Skip to main content

Advertisement

SpringerLink
Log in

Machine learning based fast multi-layer liquefaction disaster assessment

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Liquefaction is one kind of earthquake-induced disasters which may cause severe damages to roads, highways and buildings and consequently delay the disaster rescue and relief actions. A fast and reliable assessment of liquefaction disaster is thus of great importance for making disaster prevention plans beforehand and for planing rescue and relief activities right after earthquakes. However, this is still a great challenge task, because the computational cost of current existing liquefaction assessment methods is very high. For example, a 50 seconds simulation (5000 time steps) needs one hour with 1000 nodes in the Supercomputer K. In this paper, we proposed a machine learning based liquefaction disaster assessment method. Here, the assessment result can be given with high efficiency (few seconds or less) for emergency evacuation in an earthquake. Meanwhile, a multi-layer approach was also proposed. Firstly, the most dangerous area will be shown immediately by using convolutional neural network (CNN) model; followed by a high precision result, which is obtained by using fast Fourier transform and a special of soil (N values) coupled with a Light Gradient Boosting Machine (Light GBM) model. One more contribution is our visualization design, which can be used to let users know the dangerous area more intuitively. Finally, the effectiveness of our proposed method was demonstrated by assessing liquefaction from a large-scale earthquake simulation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

Notes

  1. http://www.strata.jp/KobeJibankun/

References

  1. Bi, C., Yuan, Y., Zhang, J., Shi, Y., Xiang, Y., Wang, Y., Zhang, R.: Dynamic mode decomposition based video shot detection. IEEE Access 6, 21397 (2018)

    Article  Google Scholar 

  2. Bi, C., Yuan, Y., Zhang, R., Xiang, Y., Wang, Y., Zhang, J.: A dynamic mode decomposition based edge detection method for art images. IEEE Photon. J. 9 (6), 1 (2017)

    Article  Google Scholar 

  3. Biot, M.: Theory of propagation of elastic wave in a fluid-saturated porous solid. J Acoust Soc Am 28(2), 168 (1956)

    Article  Google Scholar 

  4. Chen, J., Takeyama, T., O-Tani, H., Fujita, K., Hori, M.: A framework for assessing liquefaction hazard for urban areas based on soil dynamics. Int. J. Comput. Method. 13(04), 1641011 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen, J., Takeyama, T., O-Tani, H., Fujita, K., Motoyama, H., Hori, M.: Using high performance computing for liquefaction hazard assessment with statistical soil models. Int. J. Comput. Method 15(2), 1840005 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  6. Chen, Q., Song, X., Yamada, H., Shibasaki, R.: Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference. In: AAAI Conference on Artificial Intelligence, pp. 338–344 (2016)

  7. Chen, T., Guestrin, C.: Liquefaction investigation of Wenchuan earthquake. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)

  8. Cubrinovski, M., Henderson, D., Bradley, B.: Liquefaction impacts in residential areas in the 2010-2011 Christchurch earthquakes. In: Proceedings of the International Symposium on Engineering Lessons Learned from the 2011 Great East Japan Earthquake, pp. 811–824 (2012)

  9. Cubrinovski, M., Ishihara, K., Pipatpongsa, T., Tanizawa, F.: Numerical simulation of the Kobe Port-Island liquefaction. In: Proceedings of the 11th World Conference on Earthquake Engineering, p 330 (1996)

  10. Dhanya, J., Raghukanth, S.T.G.: Ground motion prediction model using artificial neural network. Pure Appl. Geophysics 175(3), 1035 (2018)

    Article  Google Scholar 

  11. Erzin, Y., Ecemis, N.: The use of neural networks for cpt-based liquefaction screening. Bull. Eng. Geol. Environ. 74(1), 103 (2015)

    Article  Google Scholar 

  12. Fang, J.T., Chang, Y.R., Chang, P.C.: Deep learning of chroma representation for cover song identification in compression domain. Multidim. Syst. Sign. Process. 29, 887 (2018)

    Article  MathSciNet  Google Scholar 

  13. Farid, D.M., Zhang, L., Rahman, C.M., Hossain, M.A., Strachan, R.: Hybrid decision tree and naïve bayes classifiers for multi-class classification tasks. Expert Syst. Appl. 41, 1937 (2014)

    Article  Google Scholar 

  14. Fujita, K., Ichimura, T., Hori, M., Wijerathne, M., Tanaka, S.: A quick earthquake disaster estimation system with fast urban earthquake simulation and interactive visualization. Procedia Comput. Sci. 29, 866 (2014)

