1887
Volume 52, Issue 6
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

The selection of the most appropriate method(s) for a specific engineering geophysical site characterisation can be challenging due to the complexity and potential ambiguity of the factors influencing the utility and cost-effectiveness of the methods. This paper, using matter-element analysis techniques with analytic hierarchy process (AHP), proposes a demonstration matter-element model for selecting the most appropriate geophysical methods, and establishes the general flow and calculation process for geophysical method selection. On the basis of the analysis of the factors influencing the utility and cost-effectiveness of geophysical methods, an evaluation criteria system is constructed. To demonstrate the application of this process, the proposed matter-element geophysical methods selection mode is applied to the selection of geophysical methods for an engineering geophysical site characterisation project that ultimately involved the acquisition of electrical resistivity tomography (ERT), multichannel analysis of surface wave data (MASW) and ground penetrating radar (GPR). The optimal results, based on the matter-element model, indicate the recommended geophysical methods are consistent with the field methods ultimately employed. The research proposes a new quantitative and objective computing approach to the selection of geophysical methods. The matter-element model can minimise subjective influences to a certain extent, and is conducive to enhancing the utility and cost-effectiveness of a geophysical survey.

Loading

Article metrics loading...

/content/journals/10.1080/08123985.2021.1881401
2021-11-02
2026-01-14
Loading full text...

Full text loading...

References

  1. Anderson, N.L., N. Croxton, R. Hoover, and P. Sirles. 2008. Geophysical methods commonly employed for geotechnical site characterization. Transportation Research Circular, E–C130.
  2. Anderson, N.L., A.Ismail, and C.Davis. 2006. Selection of appropriate geophysical techniques: A generalized protocol based on engineering objectives and site characteristics. In Proceedings of 2006 Highway Geophysics-NDE Conference, 29–47.
  3. Breaz, R.E., O.Bologa, and S.G.Racz. 2017. Selecting industrial robots for milling applications using AHP. Procedia Computer Science122: 346–53.
    [Google Scholar]
  4. Cai, W.1983. The extension set and incompatibility problem. Journal of Scientific Exploration 1: 81–93.
  5. Chan, F.T.S., N.Kumar, M.K.Tiwari, H.C.W.Lau, and K.L.Choy. 2008. Global supplier selection: a fuzzy-AHP approach. International Journal of Production Research46: 3825–57.
    [Google Scholar]
  6. Cheng, F., J.Xia, Z.Xu, Y.Hu, and B.Mi. 2018. Frequency–wavenumber (FK)-based data selection in high-frequency passive surface wave survey. Surveys in Geophysics39: 661–82.
    [Google Scholar]
  7. Dai, T., Y.Hu, L.Ning, F.Cheng, and J.Pang. 2018. Effects due to aliasing on surface-wave extraction and suppression in frequency-velocity domain. Journal of Applied Geophysics158: 71–81.
    [Google Scholar]
  8. Deng, X., Y.Xu, L.Han, Z.Yu, M.Yang, and G.Pan. 2015. Assessment of river health based on an improved entropy-based fuzzy matter-element model in the Taihu Plain, China. Ecological Indicators57: 85–95.
    [Google Scholar]
  9. Dweiri, F., S.Kumar, S.A.Khan, and V.Jain. 2016. Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Systems with Applications62: 273–83.
    [Google Scholar]
  10. Fortunet, C., S.Durieux, H.Chanal, and E.Duc. 2018. DFM method for aircraft structural parts using the AHP method. The International Journal of Advanced Manufacturing Technology95: 397–408.
    [Google Scholar]
  11. Hachoł, J., M.Hämmerling, and E.Bondar-Nowakowska. 2017. Applying the analytical hierarchy process (AHP) into the effects assessment of river training works. Journal of Water and Land Development35: 63–72.
    [Google Scholar]
  12. Hu, Y., J.Xia, B.Mi, F.Cheng, and C.Shen. 2018. A pitfall of muting and removing bad traces in surface-wave analysis. Journal of Applied Geophysics153: 136–42.
    [Google Scholar]
  13. Li, H.2012. Project evaluation method based on matter-element and hierarchy model. Telkomnika10: 586–91.
    [Google Scholar]
  14. Li, S., and R.Li. 2017. Energy sustainability evaluation model based on the matter-element extension method: A case study of Shandong Province, China. Sustainability9: 2128.
    [Google Scholar]
  15. Ouyang, X., F.Guo, D.Shan, H.Yu, and J.Wang. 2015. Development of the integrated fuzzy analytical hierarchy process with multidimensional scaling in selection of natural wastewater treatment alternatives. Ecological Engineering74: 438–47.
    [Google Scholar]
  16. Radionovs, A., and O.Užga-Rebrovs. 2017. Software tool implementing the fuzzy AHP method in ecological risk assessment. Information Technology and Management Science20: 34–39.
    [Google Scholar]
  17. Saatsaz, M., I.Monsef, M.Rahmani, and A.Ghods. 2018. Site suitability evaluation of an old operating landfill using AHP and GIS techniques and integrated hydrogeological and geophysical surveys. Environmental Monitoring and Assessment190: 1–31.
    [Google Scholar]
  18. Saaty, T.L.1978. Exploring the interface between hierarchies, multiple objectives and fuzzy sets. Fuzzy Sets and Systems1: 57–68.
    [Google Scholar]
  19. Saaty, T.L.1990. How to make a decision: The analytic hierarchy process. European Journal of Operational Research48: 9–26.
    [Google Scholar]
  20. Salamat, V.R., A.Aliahmadi, M.S.Pishvaee, and K.Hafeez. 2018. A robust fuzzy possibilistic AHP approach for partner selection in international strategic alliance. Decision Science Letters7: 481–502.
    [Google Scholar]
  21. Wang, C., A.Wu, H.Lu, T.Bao, and X.Liu. 2015. Predicting rockburst tendency based on fuzzy matter–element model. International Journal of Rock Mechanics and Mining Sciences75: 224–32.
    [Google Scholar]
  22. Wightman, M.J., and E.D.Zisman. 2008. The selection and application of geophysical test methods in West Central Florida Karst Regions. In Sinkholesand the Engineering and Environmental Impacts of Karst, 81–90.
  23. Wu, J., D.Peng, J.Ma, L.Zhao, C.Sun, and H.Ling. 2015. Selection of atmospheric environmental monitoring sites based on geographic parameters extraction of GIS and fuzzy matter-element analysis. PLoS ONE10: e0123766.
    [Google Scholar]
  24. Wu, Y., L.Chen, C.Cheng, K.Yin, and ÁTörök. 2014. GIS-based landslide hazard predicting system and its real-time test during a typhoon, Zhejiang Province, Southeast China. Engineering Geology175: 9–21.
    [Google Scholar]
/content/journals/10.1080/08123985.2021.1881401
Loading
/content/journals/10.1080/08123985.2021.1881401
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): electrical resistivity; GPR; optimisation; surface wave

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error