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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted alphabetically
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 61)
Annals of Applied Statistics     Full-text available via subscription   (Followers: 39)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Argumentation et analyse du discours     Open Access   (Followers: 10)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 4)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 13)
Bernoulli     Full-text available via subscription   (Followers: 9)
Biometrical Journal     Hybrid Journal   (Followers: 10)
Biometrics     Hybrid Journal   (Followers: 51)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 18)
Building Simulation     Hybrid Journal   (Followers: 1)
Bulletin of Statistics     Full-text available via subscription   (Followers: 4)
CHANCE     Hybrid Journal   (Followers: 5)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 11)
Computational Statistics     Hybrid Journal   (Followers: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 37)
Current Research in Biostatistics     Open Access   (Followers: 8)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 11)
Demographic Research     Open Access   (Followers: 16)
Electronic Journal of Statistics     Open Access   (Followers: 8)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
ESAIM: Probability and Statistics     Full-text available via subscription   (Followers: 5)
Extremes     Hybrid Journal   (Followers: 2)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 13)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 5)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
International Journal of Stochastic Analysis     Open Access   (Followers: 3)
International Statistical Review     Hybrid Journal   (Followers: 12)
International Trade by Commodity Statistics - Statistiques du commerce international par produit     Full-text available via subscription  
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 4)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 20)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 39, SJR: 3.664, CiteScore: 2)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 20)
Journal of Econometrics     Hybrid Journal   (Followers: 82)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 6)
Journal of Forecasting     Hybrid Journal   (Followers: 17)
Journal of Global Optimization     Hybrid Journal   (Followers: 7)
Journal of Interactive Marketing     Hybrid Journal   (Followers: 10)
Journal of Mathematics and Statistics     Open Access   (Followers: 8)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Journal of Probability and Statistics     Open Access   (Followers: 10)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 32)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 5)
Journal of Statistical Physics     Hybrid Journal   (Followers: 13)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 8)
Journal of Statistical Software     Open Access   (Followers: 20, SJR: 13.802, CiteScore: 16)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 72, SJR: 3.746, CiteScore: 2)
Journal of the Korean Statistical Society     Hybrid Journal   (Followers: 1)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 31)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 26)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 43)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 16)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 30)
Law, Probability and Risk     Hybrid Journal   (Followers: 8)
Lifetime Data Analysis     Hybrid Journal   (Followers: 7)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Metrika     Hybrid Journal   (Followers: 4)
Modelling of Mechanical Systems     Full-text available via subscription   (Followers: 1)
Monte Carlo Methods and Applications     Hybrid Journal   (Followers: 6)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 2)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 5)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 8)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 34)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 17)
Probability Surveys     Open Access   (Followers: 4)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 7)
Review of Economics and Statistics     Hybrid Journal   (Followers: 124)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 15)
Sankhya A     Hybrid Journal   (Followers: 2)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Sequential Analysis: Design Methods and Applications     Hybrid Journal  
Significance     Hybrid Journal   (Followers: 7)
Sociological Methods & Research     Hybrid Journal   (Followers: 37)
SourceOCDE Comptes nationaux et Statistiques retrospectives     Full-text available via subscription  
SourceOCDE Statistiques : Sources et methodes     Full-text available via subscription  
SourceOECD Bank Profitability Statistics - SourceOCDE Rentabilite des banques     Full-text available via subscription   (Followers: 1)
SourceOECD Insurance Statistics - SourceOCDE Statistiques d'assurance     Full-text available via subscription   (Followers: 2)
SourceOECD Main Economic Indicators - SourceOCDE Principaux indicateurs economiques     Full-text available via subscription   (Followers: 1)
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
SourceOECD Monthly Statistics of International Trade     Full-text available via subscription   (Followers: 1)
SourceOECD National Accounts & Historical Statistics     Full-text available via subscription  
SourceOECD OECD Economic Outlook Database - SourceOCDE Statistiques des Perspectives economiques de l'OCDE     Full-text available via subscription   (Followers: 2)
SourceOECD Science and Technology Statistics - SourceOCDE Base de donnees des sciences et de la technologie     Full-text available via subscription  
SourceOECD Statistics Sources & Methods     Full-text available via subscription   (Followers: 1)
SourceOECD Taxing Wages Statistics - SourceOCDE Statistiques des impots sur les salaires     Full-text available via subscription  
Stata Journal     Full-text available via subscription   (Followers: 9)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Applications in Genetics and Molecular Biology     Hybrid Journal   (Followers: 5)
Statistical Communications in Infectious Diseases     Hybrid Journal  
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methodology     Hybrid Journal   (Followers: 7)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 27)
Statistical Modelling     Hybrid Journal   (Followers: 19)
Statistical Papers     Hybrid Journal   (Followers: 4)
Statistical Science     Full-text available via subscription   (Followers: 13)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Statistics & Risk Modeling     Hybrid Journal   (Followers: 2)
Statistics and Computing     Hybrid Journal   (Followers: 13)
Statistics and Economics     Open Access   (Followers: 1)
Statistics in Medicine     Hybrid Journal   (Followers: 191)
Statistics, Politics and Policy     Hybrid Journal   (Followers: 6)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 14)
Stochastic Models     Hybrid Journal   (Followers: 3)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Teaching Statistics     Hybrid Journal   (Followers: 7)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 3)
The American Statistician     Full-text available via subscription   (Followers: 24)
The Annals of Applied Probability     Full-text available via subscription   (Followers: 8)
The Annals of Probability     Full-text available via subscription   (Followers: 10)
The Annals of Statistics     Full-text available via subscription   (Followers: 34)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 11)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)

