Detection of defective pile geometries using a coupled FEM/SBFEM approach and an ant colony classification algorithm

Piles are widely used to build a proper foundation for various buildings. The piles’ quality in situ can be tested by a so-called pile integrity test. In order to apply this test, an impulse is given to the piles’ head which generates a p -wave running through the pile. An acceleration sensor is att...

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Bibliographic Details
Published inActa mechanica Vol. 227; no. 5; pp. 1279 - 1291
Main Authors Psychas, Iraklis Dimitrios, Schauer, Marco, Böhrnsen, Jens-Uwe, Marinaki, Magdalene, Marinakis, Yannis, Langer, Sabine C., Stavroulakis, Georgios E.
Format Journal Article
LanguageEnglish
Published Vienna Springer Vienna 01.05.2016
Springer
Springer Nature B.V
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Summary:Piles are widely used to build a proper foundation for various buildings. The piles’ quality in situ can be tested by a so-called pile integrity test. In order to apply this test, an impulse is given to the piles’ head which generates a p -wave running through the pile. An acceleration sensor is attached to the piles’ head, to measure the vertical movement. The major part of this wave is reflected from the piles’ toe and is measured by the attached acceleration sensor on top of the pile. This yields an acceleration–time plot which has to be analysed in order to determine the piles’ condition with respect to structural consistence and mostly radius defects. Since deviations in the cross section of the pile cause additional reflections, suitable post-processing can be used in order to detect these defects. In this paper, we propose an ant colony classification model to detect structural defects in piles by evaluating displacement–time plots to improve the reliability of pile monitoring. The data of these plots result to numerically performed pile integrity tests. To conduct these tests, a simulation of a combined finite element method and scaled boundary finite element methods has been carried out. These results are used for learning and training the ant colony classification model and to have different sets of data to validate the optimization algorithm. The position and the type of piles’ defect can be identified by the applied algorithm.
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ISSN:0001-5970
1619-6937
DOI:10.1007/s00707-015-1548-3