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Proposing new relationships to estimate the pressuremeter modulus of cohesive and cohesionless media

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Abstract

Pressuremeter is one of the most reliable geotechnical in situ tests to be utilized in estimating different soil properties. The main parameters that can be obtained from this test are soil deformation modulus. In general, the pressuremeter test is time-consuming and costly that requires suitable equipment and experienced operators. With these limitations, it is necessary to introduce models for indirect determination of the pressuremeter modulus (EPM). Based on the literature, various models and equations have been proposed to predict the modulus of pressuremeter; however, most of them are for fine-grained soils. In this paper, multiple linear regression (MLR), artificial neural network (ANN), and gene expression programming (GEP) have been used to design new relationships for prediction of the pressuremeter modulus. The main difference between this research and other studies is the use of 62 pre-bored pressuremeter tests results in cohesive and granular soils for training and testing models. The study area is in Tehran where soils show a variation from low plasticity clay to clayey gravel. The moisture content, depth of test, and grain size distribution of soils are considered as independent variables. Comparison of the predicted EPM with the actual modulus obtained from the pressuremeter tests indicates that the proposed relationships are able to estimate the pressuremeter modulus well. The results showed that the relationships obtained from nonlinear analyses performed by smart methods are more accurate and have less error in comparison to MLR method. Comparison of the coefficient of determination (R2) values and errors related to them in the testing phase show that the obtained results from the GEP method are more reliable than the ANN method. Also, sensitivity analysis revealed that the soil moisture content is the most effective parameter among the variables on the pressuremeter modulus.

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References

  1. Mollahasani A, Alavi AH, Gandomi AH (2011) Empirical modeling of plate load test moduli via gene expression programming. Comput Geotech 38(2):281–286

    Article  Google Scholar 

  2. Shahnazari H, Dehnavi Y, Alavi AH (2010) Numerical modeling of stress–strain behavior of sand under cyclic loading. Eng Geol 116(1–2):53–72

    Article  Google Scholar 

  3. Murthy S (2008) Geotechnical engineering: principles and practice of soil mechanics, 2nd edn. CRC Press, Boca Raton

    Google Scholar 

  4. Menard L (1955) Pressiometre, Brevet Francais. de’invention. No 1.117.983

  5. Menard L (1957) Mesure In situ des Proprietes Physiques Desols. Ann Ponts Chaussees 14:357–377

    Google Scholar 

  6. ASTM D4719 (2000) Standard test method for pre-bored pressuremeter testing in soils. ASTM International, vol 04-08

  7. Keyabasi A (2012) Prediction of pressuremeter modulus and limit pressure of clayey soils by simple and non-linear multiple regression techniques: a case study from Mersin, Turkey. Environ Earth Sci 66:2171–2183

    Article  Google Scholar 

  8. Adalag A, Kayabasi A, Gokceoglu C (2013) Estimation of pressuremeter modulus and limit pressure of clayey soils by various artificial neural network models. Neural Comput Appl 23:333–3339

    Article  Google Scholar 

  9. Alavi AH, Gandomi AH, Chahkandi Nejad H (2013) Design equations for prediction of pressuremeter soil deformation moduli utilizing expression programming systems. Neural Comput Appl 23(6):1771–1776

    Article  Google Scholar 

  10. Ziaie Moayed R, Kordnejad A, Mola Abasi H (2018) Pressuremeter modulus and limit pressure of clayey soils using GMDH type neural network and genetic algorithm. Geotech Geol Eng 36(1):165–178

    Article  Google Scholar 

  11. Giakomino A, Abollino O, Malan Drino M, Mentasti E (2011) The role of chemometric in single and sequential extraction assays: a review part II, cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques. Anal Chim Acta 688:12–139

    Google Scholar 

  12. Akan R, Keskin SN, Unzundur Ukan S (2015) Multiple regression model for the prediction of unconfined compressive strength of jet grout columns. Procedia Earth Planet Sci 15:299–303

    Article  Google Scholar 

  13. Yieldirim I, Ozashin S, Akyuz KC (2011) Prediction of the financial return of the paper sector with artificial neural networks. BioResources 6(4):4076–4091

    Google Scholar 

  14. Hannan SHA, Manza RR, Ramteke RJ (2010) Generalized regression neural network and radial basis function for heart disease diagnosis. Int J Comput Appl 7(13):7–13

    Google Scholar 

  15. Specht DF (1991) A general regression neural network. IEEE Trans Neural Networks 2(6):568–576

    Article  Google Scholar 

  16. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129

    Google Scholar 

  17. Cevik A (2007) A new formulation for longitudinally stiffened webs subjected to patch loading. J Constr Steel Res 63(10):1328–1340

    Article  Google Scholar 

  18. Kayadelen C (2011) Soil liquefaction modeling by genetic expression programming and neuro-fuzzy. Expert Syst Appl 38(4):4080–4087

    Article  Google Scholar 

  19. Atici U (2016) Modeling of the elasticity modulus using gene expression programming. Adv Mater Sci Eng. https://doi.org/10.1155/2016/2063987

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Shahin Razavi & Kaveh Ahangari

  2. Department of Mining Engineering, Tarbiat Modares University, Jalal AleAhmad Street, P.O. Box: 14115-111, Tehran, Iran

    Kamran Goshtasbi

  3. Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran

    Ali Noorzad

Authors
  1. Shahin Razavi
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  2. Kamran Goshtasbi
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  3. Ali Noorzad
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  4. Kaveh Ahangari
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Correspondence to Kamran Goshtasbi.

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Razavi, S., Goshtasbi, K., Noorzad, A. et al. Proposing new relationships to estimate the pressuremeter modulus of cohesive and cohesionless media. Innov. Infrastruct. Solut. 3, 67 (2018). https://doi.org/10.1007/s41062-018-0172-1

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  • DOI: https://doi.org/10.1007/s41062-018-0172-1

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