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Prediction of in situ state parameter of sandy deposits from CPT measurements using optimized GMDH-type neural networks

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Abstract

Increase in seismic events around the world has necessitated the evaluation of soil liquefaction potential since it adversely influences the stability of adjacent structures and poses a threat to lives and property. The first step in evaluating the liquefaction potential involves the estimation of the in situ state of sand and the state parameter as an index to characterize the behavior of sand. Due to the difficulty in obtaining high-quality undisturbed sandy samples for laboratory testing and evaluation, simplified equations based on the cone penetration test (CPT) are used for reasonable estimation of field behavior. In the present study, an optimized group method of data handling (GMDH)-type neural network was proposed to estimate the state parameter from CPT data obtained from the historical liquefaction database. A comparison was made between the measured and the predicted values of the state parameter to evaluate the performance of the proposed GMDH neural network method. A sensitivity analysis of the proposed model was also carried out to study the effect of input variables on the output variable of the proposed model. Additionally, the evaluation of in situ state and liquefaction potential of sand based on the state parameter was also presented and compared with the existing methods. Overall, the use of the GMDH model in evaluating the in situ state of sand and subsequent liquefaction potential assessment has shown promising results.

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Acknowledgements

The majority of the work presented in this paper was funded by the National Key R&D Program of China (2020YFC1807200), the National Natural Science Foundation of China (Grants Nos. 52108332, 41877231 and 42072299), the Project funded by China Postdoctoral Science Foundation (No. 2021M702421). These financial supports are gratefully acknowledged.

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Author notes

  1. Wei Duan and Zening Zhao contributed equally to this work.

Authors and Affiliations

  1. College of Civil Engineering, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China

    Wei Duan & Xiaoqiang Dong

  2. Institute of Geotechnical Engineering, School of Transportation, Southeast University, Nanjing, 211189, Jiangsu, China

    Wei Duan, Guojun Cai, Zening Zhao, Songyu Liu, Ruifeng Chen & Huanhuan Qiao

  3. Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, 77843-3136, USA

    Surya Sarat Chandra Congress

  4. School of Civil Engineering, Anhui Jianzhu University, Hefei, 230601, Anhui, China

    Guojun Cai

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  1. Wei Duan
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Appendix: CPTU-related parameters

Appendix: CPTU-related parameters

  • qc raw CPT cone resistance (generally in MPa, but may need to convert into kPa when used with other parameters to derive dimensionless parameters such as Q and qt1N)

  • qt corrected cone resistance (=qc+(1-a)u2)

  • a area ratio of cone used (in this paper, a = 0.85)

  • u2 penetration in situ pore pressure (kPa)

  • fs sleeve friction (kPa)

  • σ′vo vertical effective overburden stress (kPa)

  • σvo vertical total overburden stress (kPa)

  • Bq pore pressure parameter (= excess pore pressure ratio = (u2-u0)/(qt-σvo).

  • u0 hydrostatic water pressure (kPa).

  • Q normalized cone resistance (= (qc- σvo)/ σ′vo) for CPT or (= (qt- σvo)/ σ′vo) for CPTU.

  • Rf friction ratio = ((fs/qc) × 100%).

  • Fr normalized friction ratio = [fs = (qt-σvo)] × 100%

  • Ic,BJ soil behavior-type index defined by Been & Jefferies [5]

$$I_{{c,{\text{BJ}}}} = \sqrt {\left\{ {3 - \lg \left[ {Q(1 - B_{q} ){ + }1} \right]} \right\}^{2} + \left[ {1.5 + 1.3(\lg F_{r} )} \right]^{2} }$$

  • Ic,RW soil behavior-type index defined by Robertson &Wride (1998)

    $$I_{{\text{c, RW}}} = \sqrt {\left( {3.47 - \lg Q} \right)^{2} + \left( {1.22 + \lg F_{{\text{r}}} } \right)^{2} }$$

  • qc1N adjusted cone tip resistance for CPT = [(qc -σvo)/ p0](p0/σ′vo)n.

  • qt1N adjusted cone tip resistance for CPTU = [(qt -σvo)/ p0](p0/σ′vo)n.

  • p0 reference pressure (= 100 kPa).

  • n stress exponent = 0.381 Ic,RW + 0.05(σ′vo/p0)-0.15 (n ≤ 1).

  • qc1N,cs is equivalent clean sand cone tip resistance for CPT = Kc × qc1N.

  • qt1N,cs is equivalent clean sand cone tip resistance for CPTU = Kc × qt1N.

  • Kc is a correction factor that is a function of soil behavior-type index Ic, RW

  • $$K_{{\text{c}}} {\text{ = }}\left\{ {\begin{array}{*{20}l} {1,} \hfill & {{\text{if }}\, I_{{c,\, {\text{RW}}}} \le 1.64} \hfill \\ { - 0.403I_{{{\text{c,}}\, {\text{RW}}}}^{{\text{4}}} {\text{ + }}5.58I^{3} _{{{\text{c,}}\, {\text{RW}}}} - 21.63I_{{{\text{c,}}\, {\text{RW}}}}^{{\text{2}}} + 33.75I_{{{\text{c,}}\, {\text{RW}}}} - 17.88,} \hfill & {{\text{if }}\, I_{{{\text{c,}}\, {\text{RW}}}} > 1.64} \hfill \\ \end{array} } \right.$$

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Duan, W., Congress, S.S.C., Cai, G. et al. Prediction of in situ state parameter of sandy deposits from CPT measurements using optimized GMDH-type neural networks. Acta Geotech. 17, 4515–4535 (2022). https://doi.org/10.1007/s11440-022-01540-6

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