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Engineering With Computers
Journal Prestige (SJR): 0.485
Citation Impact (citeScore): 2
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1435-5663 - ISSN (Online) 0177-0667
Published by Springer-Verlag Homepage  [2626 journals]
  • Quick integrative optimizers for minimizing the error of neural computing
           in pan evaporation modeling
    • Abstract: Abstract To achieve an efficient methodology for approximating pan evaporation (EP), this study offers two metaheuristic-integrated predictors. Shuffled complex evolution (SCE) and electromagnetic field optimization (EFO) are two of the fastest metaheuristic algorithms that are synthesized with artificial neural network (ANN). By doing this, the ANN is optimized in a noticeably shorter time compared to its integration with other metaheuristic techniques. Five-year climatic data of the Bakersfield station (California, USA) with an 80:20 ratio are used for developing and testing the methods. The proposed hybrids are implemented with appropriate population sizes (20 and 35 for the SCE and EFO, respectively) and their results are compared to a single ANN. Accuracy evaluation (correlation coefficients > 0.99) professed that the neural network with both conventional and sophisticated trainers is a competent approach for the EP simulation. Besides, it was observed that the error of prediction by the ANN-SCE and ANN-EFO is 6.02 and 9.27% lower than the single ANN, respectively. Therefore, the used strategies can enhance the applicability of the ANN. The time elapsed in the optimization using SCE and EFO was 479.0 and 281.9 s, respectively. A comparison between these algorithms revealed that the EFO is both a faster and more accurate optimizer. The ANN-EFO is accordingly recommended as a new efficient model for predicting the EP.
      PubDate: 2021-01-23
       
  • Axial magnetic field effect on wave propagation in bi-layer FG graphene
           platelet-reinforced nanobeams
    • Abstract: Abstract This paper aims to investigate the size-dependent wave propagation in functionally graded (FG) graphene platelet (GPL)-reinforced composite bi-layer nanobeams embedded in Pasternak elastic foundation and exposed to in-plane compressive mechanical load and in-plane magnetic field. The small-scale effects are taken into account by employing the nonlocal strain gradient theory that contains two different length scale parameters. The present two nanobeams are made of multi-composite layers. Each layer is composed of a polymer matrix reinforced by uniformly distributed and randomly oriented GPLs. The GPLs weight fraction is graded from layer to other according to a new piece-wise rule and then four distribution types will be established. Our technique depends on applying the four-variable shear and normal deformations theory to model the wave propagation problem. The equations of motion are obtained using Hamilton principle. These equations are then analytically solved to obtain the wave frequencies and phase velocities of the waves. The calculated results are compared with those published in the literature. The impacts of the length scale parameters, foundation stiffness, in-plane magnetic field, weight fraction of graphene, graphene platelets distribution type and beam geometry on the propagating waves in the FG GPLs nanobeams are discussed in details. It is found that the strength of the composite beams may be enhanced with increasing in the GPLs weight fraction and magnetic field leading to an increment in the phase velocity and wave frequency of the present system.
      PubDate: 2021-01-23
       
