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Showing 3 results for Mansouri

H. Fattahi, M. A. Ebrahimi Farsangi, S. Shojaee, K. Nekooei , H. Mansouri,
Volume 3, Issue 2 (6-2013)
Abstract

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.
H. Fattahi, S. Shojaee, M A. Ebrahimi Farsangi, H. Mansouri,
Volume 3, Issue 3 (9-2013)
Abstract

The excavation damaged 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. In this paper, a methodology was examined for computing the creation probability of damaged zone by Latin hypercube sampling based on a feed-forward artificial neural network (ANN) optimized by hybrid particle swarm optimization and genetic algorithm (HPSOGA). The HPSOGA was carried out to decide the initial weights of the neural network. A case study in a test gallery of the Gotvand dam, Iran was carried out and creation probabilities of 0.191 for highly damaged zone (HDZ) and 0.502 for EDZ were obtained.
R. Mansouri, M. Mohamadizadeh,
Volume 7, Issue 3 (7-2017)
Abstract

For any agency dealing with the design of the water distribution network, an economic design will be an objective. In this research, Central Force Optimization (CFO) and Differential Evolution (DE) algorithm were used to optimize Ismail Abad water Distribution network. Optimization of the network has been evaluated by developing an optimization model based on CFO and DE algorithm in MATLAB and the dynamic connection with EPANET software for network hydraulic calculation. Conclusions show CFO runtime is less than DE. While optimization of CFO (737,924 $) and DE (737,920 $) are %1.61 and %1.57 more than the absolute optimum that determined by the MILP method (726,463 $), respectively.



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