approval and consent to participate Not applicable. Competing interests The authors declare that they have no competing interests. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Author details 1Electronic and Communication Department, Al-Madina Higher Institute for Engineering and Technology, Giza, Egypt. 2Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, SE-97187 Luleå, Sweden. 3Faculty of Engineering, Al-Azhar University, P.O. Box 83513, Qena, Egypt. 4Faculty of Engineering, Minia University, Minia, Egypt, P.O. Box 61111, Minia, Egypt. Received: 19 September 2017 Accepted: 7 September 2018 References 1. T. Logeswari, M. Karnan, An improved implementation of brain tumor detection using segmentation based on hierarchical self organizing map. Int. J. Comput. Theory Eng. 2(4), 591 (2010) 2. E.-S. A. El-Dahshan, H. M. Mohsen, K. Revett, A.-B. M. Salem, Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Syst. Appl. 41(11), 5526–5545 (2014) 3. D. Jayadevappa, S. Srinivas Kumar, D. Murty, Medical image segmentation algorithms using deformable models: a review. IETE Tech. Rev. 28(3), 248–255 (2011) 4.


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks; DOI: 10.1186/s13640-018-0332-4; license: cc-by-sa.

[Assistant]
S. Yazdani, R. Yusof, A. Karimian, M. Pashna, A. Hematian, Image segmentation methods and applications in MRI brain images. IETE Tech. Rev. 32(6), 413–427 (2015) 5. L. Zhao, K. Jia, Multiscale CNNs for brain tumor segmentation and diagnosis. Comput. Math. Methods Med. 2016, 7 (2016). https://doi.org/ 10.1155/2016/8356294. Article ID 8356294 6. A. Madabhushi, G. Lee, Image analysis and machine learning in digital pathology: challenges and opportunities. Med. Image Anal. 33, 170–175 (2016). 20th anniversary of the Medical Image Analysis journal (MedIA) 7. J. Jiang, P. Trundle, J. Ren, Medical image analysis with artificial neural networks. Comput. Med. Imaging Graph. 34(8), 617–631 (2010) 8. D. J. Hemanth, C. K. S. Vijila, A. I. Selvakumar, J. Anitha, Performance improved iteration-free artificial neural networks for abnormal magnetic resonance brain image classification. Neurocomputing. 130, 98–107 (2014) 9. G. E. Hinton, S. Osindero, Y.-W. Teh, A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006) 10. C. Yan, F. Coenen, B. Zhang, Driving posture recognition by convolutional neural networks. IET Comp. Vision. 10, 103–11411 (2016) 11. H.-I. Suk, S.-W. Lee, D. Shen, Deep ensemble learning of sparse regression models for brain disease diagnosis. Med. Image Anal. 37, 101–113 (2017) 12. A. Krizhevsky, I. Sutskever, G. E. Hinton, in Proceedings of the 25th International ConferenceonNeuralInformation Processing Systems – Volume 1.
[page 10] Abd-Ellah et al. EURASIP Journal on Image and Video Processing (2018) 2018:97 Page 10 of 10 ImageNet Classification with Deep Convolutional Neural Networks (Curran Associates Inc., Lake Tahoe, Nevada, 2012), pp. 1097–1105 13. C. Yan, H. Xie, D. Yang, J. Yin, Y. Zhang, Q. Dai, Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans. Intell. Transp. Syst. 19(1), 284–295 (2018). https://doi. org/10.1109/TITS.2017.2749965 14. A. ¸Senta¸s, ˙I. Tashiev, F. Küçükayvaz, S. Kul, S. Eken, A. Sayar, Y. Becerikli, in Advances in Internet, Data & Web Technologies, ed. by L. Barolli, F. Xhafa, N. Javaid, E. Spaho, and V. Kolici. Performance evaluation of support vector machine and convolutional neural network algorithms in real-time vehicle type classification (Springer, Cham, 2018), pp. 934–943 15. Y. LeCun, K. Kavukcuoglu, C. Farabet, in Proceedings of 2010 IEEE International Symposium on Circuits and Systems. Convolutional networks and applications in vision (IEEE, Paris, 2010), pp. 253–256 16. J. Lei, G. Li, J. Zhang, Q. Guo, D. Tu, Continuous action segmentation and recognition using hybrid convolutional neural network-hidden Markov model model. IET Comput. Vision. 10, 537–5447 (2016) 17. The Cancer Imaging Archive, RIDER NEURO MRI Database (2016). https:// wiki.cancerimagingarchive.net/display/Public/RIDER+NEURO+MRI. Accessed 14 Sept 2018 18. M. K. Abd-Ellah, A. I. Awad, A. A. M. Khalaf, H. F. A. Hamed, in Building Sustainable Health Ecosystems: 6th International Conference on Well-Being in the Information Society, WIS 2016, Tampere, Finland, September 16-18, 2016, Proceedings, ed. by H. Li, P. Nykänen, R. Suomi, N. Wickramasinghe, G. Widén, and M. Zhan. Classification of brain tumor MRIs using a kernel support vector machine, vol. 636 (Springer, Cham, 2016), pp. 151–160 19. M. K. Abd-Ellah, A. I. Awad, A. A. M. Khalaf, H. F. A. Hamed, in IEE International Conference on Microelectronics, ICM 2016, Giza, Egypt, December 17–20, 2016. Design and implementation of a computer-aided diagnosis system for brain tumor classification (IEEE, 2016), pp. 73–76 20. C. Lakshmi Devasena, M. Hemalatha, Efficient computer aided diagnosis of abnormal parts detection in magnetic resonance images using hybrid abnormality detection algorithm. Cent. Eur. J. Comput. Sci. 3(3), 117–128 (2013) 21. S. Patil, V. R. Udupi, A computer aided diagnostic system for classification of brain tumors using texture features and probabilistic neural network. Int. J. Comput. Sci. Eng. Inf. Technol. Res. 3, 61–66 (2013) 22. M. P. Arakeri, G. R. M. Reddy, Computer-aided diagnosis system for tissue characterization of brain tumor on magnetic resonance images. Sig. Image Video Process. 9(2), 409–425 (2015) 23. E. Dandıl, M. Çakıro˘glu, Z. Ek¸si, in ICT Innovations 2014. Computer-aided diagnosis of malign and benign brain tumors on MR images (Springer, Cham, 2015), pp. 157–166 24. S. Goswami, L. K. P.

Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms; DOI: 10.3390/app12052656; license: cc-by-sa.

[Assistant] Ensemble Learning Approach for the Prediction of Quantitative Rock Damage Using Various Acoustic Emission Parameters. Appl. Sci. 2021, 11, 4008. [CrossRef] 36. Han, M.-S.; Yang, I.-J.; Kim, K.-M. A prediction of the rock mass rating of tunnelling area using artiﬁcial neural networks. J. Korean Tunn. Undergr. Space Assoc. 2002, 4, 277–286. 37. Bressan, T.S.; de Souza, M.K.; Girelli, T.J.; Junior, F. Evaluation of machine learning methods for lithology classiﬁcation using geophysical data. Comput. Geosci. 2020, 139, 104475. [CrossRef] 38. Sun, D.; Lonbani, M.; Askarian, B.; Armaghani, D.J.; Huynh, V.V. Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index. Appl. Sci. 2020, 10, 1691. [CrossRef] 39. Schimazek, J.; Knatz, H. Der Einﬂuß des Gesteinsaufbaus auf die Schnittgeschwindigkeit und den Meißelverschleiß von Streckenvortriebsmaschinen. Glückauf 1970, 106, 274–278. 40. Schimazek, J.; Knatz, H. Die beurteilung der bearbeitbarkeit von gesteinen durch schneid-und rollenbohrwerkzeuge. Erzmetall 1976, 29, 113–119. 41. Gehring, K. Möglichkeiten zur Beurteilung des Arbeitsverhaltens von Werkzeuge zur schneidenden Gesteinsbearbeitung. Berg-Und Hüttenmännische Mon. 1973, 118, 319–327. 42. Becker, H.; Lemmes, F. Gesteinsphysikalische Untersuchungen im Streckenvortrieb. Tunnel 1984, 2, 71–76. 43. Thuro, K. Bohrbarkeit Beim Konventionellen Sprengvortrieb: Geologisch-Felsmechanische Untersuchungen Anhand Sieben Ausgewählter Tunnelprojekte; Inst. für Allg. u. Angewandte Geologie d. Ludwig-Maximilians-Univ.: Berlin, Germany, 1996. 44. Torno, S.; Torao, J.; Menéndez, M.; Gent, M.; Velasco, J. Mathematical and fuzzy logic models in prediction of geological and geomechanical properties of rock mass by excavation data on underground works. J. Civ. Eng. Manag. 2011, 17, 197–206. [CrossRef] 45. Mostoﬁ, M.; Rasouli, V.; Mawuli, E. An estimation of rock strength using a drilling performance model: A case study in blacktip ﬁeld, Australia. Rock Mech. Rock Eng. 2011, 44, 305. [CrossRef] 46. Naville, C.; Serbutoviez, S.; Throo, A.; Vincké, O.; Cecconi, F.J.O. Seismic while drilling (SWD) techniques with downhole measurements, introduced by IFP and its partners in 1990–2000. Oil Gas Sci. Technol. 2004, 59, 371–403. [CrossRef] 47. Kalantari, S.; Hashemolhosseini, H.; Baghbanan, A. Estimating rock strength parameters using drilling data. Int. J. Rock Mech. Min. 2018, 104, 45–52. [CrossRef] 48. Mehmet, C.; Osman, Y.A.; Serdar, Y. Relationships between the drilling rate index and physicomechanical rock properties. Bull. Eng. Geol. Environ. 2017, 76, 253–261. 49. Aydin, G.; Karakurt, I.; Hamzacebi, C. Performance Prediction of Diamond Sawblades Using Artiﬁcial Neural Network and Regression Analysis. Arab. J. Sci. Eng. 2015, 40, 2003–2012. [CrossRef] 50. Aydin, G.; Karakurt, I.; Hamzacebi, C. Artiﬁcial neural network and regression models for performance prediction of abrasive waterjet in rock cutting. Int. J. Adv. Manuf. Technol. 2014, 75, 1321–1330. [CrossRef] 51. He, M.; Li, N.; Yao, X.; Chen, Y. A New Method for Prediction of Rock Quality Designation in Borehole Using Energy of Rotary Drilling. Rock Mech. Rock Eng. 2020, 53, 3383–3394. [CrossRef] 52. Yue, Z.Q.; Lee, C.F.; Law, K.T.; Tham, L.G. Automatic monitoring of rotary-percussive drilling for ground characterization— Illustrated by a case example in Hong Kong. Int. J. Rock Mech. Min. 2004, 41, 573–612. [CrossRef] 53. Eldert, J.V.; Schunnesson, H.; Johansson, D.; Saiang, D. Application of measurement while drilling technology to predict rock mass quality and rock support for tunnelling. Rock Mech. Rock Eng. 2019, 53, 1349–1358. [CrossRef] 54. Galende-Hernández, M.; Menéndez, M.; Fuente, M.; Sainz-Palmero, G. Monitor-While-Drilling-based estimation of rock mass rating with computational intelligence: The case of tunnel excavation front. Automat. Constr. 2018, 93, 325–338. [CrossRef] 55. Chen, L.; Chen, S.; Tu, P.; He, T. Study on Mutual Relationships between Surrounding Rock Classiﬁcations by Q Value, RMR and BQ Method for Underground Cavern. Subgrade Eng. 2017, 6, 107–112. (In Chinese) 56. Aydin, G.; Karakurt, I.; Aydiner, K. Investigation of the surface roughness of rocks sawn by diamond sawblades. Int. J. Rock Mech. Min. 2013, 61, 171–182. [CrossRef]

Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV; DOI: 10.3390/min12020268; license: cc-by-sa.

[Assistant]
Best performing machine learning vs. deep learning classiﬁcation models based on 0.12, 0.13 and 0.17 SAM cosine similarity thresholds at 2 m, 10 m and 20 m, respectively: (a) Comparisons based on global accuracy and pre-class precision; (b) Comparisons based on model training durations. Lastly, considering the UAV ﬂight logs, as well as the AI model outputs in this study, one can come to the conclusion that the most effective UAV ﬂight elevation is 20 m. At 20 m, one has a larger spatial area coverage, which eliminated the need to capture multitudes of images. Matching these variables with the attained AI model classiﬁcations, shows that the best classiﬁer at this UAV ﬂight elevation in identifying magnetite iron sands is the 1D CNN. 5. Conclusions This study proposed the employment of a 6-bands multispectral UAV drone-mounted camera in the automatic identiﬁcation of magnetite iron sands at a placer deposit. As an initial step, the UAV drone was ﬂown at three different ﬂight elevations of 2 m, 10 m, and 20 m so as to ﬁnd the optimum magnetite iron sand identiﬁcation ﬂight elevation. From the ﬂight time, power consumption and number of spectral images capturing ﬂight logs, 20 m ﬂight elevation was the most viable. This study went on to examine the SAM analysis maps at these three ﬂight elevations. It was found that the SAM cosine similarities could indeed identify the magnetite iron sand target areas at 2 m, 10 m and 20 m at 0.12, 0.13 and 0.17 cosine similarity threshold values, respectively, from the 6 bands multispectral images. Using the respective cosine similarity thresholds per ﬂight elevation, the paper contin- ued to address the viability of coupling SAM analysis segmentation maps as well as ground truths, with different AI models. It was found that the proposed coupled system did indeed yield industry viable outputs across all ﬂight heights. However, considering all outputs of this study, it was found that the best overall performance ratios were demonstrated at 20 m ﬂight elevation. At this elevation, the UAV drone was able to capture 6-bands multispectral images with a spatial resolution of 338 m2. Furthermore, using the 6-bands multispectral imagery data, a 1D CNN DL model with a global accuracy of 99.7% and a per-class precision of 99.4% was found to be the overall best AI model. System capabilities with the potential for applicability in the mining industry/sphere have been demonstrated by this study. Said applications include rock and mineral ex- ploration from a remote location; mapping of mine sites via UAV drone technology; the ability to classify rocks and minerals based on their cosine similarities (SAM analysis) to a reference spectrum; the ability to take advantage of multispectral imaging from an elevation and distance of one’s choosing, and lastly, the ﬂexibility of applying objective AI models in the classiﬁcation of rocks and minerals (as demonstrated with magnetite).
[page 18] Minerals 2022, 12, 268 18 of 19 Author Contributions: Conceptualisation, B.B.S. and Y.K.; Data curation, H.T.; Formal analysis, H.I.; Funding acquisition, Y.K. and T.A.; Investigation, B.B.S.; Methodology, B.B.S.; Resources, H.I.; Software, N.O.; Supervision, Y.K.; Validation, Z.B. and E.S.; Visualisation, H.I. and H.T.; Writing— original draft, B.B.S.; Writing—review & editing, Z.B., E.S., T.A. and Y.K. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Cooperative Research Project Program of the Penta-Ocean Construction Co., Ltd. Data Availability Statement: Not applicable. Acknowledgments: ‘Inter-University Exchange Project: An Innovative Program for Development of Core Human Resources for Smart Mining to Lead Sustainable Resource Development in Southern Africa’. Part of this work was carried out under the Cooperative Research Project Program of the Penta-Ocean Construction Co., Ltd. JSPS ‘Establishment of Research and Education Hub on Smart Mining for Sustainable Resource Development in Southern African Countries’ (JPJSCCB2018005). Conﬂicts of Interest: The authors declare no conﬂict of interest. Appendix A The procedure for coupling SAM with AI algorithms is as follows; At Figure 7 1⃝, a region within an image that is known to possess magnetite iron sands is pinpointed and used as a reference from which SAM is performed. As Figures 6 and 7 2⃝show, though a SAM may seem satisfactory from visual observations, it is rarely, if ever, 100% perfect.

Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Cutterhead and Cutting Tools Configurations in Coarse Grain Soils; DOI: 10.2174/1874836801711010182; license: cc-by.

[Assistant] The cutterhead selection and the cutting tools configuration are rather difficult in grasping the complex interaction between the pressurized shield driving and the ground. The complex interaction has been studied mainly by approaches of numerical simulations, laboratory tests and field observations. Manuel and Luis (2005) using discrete numerical models analyzed the thrust and torque needed for the shields to excavate the tunnels on two Madrid Metro extension projects, and studied soil stability problem at the tunnel face [1]. Shen et al. (2009) provided a 3D finite element model for dynamic simulation of the cutterhead and soil interaction in slurry shield tunneling by coupling the ALE approach in commercial package LS-DYNA with submodeling method in ANSYS [2]. Glenn and Mustafa (2011) analyzed critical operational characteristics during the design phase of shield machines with the use of Caterpillar proprietary discrete element method software, and examined how discrete element analysis could be applied to cutterhead performance optimization [3]. Hunt and Del Nero (2011) provided an overview of tunneling in the cobbly-bouldery ground, cited key references for more detailed study and gave emphasis to the key risk issues and approaches to mitigate them [4]. Lambrughi et al., (2012) developed a 3D numerical model using the software FLAC3D to simulate the overall process of excavation and construction of an EPB shield tunnel with the emphasis on the reasonable constitutive equation of soil [5]. Wu et al. (2013) presented a 3D model for cutterhead excavation system of an EPB machine with the discrete element method software PFC3D, and the measured system performance parameters indicating stability of excavation face, soil discharging rate, cutterhead system torque and cutter wear were in accordance with the in-situ data [6]. Katushin et al. (2013) briefly explained the importance and gave the insight on laboratory wear testing of the drag bit materials for tunneling machines [7]. Mosleh et al. (2013) presented an experimental study of tool wear using a laboratory tester which simulated the operating conditions of cutting tools in soil excavation operations, and analyzed effects of the relative hardness and moisture on tool wear in soil excavation operations [8]. Jakobsen et al. (2013) summarized the development of the Soft Ground Abrasion Tester (SGAT) apparatus, and showed its capabilities to evaluate, quantify and compare how the soil mineralogy, water content, pressure, compaction, and the use of soil conditioning additives influenced the wear rate on the SGAT excavation tool [9]. Nishitake (1987) described the EPB shields designed by Mitsubishi Heavy Industries, Ltd. to cope with large size boulders scattered in rather soft alluvial soil, which were equipped with both drag bits and rolling disc cutters on the cutterheads [10]. Dowden and Cass (1991) summarized the designs of shielded type machines and their application, and discussed the new developments which blend hard and soft ground tunneling technology [11]. Burger (2007) recalled evolution of the design of both slurry and EPB cutterheads, reviewed the state of the art, and highlighted both common as well as geological specific design features [12]. Herrenknecht and Bӓpler (2007) focused on the design and technical challenges of the three largest soft ground tunnel boring machines: two slurry shields (15.4 m diameter) for the Shanghai River Crossing project and one EPB shield (15.2 m diameter) for a road tunnel project in Madrid in Spain [13]. Burger and Wehrmeyer (2008a, 2008b) examined the development of the modern mixshield including the design of cutterhead, and looked at two particularly influential projects and their impact on the future mixshield technology [14, 15]. Metzger et al. (2009) focused on design considerations of the largest mixshield machine ever implemented in North America to excavate the 8534 linear m × 6.7 m ID tunnel for the City of Portland Oregon’s, Bureau of Environmental Services East Side CSO Tunnel Project, which included machine configuration, extended-wear protection, hyperbaric interventions, boulder digestion and so on [16]. Maidl and Comulada (2011) pointed out the key performance indicators for estimating performance of the EPB shields based on the evaluation of data from a great of projects [17].

Cutterhead and Cutting Tools Configurations in Coarse Grain Soils


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Cutterhead and Cutting Tools Configurations in Coarse Grain Soils; DOI: 10.2174/1874836801711010182; license: cc-by.

