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Predicting Compression Index Using Artificial Neural Networks: A Case Study from Dalian Artificial Island

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Proceedings of GeoShanghai 2018 International Conference: Ground Improvement and Geosynthetics (GSIC 2018)

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Abstract

Compression index is very important in the design of geotechnical engineering such as consolidation settlement prediction and construction design. However, measuring compression index is very complex and time-consuming. In addition, it is very difficult to collect unbroken core samples from underground. Artificial neural network has been adopted in some geotechnical applications and has achieved some success. In this paper, artificial neural network (ANN) models are developed for estimating compression index by basic soil parameters based on 2859 soil test data. All of the marine soil samples, which are divided into three subsets to train the optimum model, are collected from Dalian Artificial Island and compression index and other parameters are measured in soil mechanical laboratory as well. At last, the optimized ANN model structure and suitable inputs are determined followed by the comparison between empirical formulas predictions and ANN models output. It is revealed that ANN models perform better than empirical formulas with respect to the accuracy of compression index prediction.

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Author information

Authors and Affiliations

  1. State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, China

    Zhijia Xue, Xiaowei Tang & Qing Yang

Authors
  1. Zhijia Xue
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  2. Xiaowei Tang
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  3. Qing Yang
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Corresponding author

Correspondence to Zhijia Xue .

Editor information

Editors and Affiliations

  1. Department of Civil and Environmental Engineering, Jackson State University, Jackson, Mississippi, USA

    Lin Li

  2. Iowa State University, Ames, Iowa, USA

    Bora Cetin

  3. Oklahoma State University, Oklahoma, Oklahoma, USA

    Xiaoming Yang

Appendix (Weights and Bias of 4-15-1 Tansigmoid-Linear ANN Model)

Appendix (Weights and Bias of 4-15-1 Tansigmoid-Linear ANN Model)

W1

2.622903

4.083057

−1.22764

0.896845

1.350291

−2.33406

−2.70143

−3.50748

−2.33836

0.22522

−4.40407

1.415465

0.909055

−2.85348

−6.9055

−2.61151

3.461982

−4.91683

0.374592

0.850418

3.920666

0.637272

4.992674

−0.48619

−0.19546

−3.75934

1.610851

−5.08277

1.247785

−0.73106

1.083275

−1.26217

−1.05275

−1.7319

2.852696

3.852191

−5.53328

−1.61691

−4.20678

6.063494

−0.08983

−1.33851

−4.45048

3.358993

−2.61411

−0.58812

−2.32164

4.178343

−1.96377

0.219849

−1.37967

−4.30137

6.408356

−0.77561

2.902883

0.558393

−3.26617

−1.40982

3.581199

−0.82867

B1

W2

B2

−1.57088

−6.10517

−0.02331

0.054728

−5.15034

 

1.280595

4.743402

−4.86217

−6.31247

2.788758

−7.68178

1.180403

−1.18919

1.596082

−1.75728

−2.98951

0.639066

−1.87916

2.290802

−1.14495

−2.59815

2.56238

−1.5928

−1.47991

−3.87658

1.482598

−4.67425

0.774307

2.109405

−0.10101

−5.39566

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Xue, Z., Tang, X., Yang, Q. (2018). Predicting Compression Index Using Artificial Neural Networks: A Case Study from Dalian Artificial Island. In: Li, L., Cetin, B., Yang, X. (eds) Proceedings of GeoShanghai 2018 International Conference: Ground Improvement and Geosynthetics. GSIC 2018. Springer, Singapore. https://doi.org/10.1007/978-981-13-0122-3_23

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