Showing papers in "Engineering With Computers in 2017"

Prediction of air-overpressure caused by mine blasting using a new hybrid PSO---SVR model

Abstract: The aim of the present study is to predict air-overpressure (AOp) resulting from blasting operations in the Shur river dam, Iran. AOp is considered as one of the most detrimental side effects induced by blasting. Therefore, accurate prediction of AOp is essential in order to minimize/reduce the environmental effects of blasting. This paper proposes a new hybrid model of particle swarm optimization (PSO) and support vector regression (SVR) for AOp prediction. To construct the PSO---SVR model, the linear (L), quadratic (Q) and radial basis (RBF) kernel functions were applied. Here, these combinations are abbreviated using PSO---SVR-L, PSO---SVR-Q and PSO---SVR-RBF. In order to check the accuracy of the proposed PSO---SVR models, multiple linear regression (MLR) was also utilized and developed. A database consisting of 83 datasets was applied to develop the predictive models. The performance of the all predictive models were evaluated by comparing performance indices, i.e. coefficient correlation (CC) and root mean square error (RMSE). As a result, PSO can be used as a reliable algorithm to train the SVR model. Moreover, it was found that the PSO---SVR---RBF model receives better results in comparison with other developed hybrid models in the field of AOp prediction.

Forecasting blast-induced ground vibration developing a CART model

Abstract: Drilling and blasting is an extensively used method for the rock fragmentation in surface mines and tunneling projects. Ground vibration is one of the most important environmental effects produced by blasting operations. In this research work, classification and regression tree (CART), multiple regression (MR), and different empirical models are used to develop predictions for ground vibrations induced by blasting operations conducted in the Miduk copper mine, Iran. To achieve this aim, a number of 86 blasting events were monitored, and the values of peak particle velocity (PPV) in terms of millimeter per second and two effective parameters on the PPV, namely, distance between blast-face and monitoring station in terms of meter and maximum charge used per delay in terms of kilogram, were measured. Performance of models established was evaluated using coefficient of correlation (R2), Nash and Sutcliffe (NS), and root mean square error (RMSE). The results revealed that the CART technique with R2 = 0.95, NS = 0.17, and RMSE = 0.17 provides a better performance when compared with empirical and MR models and has the capacity to generalize.

A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration

Abstract: Drilling and blasting is an inseparable part of the rock fragmentation process in open-pit mines. Prediction of blast-produced ground vibration is considered as an important issue in blasting works. The aim of this study is to propose a hybrid model for predicting blast-produced ground vibration in the Miduk copper mine, Iran, using combination of the artificial neural network (ANN) combined with artificial bee colony (ABC) (codename ABC-ANN). Here, ABC was used as an optimization algorithm to adjust weights and biases of the ANN. The predicted values of ground vibration by ANN and ABC-ANN models were also compared with several empirical models. In this regard, 89 blasting events were monitored and values of two influential factors on ground vibration, i.e., maximum charge weight used per delay (MC) and distance between monitoring station and blasting-point (DI) together with their peak particle velocity values (as an index of ground vibration) were carefully measured. The results of the predictive models have been compared with the data at hand using mean absolute percentage error, root mean squared error and coefficient of correlation (R2) criteria. Eventually, it was indicated that the constructed ABC-ANN model outperforms the other models in terms of the prediction accuracy and the generalization capability.

