Yen Liang Tung¹, Zubair Ahmad^2,*, G. G. Hamedani³

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 113-126, 2021, DOI:10.32604/cmc.2020.012780

Abstract Proposing new statistical distributions which are more flexible than the existing distributions have become a recent trend in the practice of distribution theory. Actuaries often search for new and appropriate statistical models to address data related to financial and risk management problems. In the present study, an extension of the Lomax distribution is proposed via using the approach of the weighted T-X family of distributions. The mathematical properties along with the characterization of the new model via truncated moments are derived. The model parameters are estimated via a prominent approach called the maximum likelihood estimation method. A brief Monte Carlo… More >

Baoyin Sun^{1, 2}, Yantai Zhang³, Caigui Huang^{4, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 755-776, 2020, DOI:10.32604/cmes.2020.09632

Abstract Steel frames equipped with buckling restrained braces (BRBs) have been increasingly applied in earthquake-prone areas given their excellent capacity for resisting lateral forces. Therefore, special attention has been paid to the seismic risk assessment (SRA) of such structures, e.g., seismic fragility analysis. Conventional approaches, e.g., nonlinear finite element simulation (NFES), are computationally inefficient for SRA analysis particularly for large-scale steel BRB frame structures. In this study, a machine learning (ML)- based seismic fragility analysis framework is established to effectively assess the risk to structures under seismic loading conditions. An optimal artificial neural network model can be trained using calculated damage… More >

Fan Yang^1,2,3,*, Jianwei Ren^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 801-814, 2020, DOI:10.32604/cmes.2020.08889

Abstract Based on the rapid simulation of Markov Chain on samples in failure region, a novel method of reliability analysis combining Monte Carlo Markov Chain (MCMC) with random forest algorithm was proposed. Firstly, a series of samples distributing around limit state function are generated by MCMC. Then, the samples were taken as training data to establish the random forest model. Afterwards, Monte Carlo simulation was used to evaluate the failure probability. Finally, examples demonstrate the proposed method possesses higher computational efficiency and accuracy. More >

G. N. Baltas^{1, *}, F. A. Prieto¹, M. Frantzi², C. R. Garcia-Alonso¹, P. Rodriguez^{1, 3}

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1343-1357, 2020, DOI:10.32604/cmc.2020.011243

Abstract During the late months of last year, a novel coronavirus was detected in Hubei, China. The virus, since then, has spread all across the globe forcing Word Health Organization (WHO) to declare COVID-19 outbreak a pandemic. In Spain, the virus started infecting the country slowly until rapid growth of infected people occurred in Madrid, Barcelona and other major cities. The government in an attempt to stop the rapssid spread of the virus and ensure that health system will not reach its capacity, implement strict measures by putting the entire country in quarantine. The duration of these measures, depends on the… More >

P. Horst¹

Structural Durability & Health Monitoring, Vol.1, No.1, pp. 49-66, 2005, DOI:10.3970/sdhm.2005.001.049

Abstract The paper presents a Monte Carlo Simulation method for the assessment of Multiple Site Damage (MSD) and a subsequent attempt to find a way to interpret intermediate results of the Monte Carlo Simulation with respect to the criticality of scenarios. The basic deterministic part of the model is based on the compounding method, which is used in order to gain an acceptable computational effort. Some examples illustrate features of MSD scenarios and this allows to check an approach for feature detection via Wavelet transforms. This Wavelet transform approach shows some positive results in the interpretation of MSD scenarios. More >

Kathleen Puskar¹, Leonard Apeltsin², Shlomo Ta’asan³, Russell Schwartz², Philip R. LeDuc⁴

Molecular & Cellular Biomechanics, Vol.1, No.2, pp. 123-132, 2004, DOI:10.3970/mcb.2004.001.123

Abstract Understanding the connection between mechanics and cell structure requires the exploration of the key molecular constituents responsible for cell shape and motility. One of these molecular bridges is the cytoskeleton, which is involved with intracellular organization and mechanotransduction. In order to examine the structure in cells, we have developed a computational technique that is able to probe the self-assembly of actin filaments through a lattice based Monte Carlo method. We have modeled the polymerization of these filaments based upon the interactions of globular actin through a probabilistic model encompassing both inert and active proteins. The results show similar response to… More >

James D.Turner, Manoranjan Majji, John L. Junkins

The International Conference on Computational & Experimental Engineering and Sciences, Vol.16, No.1, pp. 11-12, 2011, DOI:10.3970/icces.2011.016.011

Abstract Modeling uncertainty for nonlinear systems is often handled by developing a mathematical model, defining suitable parameters, establishing suitable initial conditions and numerically integrating the system response in order to study the behavior of the system. The potential range of behaviors that can be realized is assessed by varying the model parameters, integrating the response, and recording the changes in the system behaviors. In theory this process is straightforward for implementing. The only potential barrier to carrying out the repeated integrations of the system dynamics is the availability of powerful computer resources that can provide the density of sample points required… More >

Taicong Chen¹, Haitao Ma¹, Wei Gao²

CMES-Computer Modeling in Engineering & Sciences, Vol.87, No.3, pp. 239-270, 2012, DOI:10.3970/cmes.2012.087.239

Abstract Monte Carlo simulation method has been used extensively in probabilistic analyses of engineering systems and its popularity has been growing. While it is widely accepted that the simulation results are asymptotically accurate when the number of samples increases, certain exceptions do exist. The major objectives of this study are to reveal the conditions of the applicability of Monte Carlo method and to provide new insights into the accuracy of the simulation results in stochastic structural analysis. Firstly, a simple problem of a spring with random axial stiffness subject to a deterministic tension is investigated, using normal and lognormal distributions. Analytical… More >

C. R. A. Silva¹, H. P. Azikri de Deus¹, G.E. Mantovani², A.T. Beck³

CMES-Computer Modeling in Engineering & Sciences, Vol.67, No.2, pp. 119-150, 2010, DOI:10.3970/cmes.2010.067.119

Abstract In this paper, the Askey-Wiener scheme and the Galerkin method are used to obtain approximate solutions to stochastic beam bending on Winkler foundation. The study addresses Euler-Bernoulli beams with uncertainty in the bending stiffness modulus and in the stiffness of the foundation. Uncertainties are represented by parameterized stochastic processes. The random behavior of beam response is modeled using the Askey-Wiener scheme. One contribution of the paper is a sketch of proof of existence and uniqueness of the solution to problems involving fourth order operators applied to random fields. From the approximate Galerkin solution, expected value and variance of beam displacement… More >

B Radhika¹, S S P,a¹, C S Manohar^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.27, No.1&2, pp. 79-110, 2008, DOI:10.3970/cmes.2008.027.079

Abstract Reliability of nonlinear vibrating systems under stochastic excitations is investigated using a two-stage Monte Carlo simulation strategy. For systems with white noise excitation, the governing equations of motion are interpreted as a set of Ito stochastic differential equations. It is assumed that the probability distribution of the maximum in the steady state response belongs to the basin of attraction of one of the classical asymptotic extreme value distributions. The first stage of the solution strategy consists of selection of the form of the extreme value distribution based on hypothesis tests, and the next stage involves the estimation of parameters of… More >