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Geotechnical and Geological Engineering

, Volume 36, Issue 4, pp 2481–2494 | Cite as

Utilisation of GIS Concepts for Foundation Design on Problematic Eastern Saudi Arabian Sabkha Soil

  • Hamzah M. Beakawi Al-Hashemi
  • Baqer M. Al-Ramadan
  • Naser Al-Shayea
Original paper
  • 136 Downloads

Abstract

The design of foundations on a problematic soil is considered one of the most challenging issues for the geotechnical engineers. Sabkha, or salt flat, is considered as a problematic soil that exists in coastal areas. Ar-Rayyas sabkha is covering a wide area of the Eastern Province of the Kingdom of Saudi Arabia. King Fahd Suburb in the city of Dammam, Saudi Arabia, having been identified with an extensive presence of Ar-Rayyas sabkha, is considered for this study. Many structural failures of the residential buildings in the suburb were reported. The presence of sabkha is predominating the reasons of these failures. 59 investigatory boreholes were performed at the suburb and standard penetration N-values, have been obtained for the boreholes. A regression analysis was carried out, and a prediction model of the N-values using the coordinates and the depth intervals of the boreholes as regressors was provided. Due to the low accuracy of the model, an artificial neural network model was proposed to enhance the accuracy of the prediction; however, a further enhancement was required. Using GIS concepts, the suburb was mapped, and the borehole locations were projected with their attributes on the map. Different interpolation methods were utilised to predict and interpolate the N-values between the boreholes. The inverse distance weighting method yielded the most accurate model and therefore was selected to produce the final layouts for each depth interval. Finally, a framework for the design of foundations with the aid of GIS techniques was developed.

Keywords

Regression AI ANN GIS Interpolation Foundations SPT Sabkha Ar-Rayyas KF Suburb 

Abbreviation

Adj.

Adjusted

AI

Artificial intelligence

ANN

Artificial neural network

ANOVA

Analysis of variances

BH

Borehole

CRD

Completely randomised design

D

Depth intervals (m)

DF

Degree of freedom

DK

Diffusion kernel

EBK

Empirical Bayesian kriging

Eq

Equation

Fig

Figure

GDB

Geotechnical database

G.I

Geotechnical investigation

GIS

Geographical information systems

GPI

Global polynomial interpolation

GPS

Global positioning system

GRNN

Generalised regression neural network

GWT

Groundwater table

IDW

Inverse distance weighting

KF Suburb

King Fahd Suburb

KIB

Kernel interpolation with barriers

KML

Keyhole markup language

LPI

Local polynomial interpolation

MLFN

Multi-layer feed-forward neural network

MS

Mean of squares

N/N-values

Number of SPT blows

P value

Probability value

RBF

Radial basis function

R-Square/R2

Correlation coefficient

SE

Standard error of estimate

Sig.

Significance

SPT

Standard penetration test

SS

Sum of squares

Std.

Standard

UTM

Universal Transverse Mercator

WGS

World geodetic system

X

Eastern coordinates (UTM)

Y

Northern coordinates (UTM)

Notes

Acknowledgements

The authors gratefully acknowledge King Fahd University of Petroleum & Minerals (KFUPM) for supporting this study. Thanks are also extended to Dammam Municipality for providing the raw data.

Supplementary material

10706_2018_477_MOESM1_ESM.pdf (19.4 mb)
Supplementary material 1 (PDF 19854 kb)
10706_2018_477_MOESM2_ESM.pdf (1.4 mb)
Supplementary material 2 (PDF 1480 kb)
10706_2018_477_MOESM3_ESM.docx (15 kb)
Supplementary material 3 (DOCX 14 kb)