Prediction of blast-produced ground vibration using particle swarm optimization

Abstract: Blasting operation is an inseparable operation of the rock fragmentation process in the surface mines and tunneling projects. Ground vibration is one of the most undesirable effects induced by blasting operation which can cause damage to the surrounding residents and structures. So, the ability to make precise predictions of ground vibration is very important to reduce the environmental side effects caused by mine blasting. The aim of this paper is to develop a simple, accurate, and applicable model based on particle swarm optimization (PSO) approach for predicting the ground vibration induced by blasting operations in Shur River dam region, Iran. In this regard, two forms of PSO models, linear and power, were developed. For this work, a database including 80 data sets was collected, and the values of the maximum charge weight used per delay (W), distance between blast-point and monitoring station (D) and peak particle velocity (PPV) were measured. To develop the PSO models, PPV was used as output parameter, while W and D were used as input parameters. To check the performance of the proposed PSO models, multiple linear regression (MLR) model and United States Bureau of Mines (USBM) equation were also developed. Accuracy of models established was evaluated using statistical criteria, i.e., coefficient of correlation (R2) and root mean square error (RMSE), variance absolute relative error (VARE) and Nash & Sutcliffe (NS). Finally, it was found that the PSO power form provided more accurate predictions in comparison with PSO linear form, MLR and USBM models.

A finite element method for the numerical solution of Rayleigh–Stokes problem for a heated generalized second grade fluid with fractional derivatives

Abstract: Our main aim in the current paper is to find a numerical plan for 2D Rayleigh–Stokes model with fractional derivative on irregular domains such as circular, L-shaped and a unit square with a circular and square hole. The employed fractional derivative is the Riemann–Liouville sense. Also, by integrating the equation corresponding to the time variable and then using the Galerkin FEM for the space direction, we obtain a full discrete scheme. The unconditional stability and the convergence estimate of the new scheme have been concluded. Finally, we evaluate results of Galerkin FEM with other numerical methods.

Evaluating the modulus of elasticity of soil using soft computing system

Abstract: The elastic modulus of soil is a key parameter for geotechnical projects, transportation engineering, engineering geology and geotechnics, but its estimation in laboratory or field is complex and difficult task due to instrument handling problems, high cost, and it being a time consuming process. For this reason, the predictive models are useful tool for indirect estimation of elastic modulus. In this study, to determine the modulus of elasticity of soil, a rapid, less expensive, and reliable predictive model was proposed using artificial neural network (ANN). For this purpose, a series of laboratory tests were conducted to estimate the index properties (i.e., particle size fractions, plastic limit, liquid limit, unit weight, and specific gravity) and the modulus of elasticity of soils collected from Mahabaleshwar (Maharashtra), Malshej Ghat (Maharashtra), and Lucknow (Uttar Pradesh), in India. The input parameters in the developed ANN model are gravel, sand, fines, plastic limit, liquid limit, unit weight, and specific gravity, and the output is modulus of elasticity. The accuracy of the obtained ANN model was also compared with the multiple regression model based on coefficient of determination (R 2), the mean absolute error (MAE), and the variance account for (VAF). The ANN predictive model had the R 2, MAE, and VAF equal to 0.98, 5.07, and 97.64 %, respectively, superseding the performance of the multiple regression model. The performance comparison revealed that ANN model has more reliable predictive performance than multiple regression and it can be applied for predicting the modulus of elasticity of soil with more confidence. Thus, the result of the present study indicates that the modulus of elasticity of soil can reliably be estimated from the indirect method using ANN analysis with greater confidence.

Classification and regression tree technique in estimating peak particle velocity caused by blasting

Abstract: Blasting is a widely used technique for rock fragmentation in surface mines and tunneling projects. The ground vibrations produced by blasting operations are the main concern for the industries undertaking blasting operations, which can damage the surrounding structures, adjacent rock masses, roads and slopes in the vicinity. Therefore, proper prediction of blast-induced ground vibrations is essential to demarcate the safety area of blasting. In this research, classification and regression tree (CART) as a rule-based method was used to predict the peak particle velocity through a database comprising of 51 datasets with results of maximum charge per delay and distance from the blast face were fixed as model inputs. For comparison, the empirical and multiple regression (MR) models were also applied and proposed for peak particle velocity prediction. Performance of the proposed models were compared and evaluated using three statistical criteria, namely coefficient of correlation (R2), root mean square error (RMSE) and variance account for (VAF). Comparison of the obtained results demonstrated that the CART technique is more reliable for predicting the peak particle velocity than the MR and empirical models and it can be introduced as a new technique in this field.

