Three‐dimensional responses of buried corrugated pipes and ANN‐based method for predicting pipe deflections

Abstract

SUMMARYThis study investigated localized responses, such as circumferential stresses, on corrugation and pipe deflections. Also, this study examined the effect of corrugation geometry on the overall and localized response of corrugated pipes with refined three-dimensional modeling of the entire soil-pipe interaction system, including corrugation. To investigate the availability of the traditional two-dimensional method, the results from the three-dimensional finite element method (FEM) were compared with those from the twodimensional FEM. The soil-pipe modeling techniques of this study were verified by comparing the FEM results by Utah State University and analytical results. An artificial neural network (ANN)-based model to predict vertical deflections of buried corrugated pipes was developed to overcome the shortcomings of existing methods and obtain results that are close to the level of accuracy of FEM results. In order to train an ANN, analyses on a large amount of data were executed with various standardized pipe geometries and burial depths regulated by the Korea Highway Corporation using the two-dimensional FEM verified in this study. The widely used back propagation algorithm was adopted. The ANN-based model developed in this study was shown to be an effective tool by comparing the results with test data and sensitivity analyses were executed based on the data from the developed ANN.

Load-settlement response of a footing over buried conduit in a sloping terrain: a numerical experiment-based artificial intelligent approach

“…Finite element modelling (FEM) can be used to solve complex geotechnical problems and achieve more accurate results (Khan and Shukla 2021b). However, the use of expensive software for FEM analysis significantly limits their application (Kim et al 2012). In recent times, the use of machine learning techniques has been widely used in mapping the non-linear relationships between the input and output variables (e.g., Ahmadi et al 2019;Yekani Motlagh et al 2019;Aamir et al 2020;Dorosti et al 2020;Ghorbani et al 2021;Kaloop et al 2021).…”

“…The machine learning (ML) models that are based on large quantities of FEM data have also been developed to solve complex problems like soil-conduit interaction and settlement of foundations. Kim et al (2012) employed FEM based artificial neural network (ANN) to predict deflections of buried corrugated conduits. The data collected from three-dimensional finite element modelling were used to develop a backpropagation (BP) neural network that examined the factors affecting the structural response of different corrugated conduits buried at various depths under the level ground.…”