  • Parallel graph-grammar-based algorithm for the longest-edge refinement of
           triangular meshes and the pollution simulations in Lesser Poland area
    • Abstract: Abstract In this paper, we propose parallel graph-grammar-based algorithm for the longest-edge refinements and the pollution simulations in Lesser Poland area. We introduce graph-grammar productions for Rivara’s longest-edged algorithm for the local refinement of unstructured triangular meshes. We utilize the hyper-graph to represent the computational mesh and the graph-grammar productions to express the longest-edge mesh refinement algorithm. The parallelism in the original Rivara’s longest edge refinement algorithm is obtained by processing different longest edge refinement paths in different three ads. Our graph-grammar-based algorithm allows for additional parallelization within a single longest-edge refinement path. The graph-grammar-based algorithm automatically guarantees the validity and conformity of the generated mesh; it prevents the generation of duplicated nodes and edges, elongated elements with Jacobians converging to zero, and removes all the hanging nodes automatically from the mesh. We test the algorithm on generating a surface mesh based on a topographic data of Lesser Poland area. The graph-grammar productions also generate the layers of prismatic three-dimensional elements on top of the triangular mesh, and they break each prismatic element into three tetrahedral elements. Next, we propose graph-grammar productions generating element matrices and right-hand-side vectors for each tetrahedral element. We utilize the Streamline Upwind Petrov–Galerkin (SUPG) stabilization for the pollution propagation simulations in Lesser Poland area. We use the advection–diffusion-reaction model, the Crank–Nicolson time integration scheme, and the graph-grammar-based interface to the GMRES solver.
      PubDate: 2021-01-22
       
  • The dynamic behavior of fractal-like tubes with Sierpinski hierarchy under
           axial loading
    • Abstract: Abstract To improve the energy absorption characteristics of regular triangular tubes, a novel thin-walled structure named as Sierpinski hierarchical triangular (SHT) tube is proposed by connecting the midpoint of each side of regular triangular tube. Finite-element (FE) model established by LSDYNA is developed to explore the mechanical behavior of SHT tube under axial loading, and the numerical models are validated by test results. Numerical simulations show that the dominant deformation mechanisms of SHT structure are in-extensional and extensional folding element. Based on Simplified Super Folding Element (SSFE) theory, mean crushing force of SHT tubes is deduced subsequently. The results show that the introduction of Sierpinski hierarchy significantly enhance energy absorption capacity. The mean crushing force of 1st, 2nd, and 3rd SHT tubes tend to increase by 70.5%, 113.2%, and 150.1% compared with the single-cell triangular (ST) tubes under the same relative density, respectively. Therefore, these studies encourage designers to introduce Sierpinski hierarchy in potential applications of thin-walled structures as energy absorption equipment.
      PubDate: 2021-01-22
       
  • Slope stability evaluation using neural network optimized by equilibrium
           optimization and vortex search algorithm
    • Abstract: Abstract A dependable evaluation of the stability of slopes is a prerequisite in many construction projects. Although machine learning models have been satisfactorily used for this purpose, combining them with metaheuristic optimizers has resulted in a larger accuracy. This study, therefore, suggests the use of equilibrium optimization (EO) and vortex search algorithm (VSA) for optimizing a multi-layer perceptron neural network (MLPNN) employed to anticipate the factor of safety of a single-layer soil slope. Two hybrid models, as well as the regular MLPNN, are fed by a total of 630 data acquired from finite element simulations. The results, first, showed the applicability of artificial intelligence in this field. Next, reducing the training root mean square error (RMSE) of the MLPNN (from 0.4715 to 0.3891 and 0.4383 by the EO and VSA, respectively) revealed the efficiency of the used algorithms in remedying the computational weaknesses of this model. Moreover, the testing RMSE declined from 0.5397 to 0.4129 and 0.5155, which indicates a higher generalization ability of the hybrid models. Furthermore, due to the larger accuracy of the EO-based ensemble, this algorithm outperformed the VSA in optimizing the MLPNN.
      PubDate: 2021-01-21
       
