سیویلیکا را در شبکه های اجتماعی دنبال نمایید.

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

Publish Year: 1396
Type: Journal paper
Language: English
View: 457

This Paper With 18 Page And PDF Format Ready To Download

این Paper در بخشهای موضوعی زیر دسته بندی شده است:

Export:

Link to this Paper:

Document National Code:

JR_CEJ-3-5_001

Index date: 28 August 2017

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

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

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

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

s Karimnader-Shalkouhi

University of Guilan, Rasht, Guilan, Iran.

M. Karimpour-Fard

University of Guilan, Rasht, Guilan, Iran.

s.l Machado

Dept. of Materials science and technology, Federal University of Bahia., Salvador-BA, Brazil.