최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기터널과 지하공간: 한국암반공학회지 = Tunnel and underground space, v.32 no.6, 2022년, pp.502 - 517
강태호 (한국건설기술연구원 지반연구본부) , 최순욱 (한국건설기술연구원 지반연구본부) , 이철호 (한국건설기술연구원 지반연구본부) , 장수호 (한국건설기술연구원 지반연구본부)
As the use of TBM increases, research has recently increased to to analyze TBM data with machine learning techniques to predict the exchange cycle of disc cutters, and predict the advance rate of TBM. In this study, a regression prediction of disc cutte wear of slurry shield TBM site was made by com...
Armaghani, D.J., Mohamad, E.T., Narayanasamy, M.S., Narita, N., and Yagiz, S., 2017, "Development of hybrid intelligent?models for predicting TBM penetration rate in hardrock condition", Tunn. Undergr. Space Technol., 63, 29-43.
Breiman, L., 1996. "Bagging predictors", Machine Learning, 24, 123-140. https://doi.org/10.1007/ BF00058655.
Breiman, L., Friedman, J., Stone, C.J., and Olshen, R.A., 1984, "Classification and Regression Trees", CRC press.
Bruland, A., 1998, "Hard rock tunnel boring advance rate and cutter wear", Doctoral Thesis at NTNU, 3, 81.
Chen, R., Zhang, P., Wu, H., Wang, Z., and Zhong, Z., 2019, " Prediction of shield tunneling-induced ground settlement using?machine learning techniques", Front. Struct. Civ. Eng., 13, 1363-1378.
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., and Chen, K., 2015, Xgboost: extreme gradient boosting, R?package version 0.4-2, 1(4), 1-4.
Cover, T. and Hart, P., 1967, "Nearest neighbor pattern classification", in IEEE Transactions on Information Theory, 13(1), 21-27,?doi: 10.1109/TIT.1967.1053964.
Friedman, J. H., 2001, Greedy function approximation: a gradient boosting machine, Annals of Statistics, 1189-1232.
Gehring, K., 1995, "Leistungs-und verschleissprognosen im maschinellen tunnelbau", Felsbau, 13(6), 439-448.
Jung, J.-H., Kim, B.-K., Chung, H., Kim, H.-M., and Lee, I.-M., 2019, "A ground condition prediction ahead of tunnel face utilizing?time series analysis of shield TBM data in soil tunnel", Journal of Korean Tunnelling and Underground Space Association,?21(2), 227-242.
Kang, T. H., Choi, S.W., Lee, C., and Chang, S.H., 2021, A Study on the Prediction of Rock Classification Using Shield TBM Data?and Machine Learning Classification Algorithms, Tunnel and Underground Space, 31(6), 494-507.
Kang, T.-H., Choi, S.-W., Lee, C., and Chang, S.-H., 2020, "A Study on Prediction of EPB shield TBM Advance Rate using?Machine Learning Technique and TBM Construction Information", Tunnel and Underground Space, 30(6), 540-550.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T. Y., 2017, Lightgbm: A highly efficient gradient?boosting decision tree, Advances in Neural Information Processing Systems, 30.
Kearns, M. and Valiant, L.G., 1994, "Cryptographic limitations on learning Boolean formulae and finite automata", Journal of the?Association for Computing Machinery, 41, 67-95.
Kim, D., Kwon, K., Pham, K., Oh, J.Y., and Choi, H., 2022, Surface settlement prediction for urban tunneling using machine?learning algorithms with Bayesian optimization, Automation in Construction, 140, 104331.
Kim, T.H., Ko, T.Y., Park, Y.S., Kim, T.K., and Lee, D.H., 2020a, "Prediction of Uniaxial Compressive Strength of Rock using?Shield TBM Machine Data and Machine Learning Technique", Tunnel & Underground Space, 30(3), 214-225.
Kim, Y., Hong, J., and Kim, B., 2020b, "Performance comparison of machine learning classification methods for decision of disc?cutter replacement of shield TBM", Journal of Korean Tunnelling and Underground Space Association, 22(5), 575-589.
Ko, T.Y., Yoon, H.J., and Son, Y.J., 2014, "A comparative study on the TBM disc cutter wear prediction model", Journal of Korean Tunnelling and Underground Space Association, 16(6), 533-542.
La, Y. S., Kim, M.I., and Kim, B., 2019, "Prediction of replacement period of shield TBM disc cutter using SVM", Journal of?Korean Tunnelling and Underground Space Association, 21(5), 641-656.
Mokhtari, S. and Mooney, M.A., 2020, "Predicting EPBM advance rate performance using support vector regression modeling",?Tunn. Undergr. Space Technol., 104, 103520. https://doi.org/10.1016/j.tust.2020.103520.
Rosenblatt, F., 1958, The perceptron: a probabilistic model for information storage and organization in the brain, Psychological?Review, 65(6), 386.
Rostami, J. and Ozdemir, L., 1993, "A new model for performance prediction of hard rock TBMs", Proceedings of the Rapid?Excavation and Tunneling Conference (RETC), Boston, U.S.A., pp. 793-809.
Rumelhart, D.E., Hinton, G.E., and Williams, R.J., 1986, "Learning Internal Representations by Error Propagation", David E.?Rumelhart, James L. McClelland, and the PDP research group. (editors), Parallel distributed processing: Explorations in the?microstructure of cognition, Volume 1: Foundation. MIT Press.
Vapnik, V., 1995, "The Nature of Statistical Learning Theory. Springer", New York.
Yagiz, S. and Karahan, H., 2011, "Prediction of hard rock TBM penetration rate using particle swarm optimization", Rock?Mechanics and Mining Science, 48(3), 427-433.
Yagiz, S., 2008, "Utilizing rock mass properties for predicting TBM performance in hard rock condition", Tunn. Undergr. Space?Technol., 23(3), 326-339.
Yang, H., Song, K., and Zhou, J., 2022, "Automated recognition model of geomechanical information based on operational data of?tunneling boring machines", Rock Mech. Rock Eng., 55, 1499-1516.?
※ AI-Helper는 부적절한 답변을 할 수 있습니다.