An ANN Based Sensitivity Analysis of Factors Affecting Stability of Gravity Hunched Back Quay Walls - Karimnader-Shalkouhi, Samir; Karimpour Fard, Mehran; Machado, Sandro (Salehan Institute of Higher Education, 2017)
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.