    Article  Google Scholar 

  15. Gordan, B., Armaghani, D.J., Hajihassani, M., Monjezi, M.: Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng. Comput. 32(1), 85 (2016)

    Article  Google Scholar 

  16. Gu, Y., Gu, M., Long, Y., Xu, G., Yang, Z., Zhou, J., Qu, W.: An enhanced short text categorization model with deep abundant representation. World Wide Web, 1–15 (2018)

  17. Güllü, H., Ercelebi, E.: A neural network approach for attenuation relationships: an application using strong ground motion data from turkey. Eng. Geol. 93(3-4), 65 (2007)

    Article  Google Scholar 

  18. Hasni, H., Alavi, A.H., Jiao, P., Lajnef, N.: Detection of fatigue cracking in steel bridge girders: a support vector machine approach. Archives Civil Mech. Eng. 17(3), 609 (2017)

    Article  Google Scholar 

  19. Huang, Y., Zhang, F., Yashima, A., Ye, W.: Numerical simulation of mitigation for liquefaction-induced soil deformations in a sandy ground improved by cement grouting. Environ. Geol. 55, 1247 (2008)

    Article  Google Scholar 

  20. Ishihara, K.: Soil Behaviors in Earthquake Geotechnics. Oxford Science Publication, Oxford (1996)

    Google Scholar 

  21. Jha, S., Suzuki, K.: Reliability analysis of soil liquefaction based on standard penetration test. Comput. Geotech. 36(4), 589 (2009)

    Article  Google Scholar 

  22. Kaya, Z.: Predicting liquefaction-induced lateral spreading by using neural network and neuro-fuzzy techniques. Int. J. Geomechanics 16(4), 04015095 (2016)

    Article  Google Scholar 

  23. Kazama, M., Noda, T.: Damage statistics (summary of the 2011 off the pacific coast of tohoku earthquake damage). Soils Found. 52(5), 780 (2012)

    Article  Google Scholar 

  24. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Liu, Q.Y.T.Y.: Lightgbm: a highly efficient gradient boosting decision tree. Advan. Neural Inform. Process. Syst., 1–9 (2017)

  25. Kohestani, V.R., Hassanlourad, M., Ardakani, A.: Evaluation of liquefaction potential based on cpt data using random forest. Nat. Hazards 79(2), 1079 (2015)

    Article  Google Scholar 

  26. Krawczyk, B., Woniak, M., Schaefer, G.: Cost-sensitive decision tree ensembles for effective imbalanced classification. Appl. Soft Comput. 14, 554 (2014)

    Article  Google Scholar 

  27. Li, H., Wang, Y., Wang, H., Zhou, B.: Multi-window based ensemble learning for classification of imbalanced streaming data. World Wide Web 20, 1507 (2017)

    Article  Google Scholar 

  28. Manek, A.S., Shenoy, P.D., Mohan, M.C., Venugopal, K.R.: Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and svm classifier. World Wide Web 20, 135 (2017)

    Article  Google Scholar 

  29. Muduli, P.K., Das, S.K.: Model uncertainty of spt-based method for evaluation of seismic soil liquefaction potential using multi-gene genetic programming. Soils Found. 55(2), 258 (2015)

    Article  Google Scholar 

  30. Oka, F., Kimoto, S.: Computational Modelling of Multiphase Geomaterials. CRC Press, Boca Raton (2012)

    Book  Google Scholar 

  31. Poulos, S., Castro, G., France, J.: Liquefaction evaluation procedure. J Geotech. Engrg. 111(6), 772 (1985)

    Article  Google Scholar 

  32. Samui, P., Kim, D., Hariharan, R.: Determination of seismic liquefaction potential of soil based on strain energy concept. Environ. Earth Sci. 74(7), 5581 (2015)

    Article  Google Scholar 

  33. Society, T.J.G.: Soil liquefaction survey in kanto district during the 2011 off the pacific coast of tohoku earthquake. Tech. rep., Ministry of Land, Infrastructure, Transport and Tourism, Kanto Regional Development Bureau. In Japanese (2011)

  34. Song, X., Shibasaki, R., Yuan, N.J., Xie, X., Li, T., Adachi, R.: Deepmob: learning deep knowledge of human emergency behavior and mobility from big and heterogeneous data. ACM Transactions on Information Systems (TOIS) 35(4), 41:1 (2017)