[Assistant]
On the other hand, reliable information about conditions of the cutting tools is an essential factor to minimize interventions to the necessary minimum and reduce risk of the extended compressed air work for repairing damaged tools and cutterheads. Using ultrasonic inspection equipment to detect wear of the cutting tools remains a promising approach with the ongoing development of the relevant technologies [22, 28]. CONCLUSION Based on the above statements and discussions, the following is concluded: The spoke type cutterheads equipped with precut bits, shell bits, scrapers and a center fish-tail bit are strongly 1. recommended for the shield tunneling in loose coarse grain soils without groundwater. The spoke-plate or plate type cutterhead with reasonable opening ratio is a better choice if the first priority is to 2. guarantee stability of the cutting face or mechanized driving will be performed in ground conditions rich in groundwater. The reasonable opening ratio of a cutterhead can be decided based on a good estimate of the size of particles in soils as well as experiences from the previous similar construction projects. Reasonable combinations of tools and proper height differences among tools for the specific particle size 3. distributions are of paramount importance to realize the efficient boring of tunnels in the coarse grain soils. Especially, the precut bits are suggested to be installed on both spoke type and plate type cutterhead on account of good performances revealed. The disc cutters, if mounted on cutterheads, will be single disc cutters with tungsten carbide inserts. Enough aggressive tools and robust cutterheads allowing for longer driving in the coarse grain soils are 4. suggested to be prepared or reserved to ensure the scheduled construction time lest the unforeseen scenarios or more difficult ground conditions will be encountered In the case that interventions and chamber access for changing tools are indispensable, the tools replacement 5. plan should be well planned beforehand and the operation procedure should be well prepared, as is helpful to reduce adverse effects caused by the over-sized particles on shield tunneling. CONFLICT OF INTEREST The authors confirm that this article content has no conflict of interest. ACKNOWLEDGEMENTS The authors gratefully acknowledge the financial support by the National Basic Research Program of China under Grant 2015CB057800. REFERENCES [1] M.J. Manuel, and L.E. Rodríguez, "Discrete numerical model for analysis of earth pressure balance tunnel excavation", J. Geotech. Geoenviron. Eng., vol. 131, pp. 1234-1242, 2005. [http://dx.doi.org/10.1061/(ASCE)1090-0241(2005)131:10(1234)] [2] J. Shen, X. Jin, Y. Li, and J. Wang, "Numerical simulation of cutterhead and soil interaction in slurry shield tunneling", Eng. Comput., vol. 26, pp. 985-1005, 2009. [http://dx.doi.org/10.1108/02644400910996862] [3] M. Glenn, and A. Mustafa, "Discrete Element Method to predict soft ground cutterhead performance", In: Rapid Excavation and Tunneling Conference Proceedings, 2011, pp. 1058-1067. [4] S.W. Hunt, and D.E. Del Nero, "Tunneling in cobbles and boulders", In: Breakthroughs in Tunneling Short Course, Colorado School of Mines, September 19-21, 2011. [5] A. Lambrughi, L. Medina, Rodríguez, and R. Castellanza, "Development and validation of a 3D numerical model for TBM-EPB mechanised
[page 18] 198 The Open Construction and Building Technology Journal, 2017, Volume 11 Li et al. excavations", Comput. Geotech., vol. 40, pp. 97-113, 2012. [http://dx.doi.org/10.1016/j.compgeo.2011.10.004] [6] L. Wu, T. Guan, and L. Lei, "Discrete element model for performance analysis of cutterhead excavation system of EPB machine", Tunn. Undergr. Space Technol., vol. 37, pp. 37-44, 2013. [http://dx.doi.org/10.1016/j.tust.2013.03.003] [7] D. Katushin, M. Antonov, T.M. Vu, and D. Yung, "Laboratory testing of materials for tunnel boring machine drag bits", In: 3 th International Symposium PÄRNU, 2013, pp. 300-303. [8] M. Mosleh, E.A. Gharahbagh, and J. Rostami, "Effects of relative hardness and moisture on tool wear in soil excavation operations", Wear, vol. 302, pp. 1555-1559, 2013. [http://dx.doi.org/10.1016/j.wear.2012.11.041] [9] P.D. Jakobsen, L. Langmaack, F. Dahl, and T. Breivik, "Development of the Soft Ground Abrasion Tester (SGAT) to predict TBM tool wear, torque and thrust", Tunn. Undergr. Space Technol., vol. 38, pp. 398-408, 2013. [http://dx.doi.org/10.1016/j.tust.2013.07.021] [10] S.


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Application of machine learning in predicting the rate-dependent compressive strength of rocks; DOI: 10.1016/j.jrmge.2022.01.008; license: cc-by-sa.