Application of JAYA algorithm for the optimization of machining performance characteristics during the turning of CFRP (epoxy) composites: comparison with TLBO, GA, and ICA

Abstract: With the widespread application of carbon fibre-reinforced polymer (CFRP) composites, mostly in defence, automotive, and aerospace industries, the machining of those materials has become a major concern today. As the machinability of those composites differs from the conventional metals, a proper understanding of process behaviour and identification of the favourable machining environment (optimal setting of process parameters) are indeed necessary to improve product quality. The present work highlights the application potential of a multi-response optimization route by integrating nonlinear regression modelling, fuzzy inference system (FIS) in combination with the JAYA optimization algorithm, for the selection of optimal process parameter setting during the machining (turning) of carbon fibre-reinforced (epoxy) composites. Experiments have been carried out in consideration with spindle speed, feed rate, and depth of cut as process control parameters, whereas material removal rate (MRR), roughness average (Ra), and net cutting force have been treated as machining performance characteristics. Attempt has been made to identify the best setting of process parameters for optimizing aforesaid output responses, simultaneously. The result of the JAYA algorithm has also been compared to that of TLBO (teaching---learning-based optimization) algorithm. In addition to this, the result obtained thereof has also been compared to that of two evolutionary optimization algorithms viz., GA (genetic algorithm) and ICA (imperialist competitive algorithm). Good agreement has been observed amongst the obtained results. The aforesaid case experimental study thus exhibits the application potential of a newly developed JAYA algorithm in the context of machining performance optimization during the turning of CFRP composites. The JAYA algorithm is basically a parameter-less optimization algorithm which does not require any algorithm-specific parameter and hence easy to implement.

Energy conservation and power bonds in co-simulations: non-iterative adaptive step size control and error estimation

Abstract: Here, we study the flow of energy between coupled simulators in a co-simulation environment using the concept of power bonds. We introduce energy residuals which are a direct expression of the coupling errors and, hence, the accuracy of co-simulation results. We propose a novel energy-conservation-based co-simulation method (ECCO) for adaptive macro step size control to improve accuracy and efficiency. In contrast to most other co-simulation algorithms, this method is non-iterative and only requires knowledge of the current coupling data. Consequently, it allows for significant speed-ups and the protection of sensitive information contained within simulator models. A quarter car model with linear and nonlinear damping serves as a co-simulation benchmark and verifies the capabilities of the energy residual concept: reductions in the errors of up to 93% are achieved at no additional computational cost.

Application of cuckoo search algorithm to estimate peak particle velocity in mine blasting

Abstract: Ground vibration is one of the most undesirable effects of blasting operation in surface mines. Therefore, it seems that the prediction of ground vibrations with a high degree of accuracy is necessary to reduce environmental effects. This article proposes a novel swarm intelligence algorithm based on cuckoo search (NSICS) to create a precise equation for predicting the ground vibration produced by blasting operations in Miduk copper mine, Iran. To evaluate the proposed NSICS model, several empirical equations were also utilized. In this regard, 85 blasting events were considered, and the values of two effective parameters on the ground vibration, namely, maximum charge used per delay and distance between blast face and monitoring station, were measured. In addition, the values of peck particle velocity (PPV), as a vibration descriptor, were recorded in each blasting. Two performance indices, i.e., root mean square error and coefficient of multiple correlation (R2), were used to determine the performance capacity of the proposed models. Comparing the values predicted by the models demonstrated that the proposed equation by NSICS is more reliable than empirical equations in predicting the PPV.

Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models

Abstract: Brittleness of rock is one of the most critical features for design of underground excavation project. Therefore, proper assessing of rock brittleness can be very useful for designers and evaluators of geotechnical applications. In this study, feasibility of genetic programming (GP) model and non-linear multiple regression (NLMR) in predicting brittleness of intact rocks is examined. For this purpose, a dataset developed by conducting various rock tests including uniaxial compressive strength, Brazilian tensile strength, unit weight and brittleness via punch penetration on rock samples gathered from 48 tunnels projects around the world is utilized herein. Considering multiple inputs, several GP models were constructed to estimate brittleness index of the rock and finally, the best GP model was selected. Note that, GP can make an equation for predicting output of the system using model inputs. To show applicability of the developed GP model, non-linear multiple regression (NLMR) was also applied and developed. Considering some model performance indices, performance prediction of the GP and NLMR models were evaluated and it was found that the GP model is superior to NLMR one. Based on coefficient of determination (R2) of testing datasets, by proposing GP model, it can be improved from 0.882 (obtained by NLMR model) to 0.904. It is worth mentioning that the proposed predictive models in this study should be planned and used for the similar types of rock and the established inputs ranges.