    Article  Google Scholar 

  35. Song, X., Zhang, Q., Sekimoto, Y., Horanont, T., Ueyama, S., Shibasaki, R.: Intelligent system for human behavior analysis and reasoning following large-scale disasters. IEEE Intell. Syst. 28(4), 35 (2013)

    Article  Google Scholar 

  36. Song, X., Zhang, Q., Sekimoto, Y., Shibasaki, R., Yuan, N.J., Xie, X.: A Simulator of Human Emergency Mobility following Disasters: Knowledge Transfer from Big Disaster Data. In: AAAI Conference on Artificial Intelligence, pp. 730–736 (2015)

  37. Song, X., Zhang, Q., Sekimoto, Y., Shibasaki, R., Yuan, N.J., Xie, X.: Prediction and simulation of human mobility following natural disasters. ACM Transactions on Intelligent Systems and Technology (TIST) 8(2), 29:1 (2017)

    Google Scholar 

  38. Sudo, A., Kashiyama, T., Yabe, T., Kanasugi, H., Song, X., Higuchi, T., Nakano, S., Saito, M., Sekimoto, Y.: Particle Filter for Real-time Human Mobility Prediction following Unprecedented Disaster. In: International Conference on Advances in Geographic Information Systems, pp. 5:1–10 (2016)

  39. Wang, X.W., Nie, D., Lu, B.L.: Emotional state classification from eeg data using machine learning approach. Neurocomputing 129, 94 (2014)

    Article  Google Scholar 

  40. Wen, Z., Zhang, R., Ramamohanarao, K., Yang, L.: Scalable and fast svm regression using modern hardware. World Wide Web 21, 261 (2018)

    Article  Google Scholar 

  41. Xue, X., Liu, E.: Seismic liquefaction potential assessed by neural networks. Environ. Earth Sci. 76, 1 (2017)

    Article  Google Scholar 

  42. Yang, L., Wang, B., Zhang, R., Zhou, H., Wang, R.: Analysis on location accuracy for the binocular stereo vision system. IEEE Photon. J. 10(1), 7800316:1 (2018)

    Google Scholar 

  43. Zhang, J., Lafta, R.L., Tao, X., Li, Y., Chen, F., Luo, Y., Zhu, X.: Coupling a fast fourier transformation with a machine learning ensemble model to support recommendations for heart disease patients in a telehealth environment. IEEE Access 5, 10674 (2017)

    Article  Google Scholar 

  44. Zhang, X., Hu, B., Chen, J., Moore, P.: Ontology-based context modeling for emotion recognition in an intelligent Web. World Wide Web 16, 497 (2013)

    Article  Google Scholar 

  45. Zhao, X., Bi, X., Qiao, B.: Probability based voting extreme learning machine for multiclass xml documents classification. World Wide Web 17, 1217 (2014)

    Article  Google Scholar 

  46. Zheng, W., Tang, H., Qian, Y.: Collaborative work with linear classifier and extreme learning machine for fast text categorization. World Wide Web 18, 235 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank Professor Takeyama Tomohide for his suggestion in liquefaction disaster assessment, and anonymous reviewers for their valuable comments.

Author information

Authors and Affiliations

  1. School of Computer Software, Tianjin University, Tianjin, 300350, China

    Chongke Bi, Bairan Fu & Yudong Zhao

  2. RIKEN Center for Computational Science, Kobe, Hyogo, 650-0047, Japan

    Jian Chen

  3. School of Mechanical Engineering, Tianjin University of Technology, Tianjin, 300384, China

    Lu Yang

  4. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Yulin Duan & Yun Shi

Authors
  1. Chongke Bi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bairan Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yudong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yulin Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yun Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jian Chen or Yulin Duan.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article belongs to the Topical Collection: Special Issue on Big Data for Effective Disaster Management

Guest Editors: Xuan Song, Song Guo, and Haizhong Wang

This work was partly supported by the National Natural Science Foundation of China under Grant No. 61702360, partly by the Tianjin Natural Science Foundation of China under Granted No. 16JCQNJC04100, partly supported by the FOCUS Establishing Supercomputing Center of Excellence and by the Council for Science, Technology and Innovation(CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), “Enhancement of societal resiliency against natural disasters” (Funding agency: JST). This research used the computational resources of the K computer provided by RIKEN Center for Computational Science.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bi, C., Fu, B., Chen, J. et al. Machine learning based fast multi-layer liquefaction disaster assessment. World Wide Web 22, 1935–1950 (2019). https://doi.org/10.1007/s11280-018-0632-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-018-0632-8

Keywords

Search

Navigation