References

  1. AbdulRazzaq KS, Hussein WA, Hameed AH (2011) Bearing capacity based on SPT-computer interpolation. Diyala J Eng Sci 4(2):118–129Google Scholar
  2. Agunbiade D, Ogunyinka P (2013) Effect of correlation level on the use of auxiliary variable in double sampling for regression estimation. Open J Stat 3(5):312–318.  https://doi.org/10.4236/ojs.2013.35037 CrossRefGoogle Scholar
  3. Al-Farraj A (2005) An evolutionary model for sabkha development on the north coast of the UAE. J Arid Environ 63(4):740–755.  https://doi.org/10.1016/j.jaridenv.2005.01.025 CrossRefGoogle Scholar
  4. Al-Hashemi HM, Bukhary A (2016). Correlation between California bearing ratio (CBR) and angle of repose of granular soil. Electron J Geotech Eng 21(17):5655–5660. http://www.ejge.com/2016/Ppr2016.0530ma.pdf
  5. Al-Jabban MJW (2013) Estimation of standard penetration test (SPT) of Hilla City-Iraq by using GPS coordination. Jordan J Civ Eng 7(1):133–145Google Scholar
  6. Alsharhan AS, Kendall CGSC (2003) Holocene coastal carbonates and evaporites of the southern Arabian Gulf and their ancient analogues. Earth Sci Rev 61(3–4):191–243.  https://doi.org/10.1016/S0012-8252(02)00110-1 CrossRefGoogle Scholar
  7. Al-Shayea N (2000) Inherent heterogeneity of sediments in Dhahran, Saudi Arabia—a case study. Eng Geol 56(3–4):305–323.  https://doi.org/10.1016/S0013-7952(99)00098-8 CrossRefGoogle Scholar
  8. Ameh P, Igwe O, Ukah B (2017) Evaluation of the bearing capacity of near-surface soils using integrated methods: a case study of Otukpa, Ogbadibo Lga, Benue State, North-Central Nigeria. J Geol Soc India 90(1):93–101.  https://doi.org/10.1007/s12594-017-0668-x CrossRefGoogle Scholar
  9. ASTM-D1586 (2011) Standard test method for standard penetration test (SPT) and split-barrel sampling of soils. ASTM Int.  https://doi.org/10.1520/D1586-11 Google Scholar
  10. Ati M, Alzo’ubi AK, Ibrahim F (2015) Smart framework for predicting drilled shaft capacity based on data mining techniques and GIS data. In: XV Pan-American conference on soil mechanics and geotechnical engineering. IOS Press, Buenos Aires, Argantina, pp 1909–1915Google Scholar
  11. Bourenane H, Bouhadad Y, Tas M (2017) Liquefaction hazard mapping in the city of Boumerdès, Northern Algeria. Bull Eng Geol Environ.  https://doi.org/10.1007/s10064-017-1137-x Google Scholar
  12. Bowles JE (1997) Foundation analysis and design. In: Clark BJ, Kimbell KV, Morriss JM (eds). 5th edn. McGraw-Hill, Inc., Singapore.  https://doi.org/10.1016/0013-7952(84)90010-3
  13. Burland JB, Burbidge MC (1986) Settlement of foundations on sand and gravel. ICE Proc 80(6):1625–1648.  https://doi.org/10.1680/iicep.1986.537 Google Scholar
  14. Chabrillat S, Goetz A, Krosley L, Olsen H (2002) Use of hyperspectral images in the identification and mapping of expansive clay soils and the role of spatial resolution. Remote Sens Environ 82(2–3):431–445.  https://doi.org/10.1016/S0034-4257(02)00060-3 CrossRefGoogle Scholar
  15. Das BM (2010) Principles of geotechnical engineering, 7th edn. Cengage Learning, Stanford. www.cengage.com/engineering
  16. Edgell HS (2006) Arabian deserts: nature, origin and evolution, 1st edn. Springer, Dordrecht.  https://doi.org/10.1007/1-4020-3970-0
  17. Goodchild M, Longley P, Maguire D, Rhind DW (2010) Geographic information systems and science, 3rd edn. Wiley, New YorkGoogle Scholar
  18. Hettiarachchi H, Brown T (2009) Use of SPT blow counts to estimate shear strength properties of soils: energy balance approach. J Geotech Geoenviron Eng 135(6):830–834.  https://doi.org/10.1061/(ASCE)GT.1943-5606.0000016 CrossRefGoogle Scholar
  19. Kim H-S, Sun C-G, Cho H-I (2017) Geospatial big data-based geostatistical zonation of seismic site effects in Seoul metropolitan area. ISPRS Int J Geo-Inf 6(6):174.  https://doi.org/10.3390/ijgi6060174 CrossRefGoogle Scholar
  20. Kimmance JP, Bradshaw MP, Seetoh HH (1999) Geographical information system (GIS) application to construction and geotechnical data management on MRT construction projects in Singapore. Tunn Undergr Space Technol 14(4):469–479.  https://doi.org/10.1016/S0886-7798(00)00009-2 CrossRefGoogle Scholar
  21. Maliene V, Grigonis V, Palevičius V, Griffiths S (2011) Geographic information system: old principles with new capabilities. Urban Des Int 16(1):1–6.  https://doi.org/10.1057/udi.2010.25 CrossRefGoogle Scholar
