جستجوی مقالات مرتبط با کلیدواژه « Particle Swarm Optimization » در نشریات گروه « مهندسی معدن »

Surface settlement induced by tunneling is one of the most crucial problems in urban environments. Hence, accurate prediction of soil geotechnical properties is an important prerequisite in the minimization of it. In this research work, the amount of surface settlement is predicted using three-dimensional numerical simulation in the finite difference method and Artificial Neural Network (ANN). In order to determine the real geotechnical properties of soil layers around the tunnel; back-analysis is carried out using the optimization algorithm and monitoring data. Among the different optimization methods, genetic algorithm (GA) and particle swarm optimization (PSO) are selected, and their performance is compared. The results obtained show that the artificial neural network has a high ability with the amounts of R=0.99, RMSE=0.0117, and MSE= 0.000138 in predicting the surface settlement obtained from 150 simulations from randomly generated data. Comparing the results of back-analysis using the optimization algorithm, the genetic algorithm shows less error than the particle swarm algorithm in different initial populations. In all cases of analysis, the calculation time for both algorithms lasts about 5 minutes, which indicates the applicability of both algorithms in optimizing the parameters in mechanized tunneling in a short time.

A probabilistic neural network (PNN) is a feed-forward neural network using a smoothing parameter. We used the PNN algorithm based on single and multi-smoothing parameters for multi-dimensional data classification. Using multi-smoothing parameters, we implemented an improved probabilistic neural network (PNN) to estimate the porosity distribution of a gas reservoir in the North Sea. Comparing the results of implementing smoothing parameters obtained from model-based optimization and particle swarm optimization (PSO) indicated the efficiency of PNN in characterizing the gas. Also, results showed that while the PSO algorithm was able to specify smoothing parameters with more precision, about 9%, it was very time-consuming. Finally, multi PNN based on PSO was applied to estimate the porosity distribution of the F3 reservoir. The results validated the main fracture or gas chimney of the F3 reservoir with higher porosity. Also, gas-bearing layers were highlighted by energy and similarity attributes.

Scientific uncertainties make the grade estimation very complicated and important in the metallic ore deposits. This paper introduces a new hybrid method for estimating the iron ore grade using a combination of two artificial intelligence methods; it is based on the single layer-extreme learning machine and the particle swarm optimization approaches, and is designed based on the location of the boreholes, depth of the boreholes, and drill hole information from an orebody, and applied for the ore grade estimation on the basis of a block model. In this work, the two algorithms of optimization clustering and neural networks are used for the iron grade estimation in the Choghart iron ore north anomaly in the central Iran. The results of the training and testing the algorithms indicate a significant ability of the optimized neural network system in the ore grade estimation.

Bedrock unconfined compressive strength (UCS) is a key parameter in designing thegeosciences and building related projects comprising both the underground and surface rock structures. Determination of rock UCS using standard laboratory tests is a complicated, expensive, and time-consuming process, which requires fresh core specimens. However, preparing fresh cores is not always possible, especially during the drilling operation in cracked, fractured, and weak rocks. Therefore, some attempts have recently been made to develop the indirect methods, i.e. intelligent predictive models for rock UCS estimation, which require no core preparation and laboratory equipment. This work focuses on the application of new combinations of intelligent techniques including adoptive neuro-fuzzy inference system (ANFIS), genetic algorithm (GA), and particle swarm optimization (PSO) in order to predict rock UCS. These models were constructed based on the collected laboratory datasets upon 93 core specimens ranging from weak to very strong rock types. The proposed hybrid model results were compared with each other, and the real data and multiple regression (MR) results. These comparisons were made using coefficient of correlation, mean of square error, mean of absolute error, and variance account for indices. The comparison results proved that the ANFIS-GA combination had a relatively higher accuracy than the ANFIS-PSO combination, and both had a higher capability than the MR model. Furthermore, the ANFIS-GA and ANFIS-PSO model results were completely in accordance with the UCS laboratory test, and they were more accurate than the previous single/hybrid intelligent models. Lastly, a parametric study of the suggested models showed that the density and Schmidt hammer rebound had the highest influence, and porosity had the lowest influence on the output (UCS).