[Assistant]
Therefore, the ﬁnal output obtained from a new input vector x is: y ¼ 1 t X t l ¼ 1 ~yl ¼ 1 t X t l ¼ 1 ~hðx; ~SlÞ (13) An optimal RF-based model is realized by tuning the number of decision trees and the depth of each decision tree. The model can be more robust and accurate with a larger number of decision trees. However, the number is also associated with the calculation time, and the prediction accuracy is prone to stabilizing after hitting a critical point. The depth of the decision tree affects its own strength and the correlation between the trees. The purpose of the RF is to ﬁnd a trade- off among the calculation time, decision tree strength, and correlation between the trees. When the terminal conditions are reached, the model is considered acceptable, and the training is completed. 3. Model training and validation We trained three machine learning models based on the SVM, the BPNN, and the RF algorithms by randomly selecting three quarters of the data in Table A1 and checked the performance of these models based on the remaining data. The compressive strength was the output, and the other parameters in Table A1 were used as the inputs (i.e. static compressive strength, P-wave velocity, specimen dimension, grain size, bulk density, and strain rate). We ﬁrst qualitatively evaluated the performance of the three models based on the comparison between the predicted and the measured compressive strengths (Fig. 6). A data point on or close to the diagonal line indicates that the predicted compressive strength matches with the measured compressive strength. The data points with a small deviation from the diagonal line are mainly distributed in the region less than 120 MPa compressive strength. This is because the data points are densely distributed in this region, as shown in the histograms next to the upper and right axes. The results indicate that the amount of data points is critical for the accuracy of machine learning prediction. We then quantitatively compared the performance of these models based on the following metrics: MAE ¼ 1 n X n i ¼ 1 jyi  byij (14) MXAE ¼ maxjyi  byij (15) MAPE ¼ 1 n X n i ¼ 1  yi  byi byi   100% (16) Fig. 5. A schematic diagram of the principle of RF algorithm. M. Wei et al. / Journal of Rock Mechanics and Geotechnical Engineering 14 (2022) 1356e1365 1360
[page 6] RMSE ¼ ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ P n i ¼ 1 ðyi  byiÞ2 n v u u u t (17) where MAE, MXAE, MAPE, and RMSE denote the mean absolute er- ror, the maximum absolute error, the mean absolute percentage error, and the root mean square error, respectively; n represents the number of data points; and yi and byi are the predicted and the measured values for the ith strength value, respectively. The performance metrics plotted in Fig. 7 demonstrate that the compressive strengths predicted by the three machine learning models are mostly similar. The RF-based performance metrics are lower than the SVM- and BPNN-based performance metrics, manifesting the superiority of the RF over the SVM and the BPNN in predicting the compressive strength of rocks. The BPNN-based performance metrics are lower than the SVM-based performance metrics, except the MAPE. The high MAPE value is attributed to the signiﬁcant absolute error obtained from the SVM-based model when predicting a high strength value. Given the better perfor- mance of the RF-based model, we used this model to investigate the rate dependence of rock strength in Section 4. 4. Discussion 4.1. Sensitivity analysis Our study demonstrates that the machine learning models perform well to predict the compressive strength of rocks using the six input parameters (i.e. static compressive strength, P-wave ve- locity, specimen dimension, grain size, bulk density, and strain rate). In rock engineering, one may not be able to collect sufﬁcient data points to train the three models and to ensure the accuracy of machine learning prediction. Insufﬁcient data points of single input parameter may inﬂuence signiﬁcantly or negligibly the perfor- mance of these models. Here, we performed a sensitivity analysis to evaluate the importance of each input parameter in the strength prediction. In each model, we excluded one of the six input pa- rameters in turn. In other words, we inputted the other ﬁve pa- rameters in the model and compared the performance difference between the model with ﬁve input parameters and the model with six input parameters.

Application of machine learning in predicting the rate-dependent compressive strength of rocks


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Application of machine learning in predicting the rate-dependent compressive strength of rocks; DOI: 10.1016/j.jrmge.2022.01.008; license: cc-by-sa.

[Assistant] Appl. Sci. 11 (19), 8973. Zhou, Y.X., Xia, K., Li, X.B., Li, H.B., Ma, G.W., Zhao, J., Zhou, Z.L., Dai, F., 2012. Sug- gested methods for determining the dynamic strength parameters and mode-I fracture toughness of rock materials. Int. J. Rock Mech. Min. Sci. 49, 105e112. Zhou, Z., Cai, X., Chen, L., Cao, W., Zhao, Y., Xiong, C., 2017. Inﬂuence of cyclic wetting and drying on physical and dynamic compressive properties of sandstone. Eng. Geol. 220, 1e12. Zhou, Z., Cai, X., Li, X., Cao, W., Du, X., 2020. Dynamic response and energy evolution of sandstone under coupled staticedynamic compression: insights from experimental study into deep rock engineering applications. Rock Mech. Rock Eng. 53, 1305e1331. Mingdong Wei is currently a research fellow in the School of Civil and Environmental Engineering, Nanyang Tech- nological University (NTU), Singapore. His research in- terests include rock fracture mechanics, rock dynamics, engineering geology, and cold regions geomechanics. He worked as a postdoctoral researcher at Aalto University, Finland, after obtaining his doctorate and bachelor’s de- gree from Sichuan University, China. He has authored or co-authored more than forty academic publications. He was awarded the Excellent Doctoral Dissertation Award 2018 by the Chinese Society for Rock Mechanics and Engi- neering, the Best Presentation Award 2021 (2nd runner- up) by the Society for Rock Mechanics & Engineering Geol- ogy, Singapore, the Research Scholarship Block (RSB)-Post- doctoral Fellowship 2019 by NTU, and the National Scholarship for Doctoral Students in China. M. Wei et al. / Journal of Rock Mechanics and Geotechnical Engineering 14 (2022) 1356e1365 1365


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Reinforcement learning based process optimization and strategy development in conventional tunneling; DOI: 10.1016/j.autcon.2021.103701; license: cc-by.