  • Prediction of long-term deflections of reinforced-concrete members using a
           novel swarm optimized extreme gradient boosting machine
    • Abstract: Abstract During the life cycle of buildings and infrastructure systems, the deflection of reinforced-concrete members generally increases due to both internal and external factors. Accurate forecasting of long-term deflection of these members can significantly enhance the effectiveness of structural maintenance processes. This research develops a hybrid data-driven method which employs the extreme gradient boosting machine and the particle swarm optimization metaheuristic for predicting long-term deflections of reinforced-concrete members. The former, a machine learning technique, generalizes a non-linear mapping function that helps to infer long-term deflection results from the input data. The later, a swarm-based metaheuristic, aims at optimizing the machine learning model by fine-tuning its hyper-parameters. The proposed hybridization of machine learning and swarm intelligence is constructed and verified by a dataset consisting of 217 experiments. The experiment results, supported by statistical tests, point out that the hybrid framework is able to attain good predictive performances with average root-mean-square error of 11.38 (a reduction of 17.4%), and average coefficient of determination of 0.88 (an increase of 6.0%) compared to the non-hybrid model. These results also outperform those obtained by other popular techniques, including Backpropagation Neural Networks and Regression Tree in several popular benchmarks, such as root-mean-square error, mean absolute percentage error, and the coefficient of determination R2. This is backed up by statistical tests with the level of significance \(\alpha = 0.05\) . Therefore, the newly developed model can be a promising tool to assist civil engineers in forecasting deflections of reinforced-concrete members.
      PubDate: 2021-01-21
       
  • Multi-extremum-modified response basis model for nonlinear response
           prediction of dynamic turbine blisk
    • Abstract: Abstract For the nonlinear dynamic analyses of complex mechanical components, it is necessary to apply efficient modeling framework to reduce computational burden. The accurate surrogate model for approximating the nonlinear responses of several failures is a vital issue to provide robust and safe design conditions in complex engineering applications. In this paper, two different Modified multi-extremum Response Surface basis Models (MRSM) are proposed for dynamic nonlinear responses of failure capacities for turbine blisk responses. The proposed MRSM is established using two regression processes including regressed the input variables by linear or exponential basis functions in first calibrating phase and regressed the second-order polynomial basis function using inputs data provided by first stage in second calibrating procedure. A sensitivity analysis using MRSM is proposed to consider the variation of input variables on the nonlinear responses. In the sensitivity analysis procedure, the effects of input variables are evaluated using the calibrating results given from the first regressed process. To evaluate the performance of the proposed MRSM, three multi-extremum failure modes including radial deformation of compressor blisk, maximum strain, and stress of compressor blade and disk are considered. the prediction of MRSM of nonlinear responses for Thermal-fluid–structure system with dynamical nonlinear finite-element analyses is compared with response surface method (RSM) and artificial neural network (ANN). The predicted results of modeling approaches showed that the sensitivity analysis based on MRSM accurately provided the effective degree for input variables. The gas temperature has the highest effects on nonlinear responses of turbine blisk which is followed by angular speed and material density. The MRSM combined with basic exponential function performs better than other models, while the MRSM coupled with linear function is more accurate than ANN and RSM. The proposed MRSM models have illustrated the accurate and efficient framework for approximating dynamic structural analysis of complex components.
      PubDate: 2021-01-21
       
  • Development of an integrated game theory-optimization subground
           stratification model using cone penetration test (CPT) measurements
    • Abstract: Abstract The continuous cone penetration test (CPT) measurements provide an advantageous liable rapid tool for stratification and soil behavior classification that can be employed in the sustainable design of the infrastructures. However, the CPT measurements are often interpreted by geotechnical experts because of the involved complexities and uncertainties. In this study, a novel stratification and soil type behavior (SBT) classification model is developed to identify the transition and thicker layers by integrating the geotechnical knowledge with the three submodels of (a) locally estimated scatterplot smoothing (LOESS), (b) a game theory model known as Nash–Harsanyi (N–H) bargaining, and (c) grey wolf optimizer (GWO). The LOESS and integrated N–H bargaining-GWO models are, respectively, used to approximate the outliers in CPT measurements and identify the SBT and layer changes. Attractively, in the proposed model, the engineer has the opportunity to judge on the precision of the stratification profile regarding their own preferences in a project. Solving simple algebraic equations, high speed, identifying thick and the interlayer transition layers, and small required training data are the other advantages of the developed model. Finally, the applicability of the proposed model has been assessed in an example. The compared estimated and two other models’ stratification profiles highlighted the potential of the proposed model to identify thin transition layers.
      PubDate: 2021-01-21
       
