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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References
Çelik, S., Tan, Ö.: Determination of preconsolidation pressure with artificial neural network. Civil Eng. Environ. Syst. 22(4), 217–231 (2005)
Lee, S.J., Lee, S.R., Kim, Y.S.: An approach to estimate unsaturated shear strength using artificial neural network and hyperbolic formulation. Comput. Geotech. 30(6), 489–503 (2000)
Park, H.I., Lee, S.R.: Evaluation of the compression index of soils using an artificial neural network. Comput. Geotech. 38(4), 472–481 (2011)
Pooya, N.F., Jaksa, M.B., Kakhi, M., et al.: Prediction of pile settlement using artificial neural networks based on standard penetration test data. Comput. Geotech. 36(7), 1125–1133 (2009)
Taskiran, T.: Prediction of California bearing ratio (CBR) of fine grained soils by AI methods. Adv. Eng. Softw. 41(6), 886–892 (2010)
Kalkan, E., Akbulut, S., Tortum, A., et al.: Prediction of the unconfined compressive strength of compacted granular soils by using inference systems. Environ. Geol. 58(7), 1429–1440 (2008)
Farrokhzad, F., Barari, A., Ibsen, L., et al.: Predicting subsurface soil layering and landslide risk with Artificial Neural Networks: a case study from Iran. Geologica Carpathica 62(5), 477–485 (2011)
Sivrikaya, O., Soycan, T.Y.: Estimation of compaction parameters of fine-grained soils in terms of compaction energy using artificial neural networks. Int. J. Numer. Anal. Meth. Geomech. 35(17), 1830–1841 (2011)
Zhao, H., Huang, Z., Zou, Z.: Simulating the stress-strain relationship of geomaterials by support vector machine. Math. Prob. Eng. 2014, 1–7 (2014)
Ozer, M., Isik, N.S., Orhan, M.: Statistical and neural network assessment of the compression index of clay-bearing soils. Bull. Eng. Geol. Env. 67(4), 537–545 (2008)
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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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DOI: https://doi.org/10.1007/978-981-13-0122-3_23
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