Prediction and minimization of blast-induced ground vibration using two robust meta-heuristic algorithms

Abstract: During blasting operations in open pit mines, most of the blast energy (approximately 40%) is wasted due to ground vibration. This phenomenon causes damages to the surrounding structures and pit slope. Therefore, prediction of ground vibration with an appropriate degree of accuracy is important to identify safety area of blasting. In this paper, in the first step, a predictive equation based on gene expression programming (GEP) was developed to estimate ground vibrations of blasting operations conducted in Gol-E-Gohar iron mine. For this aim, 115 blasting operations were identified and the most effective parameters on peak particle velocity (PPV), i.e., burden, spacing, stemming, hole-depth, hole-diameter, powder factor, maximum charge per delay and distance from the blast face were collected from the mine. Capability of the developed GEP model was compared with a non-linear multiple regression (NLMR) model and five conventional equations. The obtained results revealed that the developed GEP model is more efficient compared to the other models in predicting PPV. In the second step, to optimize the GEP predictive model, cuckoo optimization algorithm (COA) was employed and proposed. To do this, several strategies were defined and then several optimized blasting patterns were determined for each strategy. It was found that by developing the COA model, a significant reduction can be happened in PPV values.

A Monte Carlo technique in safety assessment of slope under seismic condition

Abstract: In geotechnical engineering, stabilization of slopes is one of the significant issues that needs to be considered especially in seismic situation. Evaluation and precise prediction of factor of safety (FOS) of slopes can be useful for designing/analyzing very important structures such as dams and highways. Hence, in the present study, an attempt has been done to evaluate/predict FOS of many homogenous slopes in different conditions using Monte Carlo (MC) simulation technique. For achieving this aim, the most important parameters on the FOS were investigated, and finally, slope height (H), slope angle (α), cohesion (C), angle of internal friction ( $$\varnothing$$ ) and peak ground acceleration (PGA) were selected as model inputs to estimate FOS values. In the first step of analysis, a multiple linear regression (MLR) equation was developed and then it was used for evaluation and prediction by MC technique. Generally, MC model simulated FOS of less than 1.18, lower and higher than measured and predicted FOS values, respectively. However, the results of MC simulation for the FOS values of more than 1.33, is higher than those measured and predicted FOS values. As a result, the mean of FOS values simulated by MC was very close to the mean of actual FOS values. Moreover, results of sensitivity analysis demonstrated that the ( $$\varnothing$$ ), among other parameters, is the most effective one on FOS. The obtained results indicated that MC is a reliable approach for evaluating and estimating FOS of slopes with high degree of performance.

Estimation of blast-induced ground vibration through a soft computing framework

Abstract: Blasting is considered as the most common technique for rock breakage in surface mines, tunneling and large infrastructural development projects. Ground vibration, as one of the most adverse effects induced by blasting operations, can cause substantial damage to structures in the nearby environment. Therefore, there is a need to make accurate predictions of blast-induced ground vibration for minimizing the environmental effects. The present paper explores the possibility of using the genetic algorithm (GA) to create a new predictive model for estimating blast-induced ground vibration at Bakhtiari dam region, Iran. In this regard, two form of equations, i.e., power and linear forms, were developed by GA. Maximum charge weight per delay (W) and distance between monitoring station and blasting point (D), as the most influential parameters on the ground vibration, were used as the input or independent variables for modeling. Also, the peak particle velocity (PPV) parameter, as a descriptor for evaluating blast-induced ground vibration, was considered as the output or dependent variables for modeling. In total, 85 blasting events were considered and the D, W and PPV parameters were precisely measured. The selected GA forms were then compared with the several empirical prediction models. Finally, it was found that the GA power form (with root-mean-square error (RMSE) 0.45 and coefficient of multiple determination (R2) of 0.92) was more acceptable model for predicting PPV than the GA linear form and the empirical prediction models.