  • Prediction of back break in blasting using random decision trees
    • Abstract: Abstract Back break is an unsolicited phenomenon caused due to rock condition, blast geometry, explosive and initiation system in mines. It does not help in creating a smooth high wall and free face for next blasting due to cracks, overhang and under-hang. It can cause rockfall during drilling due to the cracks present in the in situ rock mass at the perimeter. Due to improper free face created from the previous blast and the presence of loose strata in the face increases the overall cost of production. Therefore, predicting and subsequently optimising back break shall reduce their problems to some extent. In this paper, an attempt is made to predict back break using the random forest method. The variables used for the study was such as burden to spacing ratio, stemming to hole-depth ratio, p-wave velocity and the density of explosive. For the random forest model, R2 0.9791 and RMSE 0.87899 and for linear regression was R2 was 0.824 and root mean square error (RMSE) 0.72, respectively. From the field trials, it was evident that the use of low-density emulsion can help in reducing the back break and optimise the overall cost of the blasting process. The same results were validated using Random forest method wherein the model R2 was 0.9791 and RMSE was 0.8799.
      PubDate: 2021-01-20
       
  • An effective approach for reliability-based robust design optimization of
           uncertain powertrain mounting systems involving imprecise information
    • Abstract: Abstract The uncertain parameters of automotive powertrain mounting systems (PMSs) may involve imprecise information (e.g., incomplete, different and conflicting information) in engineering practice. An effective approach is proposed for the reliability-based robust design optimization (RBRDO) of uncertain PMSs involving imprecise information. In the proposed approach, the imprecise information of uncertain parameters is firstly addressed and combined based on evidence theory, and the uncertain parameters are treated as evidence variables. Then, an uncertainty analysis method named evidence perturbation-central difference method (EPCDM) is derived to fast estimate the mean intervals, standard deviation intervals, and the belief and plausibility measures related to system inherent characteristics. A reference method named evidence-Monte Carlo method (EMCM) is developed to verify the effectiveness of EPCDM. Next, to conduct robustness design, the weighted sum of the lower bounds of means and the upper bounds of standard deviations of system inherent characteristics are taken to construct optimization objective; while to perform reliability design, the belief measures related to system inherent characteristics are used to create reliability constraints. Afterwards, a nested RBRDO model is established to explore the optimum design of the PMS, which considers both reliability and robustness simultaneously. The nested PBRDO can be effectively simplified based on EPCDM. The effectiveness of the proposed approach is finally demonstrated by the application example.
      PubDate: 2021-01-20
       
  • A three-level linearized high-order accuracy difference scheme for the
           extended Fisher–Kolmogorov equation
    • Abstract: Abstract A three-level linearized difference scheme for the extended Fisher–Kolmogorov equation is considered. It is proved that the proposed difference scheme is uniquely solvable and unconditionally convergent. The convergence order in maximum norm is \(O(h^4+k^2)\) , where k and h are the temporal and spatial grid sizes, respectively. The accurateness and effectiveness of the method are tested by taking various examples. The numerical results of the method are compared with the exact solutions and also compared with earlier published results. It is found that the proposed method produces more accurate results than the others available in the literature.
      PubDate: 2021-01-20
       
  • Improved Levenberg–Marquardt backpropagation neural network by particle
           swarm and whale optimization algorithms to predict the deflection of RC
           beams
    • Abstract: Abstract The aim of this study is to develop a novel computer-aided method for the prediction of the deflection of reinforced concrete beams (DRCB) under concentrated loads. To this end, in the present work, a Levenberg–Marquardt-based backpropagation novel neural network model, optimized by the whale optimization algorithm (WOA), called WOA-LMBPNN, has been developed. Specifically, a neural network, using the Levenberg–Marquardt backpropagation training algorithm with multiple hidden layers, was optimized by the WOA, aiming to obtain higher accuracy in predicting DRCB. For the training of the models, 120 experiments with the geometrical and mechanical properties of concrete beams were compiled using were used as the input parameters. Seven datasets with different number of input variables were investigated to evaluate the effect of the input variables on DRCB. For comparison purposes, another swarm optimization algorithm (i.e., particle swarm optimization—PSO) was also used to optimize the LMBPNN model (i.e., PSO-LMBPNN model). The results obtained by the PSO-LMBPNN and WOA-LMBPNN models are then compared based on the different datasets. Finally, the results revealed the effective role of the WOA, as well as the efficiency and robustness of the new hybrid WOA-LMBPNN model in predicting DRCB.
      PubDate: 2021-01-20
       