[Assistant]
While [14], who trained their DQN to play classical Atari 2600 games, took a modified version of the original games’ scoring systems as a means to deal with the reward in each timestep, [15] only gave a reward of +1 or − 1 at the end of the game depending on whether or not the agent has won the respective Go-match. In our tunneling simulation, we designed a hierarchical point system that values (i.e. rewards/penalizes) either a state or an action. The rewarding/penalizing is done based on a list of conditions, that is worked through from top to bottom after every step of an episode and the agent is given the first reward where the condition is fulfilled. Except for the reward for achieving a breakthrough, all rewards are negative (i. Fig. 6. 3D visualization of the 2 × 2100 × 2 hypermatrix that represents the state of the simulation. G.H. Erharter et al.
[page 7] Automation in Construction 127 (2021) 103701 7 e. penalties) and from top to bottom the penalties are sorted from biggest to smallest. It is therefore possible that multiple conditions are fulfilled in one state, but only the most severe penalty is given. Table 4 shows the list of rewards and their respective conditions. We designed the list of rewards based on practical engineering experience and requirements arising from training a completely unex perienced agent. For example, we penalize face instabilities higher than a too far distance between top heading and bench excavation, as the immediate safety hazard of an unstable face is bigger. In contrast to that, an even bigger penalty (i.e. -6 points) is triggered if the agent would try to do bench excavation ahead of the top heading excavation. A geo technician in real world tunneling would know from the start that doing so brings numerous technical-, logistical- and safety problems, but an untrained RL-agent does not. During the experiments for this study we tried to design the reward system in a way that it is the sum of all penalties that would be fulfilled in a given state and not a hierarchical system. However, doing this was not beneficial for training and seemed to confuse the agent as it apparently did not know what it was punished for. For example, a combination of the penalty for a too far distance between top heading and bench (i.e. -3) plus the penalty for using face support (i.e. -2) would sum up to −5 which is the same penalty as the one for unstable face conditions. As given in Section 2.1, the evaluation to check if a newly excavated face is stable or not, is done based on the face pressure equation for open face tunneling from [29] (Eq. (3)). In Eq. (3), pf is the required pressure to achieve stable face conditions, where a pf < 0 indicates stable con ditions and a pf ≥0 indicates unstable conditions, respectively the amount of pressure that is necessary to stabilize the face. γR is the ground’s unit weight, D the (equivalent) tunnel diameter (see chapter 3.1), d the advance length (describing the unsupported area of the un lined wall), c′ the effective cohesion and φ′ the effective friction angle. pf = γRD* ⎛ ⎜ ⎜ ⎜ ⎝ 2 + 3* ( d D )6*tanφ ′ 18*tanφ ′ −0.05 ⎞ ⎟ ⎟ ⎟ ⎠− c ′ tanφ ′ (3) 4. Training In RL, one episode is the whole succession of states in between an initial and a terminal state [26]. In other words, an episode is one whole match of a game, or in this simulation, one whole sequence of actions that ultimately should lead to a breakthrough of the tunnel. We started training with an ε of 1 (i.e. “initial exploration” in Table 2) to promote exploration in the initial phase of training. The exploration decay of 0.99997 (see Table 2) that decreases ε following Eq. (2) was determined by trial and error. A smaller exploration decay (i.e. faster reaching of the final exploration) has shown to increase in stabilities in the training process at an early stage which are presenting themselves in spontaneous increases of the loss and decreases of the reward. With the given exploration decay, the final exploration is reached after 99,858 episodes of training. After this point ε is kept at a constant value of 0.05. Training does not need to be aborted after reaching the final exploration. In the current simulation, we aimed at training the agents for 120,000 episodes to observe one full epsilon decay and some episodes beyond that to check for stable conditions in different rockmasses, as illustrated in Fig. 7 (except for cases where training became unstable at some point; see next section).

Reinforcement learning based process optimization and strategy development in conventional tunneling


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks; DOI: 10.1186/s13640-018-0332-4; license: cc-by-sa.

[Assistant]
4.2 Accuracy evaluation criteria To evaluate the two system phases, the following stan- dard metrics were used: sensitivity, which represents the proportion of actual positives that are correctly classified; specificity, which indicates the proportion of negatives Table 7 Performance analysis of the developed localization R-CNN-based method compared to other methods in the literature Localization method Performance evaluation metrics (%) Layers Sensitivity PPV DICE Havaei et al. [29] 10 87.00 88.00 0.88 Chang [30] 22 – – 0.87 Casamitjana et al. [31] 22 83.08 84.60 0.84 Zhao et al. [32] 16 83.00 92.00 0.87 Pereira et al. [33] 51 93.00 80.00 0.85 Pereira et al. [34] 11 89.00 88.00 0.83 Proposed method 05 95.44 81.80 0.87
[page 8] Abd-Ellah et al. EURASIP Journal on Image and Video Processing (2018) 2018:97 Page 8 of 10 that are correctly classified; and accuracy, which is the proportion of both true positives and true negatives. These three metrics were calculated using Eqs. 8, 9, and 10, respectively [18, 19]: Sensitivity = σ σ +  (8) Specificity = η η +  (9) Accuracy = σ + η σ + η +  +  (10) where true positives (σ) are the correctly classified pos- itive cases, true negatives (η) are the correctly classified negative cases, false positives () are the incorrectly classified positive cases, and false negatives () are the incorrectly classified negative cases. In addition to sensitivity, accuracy, and specificity, other parameters have been considered for evaluating tumour detection and localization phases. The new parame- ters were balanced accuracy (BA) = (sensitivity + speci- ficity)/2, positive predictive value (PPV) = σ/(σ + ), and negative predictive value (NPV) = η/(η + ) [11]. Addi- tionally, DICE scores are considered a common parameter for tumour localization approaches. 4.3 Tumour detection accuracy analysis The performance of the CNN is measured in every net- work layer. The goal of this step is to identify the optimum number of network layers for achieving the highest accu- racy and the shortest feature extraction time. The mea- sured performance of each layer is recorded in Table 3. It is demonstrated in Table 3 that setting the number of layers equal to 9 provided the best performance. Table 4 shows the best accuracy obtained from the three CNN models used in this study and the corresponding layer number. The network size has been optimized to give the best performance in learning phase and then has been fixed, with the validation and testing phases using the same network size as the learning phase. This is to generalize the CNN model for all data samples. To further investigate the performance of classical learning models against the proposed deep CNN-based model, a classical brain tumour diagnosis method has been applied and studied using the same database con- figurations as in Table 2. The proposed CNN system achieved 99.55% accuracy, while the classical method accomplished 66.96%. A short comparison between the performance of the previous classical method and the newly proposed deep learning system using the same database is presented in Table 5. The obtained results in Table 5 show the superiority of the deep learning method in terms of brain tumour detection accuracy, which is reflected in the MRI classification. The proposed CNN-based system has achieved 99.55% accuracy for tumour diagnosis when selecting nine CNN layers. To ensure the credibility of the obtained results, Fig. 4 Visual results in the axial view from the localization phase. The top row shows the T2 modality. The second row shows the ground truth. The bottom row shows the segmented images
[page 9] Abd-Ellah et al. EURASIP Journal on Image and Video Processing (2018) 2018:97 Page 9 of 10 a comparison with results from the literature was per- formed. The outcome of the proposed CNN-based system compared with those of three popular methods reported in the literature is reported in Table 6. 4.4 Tumour localization accuracy analysis Simulation experiments have been conducted to show the performance of the proposed system in fulfiling the tumour localization task. A comparison with other studies found in the literature is shown in Table 7. It is clear that the proposed system is superior to the others in terms of the network size, sensitivity and DICE metrics but not the PPV metric. A sample of visual results from the MRIs database used in the localization phase of the experimental work is shown in Fig. 4.


