Volume 3, Issue 2 (6-2013)                   2013, 3(2): 345-358 | Back to browse issues page

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Fattahi H, Ebrahimi Farsangi M A, Shojaee S, Nekooei K, Mansouri H. APPLICATION OF THE HYBRID HARMONY SEARCH WITH SUPPORT VECTOR MACHINE FOR IDENTIFICATION AND CALSSIFICATION OF DAMAGED ZONE AROUND UNDERGROUND SPACES. International Journal of Optimization in Civil Engineering 2013; 3 (2) :345-358
URL: http://ijoce.iust.ac.ir/article-1-137-en.html
Abstract:   (18732 Views)
An excavation damage zone (EDZ) can be defined as a rock zone where the rock properties and conditions have been changed due to the processes related to an excavation. This zone affects the behavior of rock mass surrounding the construction that reduces the stability and safety factor and increase probability of failure of the structure. This paper presents an approach to build a model for the identification and classification of the EDZ. The Support vector machine (SVM) is a new machine learning method based on statistical learning theory, which can solve the classification problem with small sampling, non-linearity and high dimension. However, the practicability of the SVM is influenced by the difficulty of selecting appropriate SVM parameters. In this study, the proposed hybrid Harmony search (HS) with the SVM was applied for identification and classification of damaged zone, in which HS was used to determine the optimized free parameters of the SVM. For identification and classification of the EDZ, based upon the modulus of the deformation modulus and using the hybrid of HS with the SVM a model for the identification and classification of the EDZ was built. To illustrate the capability of the HS-SVM model defined, field data from a test gallery of the Gotvand dam, Iran were used. The results obtained indicate that the HS-SVM model can be used successfully for identification and classification of damaged zone around underground spaces.
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Type of Study: Research | Subject: Optimal design
Received: 2013/04/27 | Accepted: 2013/04/27 | Published: 2013/04/27

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