Background & Purpose Artificial Neural Network Spatial Interpolation of soil properties Numerical analysis of Kobe Airport Conclusions.

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Cory Small
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1 IACMAG 214 September 22, Applicability of artificial neural network to estimating soil of Holocene clays in Osaka Bay Kazuhiro ODA Graduate school of Engineering, Osaka University 3 BACKGROUND 4 Recently, the numerical analyses became very powerful tools to reproduce the deformation of ground due to construction works. Constitutive equations (Elasto plastic models) Development of analytical techniques Development of computers Etc. The packaged software tools have been widely used in the various situations in the geotechnical fields.

Sand mat Revetmen Rubble (1) Sand drain Rubble BACKGROUND 5 PURPOSE 6 The some difficulties have still remained. One of the most important difficulties is a method of modeling of the ground.

2 Sand mat Revetmen Rubble (1) Sand drain Rubble BACKGROUND 5 PURPOSE 6 The some difficulties have still remained. One of the most important difficulties is a method of modeling of the ground. Constitutive models Selection of constitutive models Mechanical parameters of constitutive models Modeling of ground The ground is not uniform. The information of ground given through boring investigations is limited. The analytical models depend on the engineers. Not easy way in numerical modeling of the ground The object of this study is to propose the method of modeling the ground for the numerical analyses. independent of judgment of engineers estimate the spatial distribution of soil. Boring investigations Artificial Neural Network Analytical model 7 ARTIFICIAL NEURAL NETWORK The artificial neural network is the information processing systems, in which the nerve cells (neurons) in human brain are reproduced mathematically. Modeling a nerve cell (neuron) Mathematical model

CONSTRUCTION OF ANN MODEL 1 The back propagation neural network is consists of the input layer, one or more hidden layer, and the outputlayer. Each layer is connected by neurons.

On the other hand, estimated soil were corresponded to items of the output layer.

3 CONSTRUCTION OF ANN MODEL 1 The back propagation neural network is consists of the input layer, one or more hidden layer, and the outputlayer. Each layer is connected by neurons. In this study, the latitude, longitude, and altitude at target position at which the soil should be estimated were corresponded to items of the input layer. On the other hand, estimated soil were corresponded to items of the output layer.. North latitude ( ) East longitude ( ) Altitude (GL-m) Weights Soil property Input-layer Hidden-layer Output-)layer Subject area 11 INDEXES FOR JUDGING OPTIMUM 12 Select the subject area and the parameters for the prediction. 99 boring investigations R 2 Coefficient of Correlation G (%) Prediction Accuracy % relativity between measured values and estimated values obtained from root-meansquare of measured values and that of errors Kobe MARE (%) Mean Absolute Relative Error Success Rates Clay natural water content void ratio compression index pre consolidation pressure Permeability Etc. % average of absolute values of each individual estimated error rates (r) percentage of r in each 3 ranges r<1 1<r<15 r>15 These indexes are used to judge the optimum ANN model

Estimation of soil 13 Compression Index (CC) 14 Cc 5.3 1.5 7.1 6.3 2.6 19.1 1.5 1.2 7. 67.. 17. 15.5 1.4 7.6 62.9 17.5 19.6 2.1 11.1 6.5 63.9 19.1 17. e 5.91 5.3 93.5 9.2 6.7 4.1 1.91 5.2 93.4 9.2 5.

7 99. 1... 2.3.7 99. 1... e logp 5.96 4. 93..5 7.4 4. 1.97 4.6 94.2 91.7 5.4 2.9 15.97 4.7 94.1 9.4 7. 3.6 2.97 4.6 94.2 9. 6.2 3. mv 5.92 25.9 7. 35.2 13. 51. 1.93 2.5 71.6 26.9 13. 6.1 15.91 29. 6. 2.5 12.

4 56.6. 27.4 2.94 12.7 6.5 55.1 15. 29.1 Optimal Neural Network & Accuracy Case Training G MARE R 2 Repetition (%) (%) r < 1 1<r<15 r > 15 CI-1 5. 7.1 1.4 6.3 2.6 19.1 CI-2 1.52 7. 1.23 67.. 17.

4 Estimation of soil 13 Compression Index (CC) 14 Cc e pc Gs e logp mv cv k Optimal Neural Network & Accuracy Case Training G MARE R 2 Repetition (%) (%) r < 1 1<r<15 r > 15 CI CI CI CI Probability density Bars : Relative frequency (based on database) Open circles : Normal distribution Open squares : Cauchy distribution Relative errors CI 2 is the most optimum NN. The distribution of probability density of relative errors can be calculated. The errors of estimated value is discussed statistically. Compression ANALYSIS Index RESULT (CC) Three dimensional distribution The compression index of the lower part increased with depth. It decreases significantly in the bottom part of the Holocene clay layer. The maximum compression index was greater in the lower part of the east side of the subject area. 15

Anaysis of Kobe Aairport 17 Settlement vs elapsed time 1 Analytical method Soil water coupled analysis Elasto-plastic constitutive model Analytical model Sand mat Revetmen Rubble (1) Sand drain

5 Anaysis of Kobe Aairport 17 Settlement vs elapsed time 1 Analytical method Soil water coupled analysis Elasto-plastic constitutive model Analytical model Sand mat Revetmen Rubble (1) Sand drain Rubble Target South North cross section of Kobe Settlement (m) Elapsed time (day) Solid circles : Measured Solid line : Analytical (Case-1) Broken line : Analytical (Case-2) KC 2 The settlement in Case 2 is less than that in Case 1. The difference of Cc. Causes of difference of the measured and the analytical Analytical parameters Compression index Soild line : Case 1 Broken line : Case 2 Void ratio Pre-consolidation pressure & overburden pressure (kpa) Soild line : Pre-consolidation pressure Broken line : Overburden pressure Permeability (cm/s) 1.x1-9 1.x1-1.x1-7 The reclamation history of Kobe airport could not perfectly be modeled in the numerical analysis. The consolidation acceleration effect of ground improvement could not perfectly be estimated. The consolidation settlement of upper Pleistocene clay layers were not considered 19 CONCLUSIONS 2 In this paper, the applicability of an artificial neural network to estimating Holocene clay was discussed to establish the analytical model. 1. An artificial neural network could estimate the spatial distribution of analytical parameters used in deformation analysis of Kobe with high accuracy. 2. The deformation behavior of Holocene clay layer in Kobe could be reproduced through the numerical analysis, in which the analytical parame ters estimated by an artificial neural network were used. 3. The applicability of an artificial neural network to estimating Holocene clay, which are ap plied to predict the deformation of sea bed ground in construction of man made islands is confirmed.

6 Thank you Kazuhiro ODA Department of Civil Engineering, Division of Global Architecture, Graduate school of Engineering, Osaka University

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