  • Analyzing three-dimensional wave propagation with the hybrid reproducing
           kernel particle method based on the dimension splitting method
    • Abstract: Abstract By introducing the dimension splitting method into the reproducing kernel particle method (RKPM), a hybrid reproducing kernel particle method (HRKPM) for solving three-dimensional (3D) wave propagation problems is presented in this paper. Compared with the RKPM of 3D problems, the HRKPM needs only solving a set of two-dimensional (2D) problems in some subdomains, rather than solving a 3D problem in the 3D problem domain. The shape functions of 2D problems are much simpler than those of 3D problems, which results in that the HRKPM can save the CPU time greatly. Four numerical examples are selected to verify the validity and advantages of the proposed method. In addition, the error analysis and convergence of the proposed method are investigated. From the numerical results we can know that the HRKPM has higher computational efficiency than the RKPM and the element-free Galerkin method.
      PubDate: 2021-01-19
       
  • An orthogonal opposition-based-learning Yin–Yang-pair optimization
           algorithm for engineering optimization
    • Abstract: Abstract Yin–Yang-pair Optimization (YYPO) is a recently developed philosophy-inspired meta-heuristic algorithm, which works with two main points for exploitation and exploration, respectively, and then generates more points via splitting to search the global optimum. However, it suffers from low quality of candidate solutions in its exploration process owing to the lack of elitism. Inspired by this, a new modified algorithm named orthogonal opposition-based-learning Yin–Yang-pair Optimization (OOYO) is proposed to enhance the performance of YYPO. First, the OOYO retains the normalization operation in YYPO and starts with a single point to exploit. A set of opposite points is designed by a method of opposition-based learning with split points generated from the current optimum for exploration. Then, the points, i.e., candidate solutions, are constructed by the randomly selected split point and opposite points through the idea of orthogonal experiment design to make full use of information from the space. The proposed OOYO does not add additional time complexity and eliminates a user-defined parameter in YYPO, which facilitates parameter adjustment. The novel orthogonal opposition-based learning strategy can provide inspirations for the improvement of other optimization algorithms. Extensive test functions containing a classic test suite of 23 standard benchmark functions and 2 test suites of Swarm Intelligence Symposium 2005 and Congress on Evolutionary Computation 2020 from Institute of Electrical and Electronics Engineers are employed to evaluate the proposed algorithm. Non-parametric statistical results demonstrate that OOYO outperforms YYPO and furnishes strong competitiveness compared with other state-of-the-art algorithms. In addition, we apply OOYO to solve four well-known constrained engineering problems and a practical problem of parameters optimization in a rainstorm intensity model.
      PubDate: 2021-01-19
       
  • A novel systematic and evolved approach based on XGBoost-firefly algorithm
           to predict Young’s modulus and unconfined compressive strength of rock
    • Abstract: Abstract To design the tunnel excavations, the most important parameters are the engineering properties of rock, e.g., Young’s modulus (E) and unconfined compressive strength (UCS). Numerous researchers have attempted to propose methods to estimate E and UCS indirectly. This task is complex due to the difficulty of preparing and carrying out such experiments in a laboratory. The main aim of the present study is to propose a new and efficient machine learning model to predict E and UCS. The proposed model combines the extreme gradient boosting machine (XGBoost) with the firefly algorithm (FA), called the XGBoost-FA model. To verify the feasibility of the XGBoost-FA model, a support vector machine (SVM), classical XGBoost, and radial basis function neural network (RBFN) were also employed. Forty-five granite sample sets, collected from the Pahang-Selangor tunnel, Malaysia, were used in the modeling. Several statistical functions, such as coefficient of determination (R2), mean absolute percentage error (MAPE) and root mean square error (RMSE) were calculated to check the acceptability of the methods mentioned above. A review of the results of the proposed models revealed that the XGBoost-FA was more feasible than the others in predicting both E and UCS and could generalize.
      PubDate: 2021-01-16
       
  • Lyapunov–Sylvester computational method for numerical solutions of a
           mixed cubic-superlinear Schrödinger system
    • Abstract: Abstract In this paper a nonlinear coupled Schrödinger system in the presence of mixed cubic and superlinear power laws is considered. A non standard numerical method is developed to approximate the solutions in higher dimensional case. The idea consists in transforming the continuous system into an algebraic quasi linear dynamical discrete one leading to generalized semi-linear operators. Next, the discrete algebraic system is studied for solvability, stability and convergence. At the final step, numerical examples are provided to illustrate the efficiency of the theoretical results.
      PubDate: 2021-01-16
       
