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Fine-tuning of line and slope based on evolutionary mechanism

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Abstract

The line and slope fine-tuning is the process of optimizing the horizontal and longitudinal sections to meet the requirements of building clearance, which is an indispensable step in the building engineering. The traditional line and slope fine-tuning, which is manually completed by designers, depends heavily on the domain knowledge of designers. The more experienced the designer is, the better the effect of line and slope fine-tuning will be. This paper makes a first attempt to apply the evolutionary algorithm to the process of line and slope fine-tuning. The main work includes: a new denoising method for tunnel point cloud data is proposed to remove noisy and redundant data from point cloud; an objective function is given to measure the deviation between the design tunnel and the real tunnel; and a learning model of the line and slope fine-tuning is built based on the point cloud data and evolutionary algorithm. A dataset from a length of the real metro tunnel is used for model testing. The testing results show that, in comparison with the traditional manually-adjusting method, our approach based on the evolutionary algorithm can significantly reduce the deviation between the adjusted tunnel and the real tunnel.

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References

  1. Argüelles-Fraga R, Ordóñez C, García-Cortés S, Roca-Pardiñas J (2013) Measurement planning for circular cross-section tunnels using terrestrial laser scanning. Autom Constr 31:1–9

    Article  Google Scholar 

  2. Bellman R (1966) Dynamic programming. Science 153(3731):34–37

    Article  Google Scholar 

  3. Ding L, Zhang L, Xianguo W, Skibniewski MJ, Qunzhou Y (2014) Safety management in tunnel construction: case study of Wuhan metro construction in china. Saf Sci 62:8–15

    Article  Google Scholar 

  4. Dong C, Ng WWY, Wang X, Chan PPK, Yeung DS (2014) An improved differential evolution and its application to determining feature weights in similarity-based clustering. Neurocomputing 146:95–103

    Article  Google Scholar 

  5. Du L, Zhong R Sun H, Wu Q (2017) Automatic monitoring of tunnel deformation based on high density point clouds data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W7, 2017 ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China

  6. Easa SM (1988) Earthwork allocations with linear unit costs. J Constr Eng Manag 114(4):641–655

    Article  Google Scholar 

  7. Fogel DB (1997) Evolutionary algorithms in theory and practice. Complexity 2(4):26–27

    Article  Google Scholar 

  8. Aimin F, Wang X, He Y, Wang L (2014) A study on residence error of training an extreme learning machine and its application to evolutionary algorithms. Neurocomputing 146:75–82

    Article  Google Scholar 

  9. Gao G, Gao M, Yang C, Zhisong Y (2010) Influence of deep excavation on deformation of operating metro tunnels and countermeasures. Chin J Geotech Eng 32(3):453–459

    Google Scholar 

  10. Han X, Jin JS, Wang M, Jiang W, Gao L, Xiao L (2017) A review of algorithms for filtering the 3d point cloud. Signal Process Image Commun 57:103–112

    Article  Google Scholar 

  11. Hayman WK (1970) Power series expansions for harmonic functions. Bull Lond Math Soc 2(2):152–158

    Article  MathSciNet  Google Scholar 

  12. Lovas T, Barsi A, Detrekoi A, Dunai L, Csak Z, Polgar A, Berenyi A, Kibedy Z, Szocs K (2008) Terrestrial lasers canning in deformation measurements of structures. Int Arch Photogramm Remote Sens 37(B5):527–531

    Google Scholar 

  13. Nuttens T, Stal C, De Backer H, Schotte K, Van Bogaert P, De Wulf A (2014) Methodology for the ovalization monitoring of newly built circular train tunnels based on laser scanning: Liefkenshoek rail link (belgium). Autom Constr 43:1–9

    Article  Google Scholar 

  14. Pourpanah F, Lim CP, Saleh JM (2016) A hybrid model of fuzzy artmap and genetic algorithm for data classification and rule extraction. Expert Syst Appl 49:74–85

    Article  Google Scholar 

  15. Pourpanah F, Lim CP, Wang X, Tan CJ, Seera M, Shi Y (2019) A hybrid model of fuzzy min-max and brain storm optimization for feature selection and data classification. Neurocomputing 333:440–451

    Article  Google Scholar 

  16. Pourpanah F, Zhang B, Ma R, Hao Q (2018) Non-intrusive human motion recognition using distributed sparse sensors and the genetic algorithm based neural network. In 2018 IEEE sensors, pp 1–4