  22. Maliki OS, Agbo AO, Maliki AO, Agwu CO (2011). Comparison of regression model and artificial neural network model for the prediction of electrical power generated in Nigeria. Adv Appl Sci Res 2(5):329–339. www.pelagiaresearchlibrary.com
  23. McHarg IL (1995) Design with nature, 1st edn. Wiley, Philadelphia, p 1969Google Scholar
  24. Menggenang P, Samanta S (2017) Modelling and mapping of landslide hazard using remote sensing and GIS techniques. Model Earth Syst Environ 3(3):1113–1122.  https://doi.org/10.1007/s40808-017-0361-5 CrossRefGoogle Scholar
  25. Montgomery DC (2006) Design and analysis of experiments. Technometrics, 8th edn. vol 48. Wiley.  https://doi.org/10.1198/tech.2006.s372
  26. Pankratz H, Sultan M, Elkadiri R, Almorgan S, Gebremichael EG, Ahmed M, Emil MK, Othman A (2015) Assessment of land deformation in Jazan City, Saudi Arabia using radar interferometry. In: AGU, 1–2. Western Michigan University, San Francisco, CA, USAGoogle Scholar
  27. Pooya Nejad F, Jaksa MB, Kakhi M, McCabe BA (2009) Prediction of pile settlement using artificial neural networks based on standard penetration test data. Comput Geotech 36(7):1125–1133.  https://doi.org/10.1016/j.compgeo.2009.04.003 CrossRefGoogle Scholar
  28. Ramirez F, Vipulanandan C (2006) Interpolation functions and GIS application for Houston soils. In: CIGMAT-2006 conference and exhibition, 18–19. University of Houston, HoustonGoogle Scholar
  29. Rogers JD (2006) Subsurface exploration using the standard penetration test (SPT) and cone penetrometer test (CPT). Environ Eng Geosci XII 2:161–179.  https://doi.org/10.2113/12.2.161 CrossRefGoogle Scholar
  30. Rura MJ, Marble DF, Alvarez D (2014) In memoriam: Roger Tomlinson—‘The Father of GIS’ and the transition to computerized geographic information. Photogramm Eng Remote Sens 80(5):400–401Google Scholar
  31. Russell SJ, Norvig P (2002) Artificial intelligence: a modern approach, 2nd edn. Prentice Hall PTR, Upper Saddle RiverGoogle Scholar
  32. Sabtan AA (2005) Performance of a steel structure on Ar-Rayyas sabkha soils. Geotech Geol Eng 23(2):157–174.  https://doi.org/10.1007/s10706-003-5200-y CrossRefGoogle Scholar
  33. Samugavelu D, Abdul Ghani AN (2016) Civil infrastructure and building design: the development of GIS database and product as design aid. Jurnal Teknologi.  https://doi.org/10.11113/jt.v78.8347 Google Scholar
  34. Sarkar G, Siddiqua S, Banik R, Rokonuzzaman Md (2015) Prediction of soil type and standard penetration test (SPT) value in Khulna City, Bangladesh using general regression neural network. Q J Eng Geol Hydrogeol.  https://doi.org/10.1144/qjegh2014-108 Google Scholar
  35. Shioi Y, Fukui J (1982) Application of N-value to design of foundation in Japan. Proc Second Eur Symp Penetr Test 1:24–27Google Scholar
  36. Shooshpashaa I, Amirib I, MolaAbasia H (2015). An investigation of friction angle correlation with geotechnical properties for granular soils using GMDH type neural networks. Sci Iran Trans A Civil Eng 22(1):157–164. www.scientiairanica.com
  37. Sitányiová D, Vondráčková T, Stopka O, Myslivečková M, Muzik J (2015) GIS based methodology for the geotechnical evaluation of landslide areas. Proc Earth Planet Sci 15:389–394.  https://doi.org/10.1016/j.proeps.2015.08.011 CrossRefGoogle Scholar
  38. Sivrikaya O, Toğrol E (2002) Relations between SPT-N and Qu. In: 5th international congress on advances in civil engineering, pp 943–952Google Scholar
  39. Skempton AW (1986) Standard penetration test procedure and the effects in sands of overburden pressure, relative density, particle size, aging, and overconsolidation. Geotechnique 36(3):425–447CrossRefGoogle Scholar
  40. Sun C-G, Kim H-S (2017) GIS-based regional assessment of seismic site effects considering the spatial uncertainty of site-specific geotechnical characteristics in coastal and inland urban areas. Geomat Nat Hazards Risk.  https://doi.org/10.1080/19475705.2017.1364305 Google Scholar
  41. Tomlinson RF (1969) A geographic information system for regional planning. J Geogr (Chigaku Zasshi) 78(1):45–48.  https://doi.org/10.5026/jgeography.78.45 CrossRefGoogle Scholar
  42. Tomlinson MJ, Boorman R (2001) Foundation design and construction, 7th edn. Longman Group, HarlowGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringKing Fahd University of Petroleum and MineralsDhahranSaudi Arabia
  2. 2.Department of City and Regional PlanningKing Fahd University of Petroleum and MineralsDhahranSaudi Arabia

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