Engineering optimization based on ideal gas molecular movement algorithm

Abstract: The present work introduces a new metaheuristic optimization method based on the ideal gas molecular movement (IGMM) to solve mathematical and engineering optimization problems. Ideal gas molecules scatter throughout the confined environment quickly. This is embedded in the high speed of molecules, collisions between them and with the surrounding barriers. In IGMM algorithm, the initial population of gas molecules is randomly generated and the governing equations related to the velocity of gas molecules and collisions between those are utilized to accomplish the optimal solutions. To verify the performance of the IGMM algorithm, some mathematical and engineering benchmark optimization problems, commonly used in the literature, are inspected. Comparison of results obtained by IGMM with other optimization algorithms show that the proposed method has a challenging capacity in finding the optimal solutions and exhibits significance both in terms of the accuracy and reduction on the number of function evaluations vital in reaching the global optimum.

A novel fuzzy adaptive teaching---learning-based optimization (FATLBO) for solving structural optimization problems

Abstract: This paper presents a new optimization algorithm called fuzzy adaptive teaching---learning-based optimization (FATLBO) for solving numerical structural problems. This new algorithm introduces three new mechanisms for increasing the searching capability of teaching---learning-based optimization namely status monitor, fuzzy adaptive teaching---learning strategies, and remedial operator. The performance of FATLBO is compared with well-known optimization methods on 26 unconstrained mathematical problems and five structural engineering design problems. Based on the obtained results, it can be concluded that FATLBO is able to deliver excellence and competitive performance in solving various structural optimization problems.

Limited conjugate gradient method for structural reliability analysis

Abstract: A limited conjugate gradient method is proposed to improve the robustness and efficiency of the first-order reliability method (FORM). A new search direction vector is developed for structural reliability analysis using a limited scalar factor with Armijo's rule and sufficient descent condition, namely limited Fletcher---Reeves (LFR) method. The conjugate gradient search direction is adaptively determined based on limited scalar factor and the instabilities of FORM formula are dynamically controlled by sufficient descent condition in the proposed LFR method. The LFR method is compared with the Hasofer---Lind and Rackwitz---Fiessler (HL---RF), stability transformation method (STM), finite-step size (FSS), chaotic conjugate stability transformation method (CCSTM), and directional stability transformation method (DSTM) using five mathematical and structural problems. Results of numerical examples illustrated that LFR is more efficient than the STM, FSS, CCSTM, and DSTM reliability methods and more robust than the HL---RF for highly non-linear performance functions.

The numerical solution of two-dimensional logarithmic integral equations on normal domains using radial basis functions with polynomial precision

Abstract: The main purpose of this article is to investigate the numerical solution of two-dimensional Fredholm integral equations of the second kind on normal domains, whose kernels have logarithmic singularity. Radial basis functions constructed on scattered points are utilized as a basis in the discrete collocation method to solve these types of integral equations. We encounter logarithm-like singular integrals in the process of setting up the presented scheme which cannot be computed by classical quadrature formulae. Therefore, a special numerical integration rule is required to approximate such integrals based on the use of dual non-uniform composite Gauss---Legendre quadratures on normal domains. Since the method proposed in the current paper does not need any background mesh, it is meshless and consequently independent of the geometry of domain. The error estimate and the convergence rate of the approach are studied for the presented method. The convergence accuracy of the new technique is examined over four integral equations on the tear, annular, crescent, and castle domains, and obtained results confirm the theoretical error estimates.