  • Dynamic simulation of moderately thick annular system coupled with shape
           memory alloy and multi-phase nanocomposite face sheets
    • Abstract: Abstract The current research work analyzes dynamics of a sandwich disk which is gently thick. The mentioned sandwich structure has honeycomb core, a couple of middle layers having fibers of shape memory alloy (SMA), and a couple of external layers of multi-scaled hybrid nanocomposite (MHC) considering in-plane force. The core in the shape of honeycomb is manufactured of aluminum due to its high stiffness and less density compared with other materials. Applying energy methods called the principle of Hamilton, we obtained governing motion equations of the mentioned structure and solved them using First-order shear-deformation-theory (FSDT), as well as generalized-differential-quadrature-method (GDQM), respectively. To layers’ joint, the compatibility equations have been taken into account. Then, a parametric mathematical manipulation has been conducted to analyze the impacts of fibers of SMA, boundary conditions (BCs), internal loads, honeycomb network angle, ratio of external to internal radiuses, ratio of thickness to length of the honeycomb, weight fraction of CNTs, angle of fibers, ratio of honeycomb to face-sheet thickness on the frequency of the multi-phase sandwich disk. The outcomes derived reveal that for any amount of internal pressure and each BCs, the relation of the honeycomb’s thickness ratios to MHC layer ( \({h}_{H}/{h}_{t}\) ) and sandwich structure’s frequency is similar to quadratic function. Further results show that the effects of the fibers’ angle on the frequency can be ignored for larger \({h}_{H}/{h}_{t}\) amounts.
      PubDate: 2021-01-13
       
  • Trefftz-unsymmetric finite element for bending analysis of orthotropic
           plates
    • Abstract: Abstract This work develops a new four-node quadrilateral displacement-based Trefftz-type plate element for bending analysis of orthotropic plates within the framework of the unsymmetric finite element method (FEM). In the present formulation, the modified isoparametric interpolations are employed to formulate the element’s test functions in which the deflection is effectively enriched by the nodal rotation degrees of freedom (DOFs). Meanwhile, the element’s trial functions are determined based on the Trefftz functions that can a prior satisfy the governing equations of orthotropic Mindlin–Reissner plates. Numerical benchmark tests reveal that the new unsymmetric plate element is free of shear locking problem and can produce satisfactory results for both the displacement and stress resultant. In particular, it exhibits quite good tolerances to the gross mesh distortion.
      PubDate: 2021-01-12
       
  • A novel truly explicit time-marching procedure for simple and effective
           analyses of wave propagation models
    • Abstract: Abstract In this paper, a novel explicit time-marching procedure is proposed for wave propagation analysis. The new method is extremely simple to implement and highly effective, providing a very attractive formulation. It considers staggered forward and backward finite difference expressions to approximate the derivative fields of the model, as well as it introduces adaptive corrections into the computations, improving the accuracy and the stability of the analysis. The novel approach is truly explicit (all force terms are treated explicitly), truly self-starting, and it enables adaptive algorithm dissipation. In fact, the proposed technique stands as a single-step approach that adapts itself (taking into account a highly straightforward algorithm) according to the computed responses, the physical properties of the model and the adopted temporal and spatial discretizations. Numerical results are presented at the end of the paper, illustrating the excellent performance of the novel formulation, considering different (linear and nonlinear) wave propagation models.
      PubDate: 2021-01-11
       
  • Fuzzy Shannon wavelet finite element methodology of coupled heat transfer
           analysis for clearance leakage flow of single screw compressor
    • Abstract: Abstract The Shannon wavelet function and its scale function are used as interpolating functions to establish the Shannon wavelet finite element. The temperature and velocity of every leakage path are calculated based on fuzzy Shannon wavelet finite element method, fuzzy Daubechies wavelet finite element model, fuzzy finite element method and experiment, and comparisons between numerical analysis results and experimental results show that fuzzy Shannon-cosine wavelet finite element method can get highest computing precision and accuracy. The coupling relationships between the flow and heat transfer of clearance leakage flow are analyzed based on fuzzy Shannon-cosine finite element method, and numerical results show that the Nusselt number of every leakage path decreases with increase of Mach number, the flow has great influence on heat transfer of clearance leakage flow of single screw compressor.
      PubDate: 2021-01-11
       
 
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