The generalized effective-medium theory of induced polarization (GEMTIP) is a newly developed relaxation model that incorporates the petro-physical and structural characteristics of polarizable rocks in the grain/porous scale to model their complex resistivity/conductivity spectra. The inversion of the GEMTIP relaxation model parameter from spectral-induced polarization data is a challenging issue because of the highly non-linear dependency of the observed data on the model parameter and non-uniqueness of the problem. To solve these problems as well as scape the local minima of the highly complicated cost function, the genetic algorithm (GA) can be applied but it has proven to be time-intensive computationally. However, this drawback can be resolved by incorporating a faster algorithm, e.g. particle swarm optimization (PSO). The aim of this work is to investigate whether recovering the model parameter of the ellipsoidal GEMTIP model from SIP data using the combined GA and PSO algorithms is possible. To achieve this aim, we set the best calculated individuals using GA as the search space of PSO, and then the best location achieved by PSO in each iteration is assigned as the updated model parameters. The results of our research work reveal that the model parameters can effectively be recovered using the approach proposed in this paper but the time constant of a noisy data that arises from the adverse dependency of this parameter on the ellipticity of a polarizable grain. Moreover, the execution time of the ellipsoidal GEMTIP modeling of complex resistivity data can be significantly improved using the proposed algorithm.

In this work, we tried to automatically optimize the cost of the concrete segmental lining used as a support system in the case study of Mashhad Urban Railway Line 2 located in NE Iran. Two meta-heuristic optimization methods including particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were presented. The penalty function was used for unfeasible solutions, and the segmental lining structure was defined by nine design variables: the geometrical parameters of the lining cross-section, the reinforced feature parameters, and the dowel feature parameters used among the joints to connect the segment pieces. Furthermore, the design constrains were implemented in accordance with the American Concrete Institute code (ACI318M-08) and guidelines of lining design proposed by the International Tunnel Association (ITA). The objective function consisted of the total cost of structure preparation and implementation. Consequently, the optimum design of the system was analyzed using the PSO and ICA algorithms. The results obtained showed that the objective function of the support system by the PSO and ICA algorithms reduced 12.6% and 14% per meter, respectively.

Permeability is the ability of porous rock to transmit fluids and one of the most important properties of reservoir rock because oil production depends on the permeability of reservoirs. Permeability is determined using a variety of methods which are usually expensive and time consuming. Reservoir rock properties with image analysis and intelligent systems has been used to reduce time and money. This study presents an improved model based on the integration of petrographic data and intelligent systems to predict permeability. Petrographic image analysis was employed to measure the types of porosity including inter granular, intra granular, moldic, micro and optical, amount of cement, limestone, dolomite and anhydrite, types of texture and mean geometrical shape coefficient of pores. The permeability was first predicted using the three individual intelligent systems including a neural network (NN), a fuzzy logic (FL), and a neuro-fuzzy (NF) model, respectively. The mean squared error (MSE) of the NN, FL and NF methods are 0.0107, 0.0081 and 0.0080, which correspond to the R2 values of 0.8830, 0.9193 and 0.9136, respectively. Afterwards, two types of committee machine with intelligent systems (CMIS) were used to combine the predicted values of permeability from individual intelligent systems: simple averaging (SA) and weighted averaging (WA). In the WA, a particle swarm optimization (PSO) was employed to obtain the optimal contribution of each intelligent system. The MSE of the CMIS-SA and CMIS-WA are 0.0072 and 0.0066, which correspond to the R2 values of 0.9262 and 0.9260, respectively. These show that the CMIS-WA performed better than NN, FL, and NF models individually. In addition, a multiple linear regression (MLR) was used to compare with the other techniques. The R2 value between the core and MLR permeability is 0.8699. Thus, the integration of petrographic data and intelligent systems operated more accurate than the MLR model.

Shear-wave velocity () is an important parameter used for site characterization in geotechnical engineering. However, dispersion curve inversion is challenging for most inversion methods due to its high non-linearity and mix-determined trait. In order to overcome these problems, in this study, a joint inversion strategy is proposed based on the particle swarm optimization (PSO) algorithm. The seismic data considered for designing the objects are the Rayleigh wave dispersion curve and seismic refraction travel time. For joint inversion, the objective functions are combined into a single function. The proposed algorithm is tested on two synthetic datasets, and also on an experimental dataset. The synthetic models demonstrate that the joint inversion of Rayleigh wave and travel time return a more accurate estimation of VS in comparison with the single inversion Rayleigh wave dispersion curves. To prove the applicability of the proposed method, we apply it in a sample site in the city of Tabriz located in the NW of Iran. For a real dataset, we use refraction microtremor (ReMi) as a passive method for getting the Rayleigh wave dispersion curves. Using the PSO joint inversion, a three-layer subsurface model was delineated.The results obtained for the synthetic datasets and field dataset show that the proposed joint inversion method significantly reduces the uncertainties in the inverted models, and improves the revelation of boundaries.