  17. Pourpanah F, Shi Y, Lim CP, Hao Q, Tan CJ (2019) Feature selection based on brain storm optimization for data classification. Appl Soft Comput 80:761–775

    Article  Google Scholar 

  18. Pourpanah F, Tan CJ, Lim CP, Mohamad-Saleh J (2017) A q-learning-based multi-agent system for data classification. Appl Soft Comput 52:519–531

    Article  Google Scholar 

  19. Pourpanah F, Wang R, Lim CP, Wang X, Seera M, Tan CJ (2019) An improved fuzzy ARTMAP and Q-learning agent model for pattern classification. Neurocomputing 359:139–152

    Article  Google Scholar 

  20. Randall T (2011) Construction engineering requirements for integrating laser scanning technology and building information modeling. J Constr Eng Manag 137(10):797–805

    Article  Google Scholar 

  21. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  22. Sithole G, Vosselman G (2003) Comparison of filtering algorithms. In: Proceedings of the ISPRS working group III/3 workshop, pp 71–78

  23. Su YY, Hashash YMA, Liu LY (2006) Integration of construction as-built data via laser scanning with geotechnical monitoring of urban excavation. J Constr Eng Manag 132(12):1234–1241

    Article  Google Scholar 

  24. Wang R, Wang X, Kwong S, Chen X (2017) Incorporating diversity and informativeness in multiple-instance active learning. IEEE Trans Fuzzy Syst 25(6):1460–1475

    Article  Google Scholar 

  25. Wang X, Xing H-J, Li Y et al (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638–1654

    Article  Google Scholar 

  26. Wang X, Wang R, Chen X (2018) Discovering the relationship between generalization and uncertainty by incorporating complexity of classification. IEEE Trans Cybern 48(2):703–715

    Article  Google Scholar 

  27. Wang X, He Q, Chen D-G, Yeung D (2005) A genetic algorithm for solving the inverse problem of support vector machines. Neurocomputing 68:225–238

    Article  Google Scholar 

  28. Wu SL, Deng HL, Chen KJ, Zhu MY, Huang DH, Fu SY (2013) Visual monitoring technology of the tunnel 3D laser scanning and engineering applications. In: Advanced materials research, vol 779. Trans Tech Publications, pp 463–468

  29. Yu N, Zhu H (2004) The simulation of shield tunnel construction and analysis of its influence upon surrounding environments. ROCK SOIL MECH WUHAN 25(2; ISSU 91):292–296

    Google Scholar 

  30. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Article  Google Scholar 

  31. Zhang D, Huang J (2005) Analysis and prediction of vault crown settlement in metro tunneling at shallow depth. Yanshilixue Yu Gongcheng Xuebao/Chin J Rock Mech Eng 24(10):1703–1707

    Google Scholar 

  32. Zhu N, Jia Y, Luo L (2016) Tunnel point cloud filtering method based on elliptic cylindrical model. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B1, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic

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Acknowledgements

The first author (Shuyue Chen, a Mphil student) and the third author (Qin Wang, a yeas-3 undergraduste) would like to thank Prof. Xizhao Wang for his supervision in completing this work. We also thank Wentao Chen (a design engineer) for many insightful discussions on this project. This work is supported by NSFC (Grants Nos. 61976141 and 61732011) and in part by Basic Research Project of Knowledge Innovation Program in ShenZhen (Grant No. JCYJ20180305125850156).

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Authors and Affiliations

  1. Big Data Institute, College of Computer Science and Software Engineering, Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, Guangdong, China

    Shuyue Chen & Qin Wang

  2. China Construction Rail Electrification Engineering Co., Ltd., Beijing, China

    Hongjie Wang & Guohua Zhang

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  1. Shuyue Chen
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  2. Hongjie Wang
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  3. Qin Wang
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  4. Guohua Zhang
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Correspondence to Shuyue Chen.

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Chen, S., Wang, H., Wang, Q. et al. Fine-tuning of line and slope based on evolutionary mechanism. Int. J. Mach. Learn. & Cyber. 11, 1631–1641 (2020). https://doi.org/10.1007/s13042-020-01071-0

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  • DOI: https://doi.org/10.1007/s13042-020-01071-0

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