An ANN Based Sensitivity Analysis of Factors Affecting Stability of Gravity Hunched Back Quay Walls

Samir Karimnader-Shalkouhi, Mehran Karimpour Fard, Sandro Machado


This paper presents Artificial Neural Network (ANN) prediction models that relate the safety factors of a quay wall against sliding, overturning and bearing capacity failure to the soil geotechnical properties, the geometry of the gravity hunched back quay walls and the loading conditions. In this study, a database of around 80000 hypothetical data sets was created using a conceptual model of a gravity hunched back quay wall with different geometries, loading conditions and geotechnical properties of the soil backfill and the wall foundation. To create this database a MATLAB aided program was written based on one of the most common manuals, OCDI (2002). Comparison between the results of the developed models and cases in the data bank indicates that the predictions are within a confidence interval of 95%. To evaluate the effect of each factor on these values of factor of safety, sensitivity analysis were performed and discussed. According to the performed sensitivity analysis, shear strength parameters of the soil behind and beneath the walls are the most important variables in predicting the safety factors.


Quay Wall; Hunched Back; Safety Factor; Sliding; Overturning; Bearing Capacity; Artificial Neural Network.


PIANC (2001), Seismic Design Guidelines for Port Structures, Permanent International Association for Navigation Congresses, Balkema, 474p.

Sadrekarimi, A., Ghalandarzadeh, A., and Sadrekarimi, J. "Static and dynamic behavior of hunchbacked gravity quay walls." Soil Dyn. Earthquake Eng. 28(2) (2008): 99–117.

Sadrekarimi, A. "Seismic Displacement of Broken-Back Gravity Quay Walls." Journal of Waterway, Port, Coastal, and Ocean Engineering, Vol. 137, (2011): No. 2.

Sadrekarimi, A. "Pseudo-static lateral earth pressures on brokenback retaining walls Seismic Displacement of Broken-Back Gravity Quay Walls", Canadian Journal of Geotechnical Engineering, Vol. 47, (2010): 1247–1258

Technical Standards and Commentaries for Port and Harbour Facilities in Japan, The Overseas Coastal Area Development Institute of Japan (OCDI), (2002): 600P.

Okabe, S. "General theory of earth pressure and seismic stability of retaining wall and dam". Journal of the Japanese Society of Civil Engineers, 10 (5) (1924): 1277–1323.

Mononobe, N. and Matsuo, M. (1929). On the determination of earth pressures during earthquakes: Proceedings of the World Engineering Congress, Tokyo, Japan. International Association for Earthquake Engineering, Japan. 1929, Vol.9, pp. 177–185.

Ichihara, M., and Matsuzawa, H. "Earth pressure during earth-quake". Soils and Foundations, 13 (4) (1973): 75–86

Westergaard, H.M. "Water pressure on dams during earthquakes". Transactions of the American Society of Civil Engineers, 98 (1933): 418–472.

Goh, ATC. "Nonlinear modeling in geotechnical engineering using neural networks". Aust Civ Eng Trans CE36(4) (1994): 293–297

Ural, D. N., and Saka, H. "Liquefaction assessment by neural networks". Electronic Journal of Geotechnical Engineering. (1998):

Najjar, Y. M., and Ali, H. E. "CPT-based liquefaction potential assessment: A neuronet approach". Geotechnical Special Publication, ASCE, 1, (1998): 542-553.

Sivakugan, N., Eckersley, J. D., and Li, H. "Settlement predictions using neural networks". Australian Civil Engineering Transactions, CE40, (1998): 49-52.

Ghiassian, H., Jamshidi, R., and Poorebrahim, G. "Neural networks analysis of silty sand reinforced by carpet wastes" Kuwait Journal of Science and Engineering, (2006): 33(1) pp. 119-139

Sinha, S. K. and Wang, M. C. "Artificial Neural Network Prediction Models for Soil Compaction and Permeability". Geotechnical and Geological Engineering, Vol. 26, Issue 1, (2008): pp 47–64

Gunaydin, O. "Estimation of soil compaction parameters by using statistical analyses and artificial neural networks". Environmental Geology (2009): 57:203

Rezania, M., Javadi, AA. and Giustolisi, O. "An evolutionary-based data mining technique for assessment of civil engineering systems". JEng Comput 25(6) (2008): 500–517.

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