Developing a new hybrid-AI model to predict blast-induced backbreak

Abstract: Drilling and blasting is the predominant rock excavation method in mining and tunneling projects. Back-break (BB) is one of the most undesirable by-products of blasting and causing rock mine wall instability, increasing blasting cost as well as decreasing performance of the blasting. In this research work, a practical new hybrid model to predict the blast-induced BB is proposed. The model is based on particle swarm optimization (PSO) in combination with adaptive neuro-fuzzy inference system (ANFIS). In this regard, a database including 80 datasets was collected from blasting operations of the Shur river dam region, Iran, and the values of four effective parameters on BB, i.e., burden, spacing, stemming and powder factor were precisely measured. In addition, the values of the BB for the whole 80 blasting events were measured. The accuracy of the proposed PSO-ANFIS model was also compared with the multiple linear regression (MLR). Median absolute error, coefficient of determination (R 2) and root mean squared error, as three statistical functions, were used to evaluate the performance of the predictive models. The results achieved indicate that the PSO-ANFIS model has strong potential to indirect prediction of BB with high degree of accuracy. The R 2 equal to 0.922 suggests the superiority of the PSO-ANFIS model in predicting BB, while this value was obtained as 0.857 for MLR model.

Multi-objective seismic design optimization of steel frames by a chaotic meta-heuristic algorithm

Abstract: In this study, multi-objective optimization is applied to implement performance-based design of steel moment-resisting frame (SMRF) structures. In order to efficiently achieve this purpose, a chaotic multi-objective firefly algorithm (CMOFA) is proposed to find the Pareto optimal front for the multi-objective performance-based optimum design (MO-PBOD) problem of SMRFs. The structural weight and the maximum inter-story drift at performance levels are taken as the conflicting objective functions of the MO-PBOD problem which should be optimized simultaneously subject to serviceability and ultimate limit-state constraints. In order to illustrate the efficiency of the proposed CMOFA meta-heuristic, two benchmark truss examples and three MO-PBOD examples of SMRFs are presented. The numerical results demonstrate the better computational performance of the proposed CMOFA meta-heuristic in comparison with some existing multi-objective algorithms.

Inverse Cauchy problem of annulus domains in the framework of spectral meshless radial point interpolation

Abstract: In this paper, the spectral meshless radial point interpolation (SMRPI) technique is applied to the Cauchy problems of two-dimensional elliptic PDEs in annulus domains. Unknown data on the inner boundary are obtained while overspecified boundary data are imposed on the outer boundary using the SMRPI. The SMRPI employs monomials and radial basis functions (RBFs) through interpolation and applies them locally with the help of spectral collocation ideas. In this way, localization in SMRPI can reduce the ill-conditioning for Cauchy problem. Furthermore, we improve previous results and overcome the ill-conditioning of Cauchy problem. It is revealed the SMRPI is more accurate and stable by adding strong perturbations.

Numerical modeling of shape and topology optimisation of a piezoelectric cantilever beam in an energy-harvesting sensor

Abstract: Piezoelectric materials are excellent transducers for converting mechanical energy from the environment for use as electrical energy. The conversion of mechanical energy to electrical energy is a key component in the development of self-powered devices, especially enabling technology for wireless sensor networks. This paper proposes an alternative method for predicting the power output of a bimorph cantilever beam using a finite-element method for both static and dynamic frequency analyses. A novel approach is presented for optimising the cantilever beam, by which the power density is maximised and the structural volume is minimised simultaneously. A two-stage optimisation is performed, i.e., a shape optimisation and then a "topology" hole opening optimisation.

The local radial point interpolation meshless method for solving Maxwell equations

Abstract: The Maxwell equations are basic equations of electromagnetic. In this paper we employed ADI–LRPIM (alternative direction implicit method is applied for approximating the time variable and the local radial point interpolation meshless method is used for space variable) to solve the two-dimensional time dependent Maxwell equations. This method consists of two stages for each time step implemented in alternative directions which are simple in computations. Local radial point interpolation method is a type of meshless method which uses a set of nodes scattered within the domain of the problem as well as a set of nodes scattered on the boundaries of the domain instead of using a predefined mesh to represent the problem domain and its boundaries, this feature makes, LRPIM to be flexible. Also it produces acceptable results for solving many partial differential equations. The proposed method is accurate and efficient, these features are illustrated by solving numerical examples in transverse magnetic and transverse electric fields. We used a kind of finite difference scheme for approximation of derivative terms in main relations to reduce errors and computational cost and eliminate integrals of weak form on internal boundaries by suitable selection of test function.

