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Showing 9 results for Fattahi

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.
H. Fattahi, S. Shojaee , M. A Ebrahimi Farsangi,
Volume 3, Issue 4 (10-2013)
Abstract

The development of an excavation damaged zone (EDZ) around an underground excavation can change the physical, mechanical and hydraulic behaviors of the rock mass near an underground space. This might result in endangering safety, achievement of costs and excavation planed. This paper presents an approach to build a prediction model for the assessment of EDZ, based upon rock mass characteristics changed. Rock engineering systems (RES) was used as an appropriate method for choosing the best parameter that expresses the occurrence of EDZ. Modulus of deformation with the highest weight in the system was selected as the most effective changed parameter. The adaptive network-based fuzzy inference system (ANFIS) with modulus of deformation as input was used to build a prediction model for the assessment of EDZ. Three ANFIS models were implemented, grid partitioning (GP), subtractive clustering method (SCM) and fuzzy c-means clustering method (FCM). A comparison was made between these three models and the results show the superiority of the ANFIS-SCM model. Furthermore, a case study in a test gallery of the Gotvand dam, Iran was carried out to illustrate the capability of the ANFIS model defined.
H. Fattahi,
Volume 5, Issue 1 (1-2015)
Abstract

The slope stability analysis is routinely performed by engineers to estimate the stability of river training works, road embankments, embankment dams, excavations and retaining walls. This paper presents a new approach to build a model for the prediction of slope stability state. 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 SVM was applied for the prediction of slope stability state, in which HS was used to determine the optimized free parameters of the SVM. A dataset that includes 55 data points was applied in current study, while 45 data points (80%) were used for constructing the model and the remainder data points (10 data points) were used for assessment of degree of accuracy and robustness. The results obtained indicate that the SVM-HS model can be used successfully for the prediction of slope stability state for circular failure.
H. Fattahi,
Volume 5, Issue 3 (8-2015)
Abstract

Displacements induced by earthquake can be very large and result in severe damage to earth and earth supported structures including embankment dams, road embankments, excavations and retaining walls. It is important, therefore, to be able to predict such displacements. In this paper, a new approach to prediction of earthquake induced displacements of slopes (EIDS) using hybrid support vector regression (SVR) with particle swarm optimization (PSO) is presented. The PSO is combined with the SVR for determining the optimal value of its user-defined parameters. The optimization implementation by the PSO significantly improves the generalization ability of the SVR. In this research, the input data for the EIDS prediction consist of values of geometrical and geotechnical input parameters. As an output, the model estimates the EIDS that can be modeled as a function approximation problem. A dataset that includes 45 data points was applied in current study, while 36 data points (80%) were used for constructing the model and the remainder data points (9 data points) were used for assessment of degree of accuracy and robustness. The results obtained show that the SVR-PSO model can be used successfully for prediction of the EIDS.
H. Fattahi,
Volume 6, Issue 1 (1-2016)
Abstract

Slope stability is one of the most complex and essential issues for civil and geotechnical engineers, mainly due to life and high economical losses resulting from these failures. In this paper, a new approach is presented for estimating the Safety Factor (SF) for circular failure slope using hybrid support vector regression (SVR) and Ant Colony Optimization (ACO). The ACO is combined with the SVR for determining the optimal value of its user-defined parameters. The optimization implementation by the ACO significantly improves the generalization ability of the SVR. In this research, the input data for the SF estimation consists of the values of geometrical and geotechnical input parameters. As an output, the model estimates the SF that can be modeled as a function approximation problem. A data set that includes 46 data points is applied in current study, while 32 data points are used for constructing the model, and the remainder data points (14 data points) are used for assessment of the degree of accuracy and robustness. The results obtained show that the hybrid SVR with ACO model can be used successfully for estimation of the SF.
H. Fattahi,
Volume 6, Issue 2 (6-2016)
Abstract

The  tunnel  boring  machine  (TBM)  penetration  rate  estimation  is  one  of  the  crucial  and complex  tasks  encountered  frequently  to  excavate  the  mechanical  tunnels.  Estimating  the machine  penetration  rate  may  reduce  the  risks  related  to  high  capital  costs  typical  for excavation  operation.  Thus  establishing  a  relationship  between  rock  properties  and  TBM penetration  rate  can  be  very  helpful  in  estimation  of  this  vital  parameter.  However, establishing relationship between rock properties and TBM penetration rate is not a simple task and cannot be done using a simple linear or nonlinear method. Adaptive neuro fuzzy inference system based on fuzzy c–means clustering algorithm (ANFIS–FCM) is one of the 
robust  artificial  intelligence  algorithms  proved  to  be  very  successful  in  recognition  of relationships  between  input  and  output  parameters.  The  aim  of  this  paper  is  to  show  the application of ANFIS–FCM in estimation of TBM performance. The model was applied to available data given in open source literatures. The results obtained show that the ANFIS–FCM model can be used successfully for estimation of the TBM performance.


H. Fattahi , Z. Bayatzadehfard,
Volume 7, Issue 1 (1-2017)
Abstract

Horizontal Directional Drilling (HDD) is extensively used in geothechnical engineering. In a variety of conditions it is essential to predict the torque required for performing the reaming operation. Nevertheless, there is presently not a convenient method to accomplish this task. To overcome this problem, in this research, the application of computational intelligence methods for data analysis named Support Vector Regression (SVR) optimized  by  differential evolution algorithm (DE) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate of the required rotational torque to operate horizontal directional drilling is demonstrated. Three ANFIS models were implemented, ANFIS–subtractive clustering method (ANFIS-SCM), ANFIS–grid partitioning (ANFIS-GP) and ANFIS–fuzzy c–means clustering method (ANFIS-FCM). The estimation abilities offered using SVR-DE, ANFIS-FCM, ANFIS-SCM, ANFIS-GP were presented by using field data given in open source literatures. In these models,  the rotational torque (M) is used as the output parameter, while the length of drill string in the borehole (L), axial force on the cutter/bit (P), rotational speed (revolutions per minute) of the bit (N), the radius for the ith reaming operation (Di), the mud flow rate (W), the total angular change of the borehole (KL), and the mud viscosity (V) are the input parameters. To compare the performance of models for rotational torque to operate horizontal directional drilling prediction, the coefficient of correlation (R2) and mean square error (MSE) of the models were calculated, indicating the good performance of the ANFIS-SCM model.


H. Fattahi,
Volume 9, Issue 2 (4-2019)
Abstract

key factor in the successful application of a tunnel boring machine (TBM) in tunneling is the ability to develop accurate penetration rate estimates for determining project schedule and costs. Thus establishing a relationship between rock properties and TBM penetration rate can be very helpful in estimation of this vital parameter. However, this parameter cannot be simply predicted since there are nonlinear and unknown relationships between rock properties and TBM penetration rate. Relevance vector regression (RVR) is one of the robust artificial intelligence algorithms proved to be very successful in recognition of relationships between input and output parameters. The aim of this paper is to show the application of RVR in prediction of TBM performance. The model was applied to available data given in open source literatures. In this model, uniaxial compressive strengths of the rock (UCS), the distance between planes of weakness in the rock mass (DPW) and rock quality designation (RQD) were utilized as the input parameters, while the measured TBM penetration rates was the output parameter. The performances of the proposed predictive model was examined according to two performance indices, i.e., coefficient of determination (R2) and mean square error (MSE). The obtained results of this study indicated that the RVR is a reliable method to predict penetration rate with a higher degree of accuracy.

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