[System] You are Gemma4Tunnel, a careful tunnel boring machine and tunnelling engineering assistant. Use engineering terminology precisely, state assumptions, and avoid inventing source claims.

[User]
Extract reusable tunnelling/TBM engineering knowledge from this allowed source excerpt. Preserve important parameters, mechanisms, risks, and caveats. Do not add unsupported claims.

Source: Cutterhead and Cutting Tools Configurations in Coarse Grain Soils; DOI: 10.2174/1874836801711010182; license: cc-by.

[Assistant] Among others, Le (2003) recommended the key technical parameters concerning configurations of the shield cutterhead and cutting tools based on analyzing typical ground conditions encountered in Beijing area [24]; Wang (2005) introduced the first application of the EPB tunneling method in Beijing subway construction to dig Line 5 tunnels and described the main design parameters of the shield machine [25]; Liu and Song (2007) dealt with the problem of cutting tools wear in building Chengdu metro Line 1 tunnels in coarse grain soils and presented some recommendations after investigating failure causes of the disc cutters and scrapers [26]; Huang (2008) discussed influences of the oversized grains in the water-rich sandy gravel soils of Lot 13 of Shenyang metro Line 2 on configurations of the cutterhead and cutting tools as well as layout of the screw conveyor [27]; Zhang et cl. (2008) presented the monitored abrasions of cutting tools used in building some shield tunnel of Beijing subway line 4, and established the relation between abrasion coefficients of tools and shield tunneling parameters [28]; Wang (2008) analyzed the crucial technical parameters, such as opening ratio of the cutterhead and combination of the cutting tools fit for Chengdu geological conditions relying on the experiences and lessons from the construction of Phase 1 projects of Chengdu metro Line 1 [29]; Wang and Zhang (2011) studied the arrangement and structure of the cutterhead and cutting tools suitable for boring subway tunnels in Shenyang medium-coarse sand stratum in virtue of the experiences from construction of the tunnels on Shenyang metro Line 1 [30]; Zhu (2011) briefly illuminated the use of the spoke type cutterhead with opening ratio of 60% and the combination of precut bits and face scrapers in the Phase 2 Lot 7 project of Beijing subway Line 10 [31]. Nearly all known publications in China concentrated on only a single case study mainly because of the tribal knowledge of the involved tunnel project. Generalized studies from the cases study in combination with some undocumented recordings are necessary to improve the shield tunneling work, resulting in a more economic process in the coarse grain soils and further providing references for the succeeding similar projects. Due to the theoretical difficulties in mastering the interactions between coarse grain soils and cutting tools, the common understanding and recognition reached from the in-situ performances of the shield cutterheads remain the well accepted approach for configurations of the cutterhead and cutting tools suitable to dig tunnels in the coarse grain soils. On the basis of briefly summarizing main characteristics of the coarse grain soils disclosed in China, the conclusions on which consensus can be reached are generalized from the typical construction cases, and the rule-of-thumb suggestions on the configurations of the cutterhead and cutting tools are put forward. 2. MAIN CHARACTERISTICS OF COARSE GRAIN SOILS ENCOUNTERED IN CHINA It is of paramount importance to grasp mechanical and physical properties of the coarse grain soils to be encountered when the EPB shield tunneling method will be utilized. Presented in Fig. (1) is the typical sandy gravel/cobble soil with boulders found in building Beijing subway tunnels. Main characteristics of the coarse grain soils disclosed in China subway tunnel construction can be briefly summarized as follows: Typical physical and mechanical properties are the large content of 30-70% by volume of coarse grains (gravel, 1. cobble, and boulder), the general grain size of 30-200 mm with the maximum size of over 500 mm and the uniaxial compressive strength of grains as high as 180 MPa, taking the coarse soils encountered in Beijing for instance. Particularly, in the tunnel project of Lot 3 of Beijing subway Line 9, the content of the coarse grains is as high as 80%, and the maximum particle size is more than 1000 mm. The coarse grain soils are of unfavorable plasticity and fluidity, which will bring serious abrasions to shield 2. machines.