Application of direct meshless local Petrov---Galerkin (DMLPG) method for some Turing-type models

Abstract: Mathematical modeling of pattern formation in developmental biology leads to non-linear reaction---diffusion systems which are usually highly stiff in both diffusion and reaction terms. In this paper, the Direct Meshless Local Petrov---Galerkin (DMLPG) procedure is applied to find the numerical solution of some non-linear time-dependent reaction---diffusion systems such as Schnakenberg model, Gierer---Meinhardt model, FitzHugh---Nagumo model and Gray---Scott model. As far as we know, it is the first time that DMLPG method is applied for solving non-linear partial differential equations (PDEs) and systems of PDEs. Computational efficiency is the most significant advantage of the DMLPG method in comparison with the classic Meshless Local Petrov---Galerkin (MLPG) method. This is due to the fact that DMLPG shifts the numerical integrations over low-degree polynomials instead of over complicated moving least squares (MLS) shape functions and this reduces the computational costs, significantly. The main aim of this paper is to show that the DMLPG method is also suitable for solving the non-linear time-dependent systems, especially reaction---diffusion systems. Numerical results support the good efficiency of the proposed method for solving non-linear reaction---diffusion systems. Also it is shown that DMLPG provides considerable savings in computational time in comparison with the classical MLPG method.

A 2D mechanical–thermal coupled model to simulate material mixing observed in friction stir welding process

Abstract: The aim of our work is the numerical modeling of two dimensional mechanical–thermal material mixing observed in stir welding process using a high order algorithm. This algorithm is based on coupling a meshless approach, a time discretization, a homotopy transformation, a development in Taylor series and a continuation method. The performance of the proposed model is the consideration of large deformations in the formulation of the posed problem. For the spatial treatment, we use the moving least squares approximation which will be applied directly to the strong form formulation of conservation equations. Each collocation point holds mechanical–thermal variables. The high order algorithm and the homotopy transformation allow reducing the number of tangent matrices to decompose and to avoid iterative procedure. Comparisons with the classical iterative solver (Jamal et al. in J Comput Mech 28:375–380, 2002) are performed. Numerical results reveal that a few number of matrix factorization is needed with the proposed approach, decreasing the computation time.

Modeling the behavior of FRP-confined concrete using dynamic harmony search algorithm

Abstract: The accurate prediction of ultimate conditions for fiber reinforced polymer (FRP)-confined concrete is essential for the reliable structural analysis and design of resulting structural members. Nonlinear mathematical models can be used for accurate calibration of strength and strain enhancement ratios of FRP-confined concrete. In this paper, a new procedure is proposed to calibrate the nonlinear mathematical functions, which involved the use of a dynamic harmony search (DHS) algorithm. The harmony memory is dynamically adjusted based on a novel pitch generation scheme using a dynamic bandwidth and random number with normal standard distribution in DHS. A new design-oriented confinement model is proposed based on three influential factors of FRP area ratio ($$ \rho_{a} $$źa), lateral confinement stiffness ratio ($$ \rho_{E} $$źE), and strain ratio ($$ \rho_{\varepsilon } $$źź). Five nonlinear mathematical design-oriented models are regressed on approximately 1000 axial compression tests of FRP-confined concrete in circular sections based on the proposed DHS algorithm. The proposed models for the prediction of the ultimate axial stress and strain of FRP-confined concrete are compared with the existing models. It has been shown that the DHS algorithm offers the best performance in terms of both accuracy and fast convergence rate in comparison with the other modified versions of harmony search algorithms for optimization problems. The proposed design-oriented model provides improved accuracy over the existing models.

Indirect estimation of compressive and shear strength from simple index tests

Abstract: Uniaxial compressive and shear strength are two of the very important parameters, commonly required in the initial stages of planning and design of rock engineering projects. So, an attempt has been made in this study to predict compressive and shear strength (output) of rocks from some simple and easily determinable parameters in laboratory viz., point load index, tensile strength, unit weight and ultrasonic velocity (input). Failure modes have also been studied and correlated with their ultrasonic velocity. The study uses two of the most commonly used predictive mathematical techniques: statistical analysis and neural networks to predict the strength parameters. The regression analysis shows that the rock quality parameters are very well correlated with the ultrasonic velocity except for the unit weight. Unlike few researchers in the past, a linear correlation was best suited for the rock quality parameters in this study. On the other hand, Artificial Neural Network (ANN) was able to predict the same strength parameters with a better reliability than regression analysis. The study shows that the proposed method for the prediction of UCS and shear strength is acceptable and can be reliably applied in various rock engineering problems.

A local meshless method for solving multi-dimensional Vlasov---Poisson and Vlasov---Poisson---Fokker---Planck systems arising in plasma physics

Abstract: In this paper, we use a linear combination of the shape functions of reproducing kernel particle method (RKPM) and RBFs for achieving the unknown weights into each stencil. We obtain an error bound for the new shape function. Also, in this paper, we investigate a numerical procedure based on the presented technique for solving the Vlasov---Poisson and Vlasov---Poisson---Fokker---Planck systems. The Vlasov equation is a differential equation describing time evolution of the distribution function of plasma. The Vlasov---Poisson equations are used to describe various phenomena in plasma, in particular Landau damping and the distributions in a double layer plasma. We use the RKPM/RBF-FD technique for discretization of space direction and employ the method of lines to achieve a high-order accuracy in temporal direction. Numerical examples are reported which demonstrate the theoretical results and the efficiency of proposed scheme.

A meshless method for multi-material topology optimization based on the alternating active-phase algorithm

Abstract: In this work, a meshless method based on the alternating active-phase algorithm is proposed for the multi-material topology optimization problems. Mathematic model of the proposed method is built by the solid isotropic microstructure with penalization (SIMP) theory and solved by the optimality criteria. During the optimization process, the nodal relative density is chosen as the design variable and Shepard interpolation combined with the moving least squares (MLS) shape function is utilized to obtain the nodal relative density. Nodal integration method is then adopted to obtain the structural stiffness matrix, with the purpose of promoting the computational efficiency. Since the element-free Galerkin (EFG) method is applied to analyze the structure, sensitivity filtering is avoided and mesh-dependence phenomena are alleviated. Several numerical examples are provided to illustrate the validity and feasibility of the proposed method.

A fast multi-objective optimization using an efficient ideal gas molecular movement algorithm

Abstract: Recently, the ideal gas molecular movement (IGMM) algorithm was proposed by the authors as a new metaheuristic optimization technique for solving SOPs. In this paper, the intention is to extend the IGMM to solve MOPs while some modifications to the algorithm are taken place. The major improvement to the algorithm comprises usage of a neighbor-based non-dominated selection technique and defining a set of non-dominated solutions stored in an archive causing a globally faster convergence of the procedure. To evaluate the proposed algorithm, a set of standard benchmark problems, the so-called ZDT functions and two engineering benchmarks, are solved and the results were compared with five known multi-objective algorithms provided in the literature. Three different performance metrics; generational distance, spacing and maximum spread are introduced as well to evaluate multi-objective optimization problems. The Wilcoxon’s rank-sum nonparametric statistical test was also attempted which resulted on the fact that the proposed algorithm may exhibit a significantly better performance than those other techniques. The results from the real engineering applications also prove the advancement of the MO-IGMM performance in practice. Compared to five other multi-objective optimization evolutionary algorithms, simulation results show that in most cases, the proposed MO-IGMM is capable to find a much better uniformly spread of solutions with a faster convergence to the true Pareto optimal front.