scholarly journals Design of Foundations Against Differential Settlement

2021 ◽  
Author(s):  
Farzaneh Naghibi ◽  
Gordon A. Fenton
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Limit State ◽  
Differential Settlement ◽  
Angular Distortion ◽  
Equivalent Stiffness ◽  
Distribution Of The Maximum ◽  
Bias Factor ◽  
Total Settlement ◽  
Design Requirements

The serviceability limit state (SLS) design of foundations typically proceeds by limiting the total settlement of individual foundations and thereby attempting to restrict the differential settlement between pairs of foundations. Due to the uncertain nature of the supporting ground, the magnitude of settlement and differential settlement are random. As it is often the differential settlement which governs serviceability, it is desirable to provide design requirements which suitably restrict differential settlements. This paper investigates, by Monte Carlo simulation, the distribution of the maximum differential settlement between pairs of foundations as a function of the spacing between foundations and the number of foundations – groups of 4, 9, or 16 foundations, arranged on a grid, are considered. The effects of the correlations between the equivalent stiffness of the ground under each foundation, as well as between the loads applied to the foundations, on the distribution of the maximum differential settlements and angular distortions are investigated. Ratios of resistance factor to resistance bias factor are presented that can be used to calibrate design requirements on the total settlement of individual foundations which also simultaneously achieve acceptable performance with respect to angular distortion.

Circular Arc Gear Reliability Study Based on Modified FOSM Method

2013 ◽  
Vol 760-762 ◽  
pp. 2216-2219
Author(s):  
Zhong Li ◽  
Bo Yu Cheng
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Limit State ◽  
Point Method ◽  
Reliability Study ◽  
Circular Arc ◽  
Gear Case ◽  
Improve Reliability ◽  
State Functions

As different limit state functions are used to analyze reliability, there is a great distinctness among the calculated results. In this paper an improved LOSM method is proposed, namely, checking point method. The circular arc gear case is employed to demonstrate this method. In contrast to the results of Monte Carlo simulation, this method can greatly improve reliability calculations precision.

scholarly journals Non-deterministic Approach for Reliability Evaluation of Steel Portal Frame

2019 ◽  
Vol 5 (8) ◽  
pp. 1684-1697
Author(s):  
Hawraa Qasim Jebur ◽  
Salah Rohaima Al-Zaidee
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Structural Reliability ◽  
Limit State ◽  
Computational Cost ◽  
Probability Of Failure ◽  
Ultimate Limit State ◽  
Axial Forces ◽  
Portal Frame ◽  
Ultimate Limit

In recent years, more researches on structural reliability theory and methods have been carried out. In this study, a portal steel frame is considered. The reliability analysis for the frame is represented by the probability of failure, P_f, and the reliability index, β, that can be predicted based on the failure of the girders and columns. The probability of failure can be estimated dependent on the probability density function of two random variables, namely Capacity R, and Demand Q. The Monte Carlo simulation approach has been employed to consider the uncertainty the parameters of R, and Q. Matlab functions have been adopted to generate pseudo-random number for considered parameters. Although the Monte Carlo method is active and is widely used in reliability research, it has a disadvantage which represented by the requirement of large sample sizes to estimate the small probabilities of failure. This is leading to computational cost and time. Therefore, an Approximated Monte Carlo simulation method has been adopted for this issue. In this study, four performances have been considered include the serviceability deflection limit state, ultimate limit state for girder, ultimate limit state for the columns, and elastic stability. As the portal frame is a statically indeterminate structure, therefore bending moments, and axial forces cannot be determined based on static alone. A finite element parametric model has been prepared using Abaqus to deal with this aspect. The statistical analysis for the results samples show that all response data have lognormal distribution except of elastic critical buckling load which has a normal distribution.

A polynomial chaos meta‐model for non‐linear stochastic magnet variations

2013 ◽  
Vol 32 (4) ◽  
pp. 1211-1218 ◽  
Author(s):  
Peter Offermann ◽  
Kay Hameyer
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Flux Density ◽  
Polynomial Chaos ◽  
Epistemic Uncertainty ◽  
Limit State ◽  
Element Model ◽  
State Function ◽  
Content Type ◽  
Meta Model

PurposeDue to the production process, arc segment magnets with radial magnetization for surface‐mounted permanent‐magnet synchronous machines (PMSM) can exhibit a deviation from the intended ideal, radial directed magnetization. In such cases, the resulting air gap field may show spatial variations in angle and absolute value of the flux‐density. For this purpose, this paper aims to create and evaluate a stochastic magnet model.Design/methodology/approachIn this paper, a polynomial chaos meta‐model approach, extracted from a finite element model, is compared to a direct sampling approach. Both approaches are evaluated using Monte‐Carlo simulation for the calculation of the flux‐density above one sole magnet surface.FindingsThe used approach allows representing the flux‐density's variations in terms of the magnet's stochastic input variations, which is not possible with pure Monte‐Carlo simulation. Furthermore, the resulting polynomial‐chaos meta‐model can be used to accelerate the calculation of error probabilities for a given limit state function by a factor of ten.Research limitations/implicationsDue to epistemic uncertainty magnet variations are assumed to be purely Gaussian distributed.Originality/valueThe comparison of both approaches verifies the assumption that the polynomial chaos meta‐model of the magnets will be applicable for a complete machine simulation.

Comparison of Monte Carlo Simulation and Response Surface Method by Using ANSYS PDS

2014 ◽  
Vol 578-579 ◽  
pp. 1449-1453
Author(s):  
Chun Xue Song ◽  
Yi Zhang ◽  
Ying Yi Cao
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Probabilistic Analysis ◽  
Limit State ◽  
Design System ◽  
State Function ◽  
Surface Method

Monte Carlo Simulation and Response Surface Method are two very powerful reliability analysis methods. Normally, in the reliability analysis of complex structures, the limit state function often can not be expressed in a closed-form. Usually, the codes for probabilistic analysis need to be combined with finite element models. ANSYS Probabilistic Design System (PDS) has provided a package to conduct probabilistic analysis automatically. This paper is going to compare the performance of these methods through an easy engineering problem in ANSYS. The results are going to be derived to show the feature of applying the corresponding reliability methods.

Local Monte Carlo Simulation for the Reliability Sensitivity Analysis

2011 ◽  
Vol 291-294 ◽  
pp. 2183-2188 ◽  
Author(s):  
Da Wei Li ◽  
Zhen Zhou Lu ◽  
Zhang Chun Tang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Numerical Technique ◽  
Limit State ◽  
Computational Cost ◽  
Simulation Method ◽  
State Function ◽  
Local Domain ◽  
Monte Carlo Simulation Method ◽  
Reliability Sensitivity

An efficient numerical technique, namely the Local Monte Carlo Simulation method, is presented to assess the reliability sensitivity in this paper. Firstly some samples are obtained by the random sampling, then the local domain with a constant probability content corresponding to each sample point can be defined, finally the conditional reliability and reliability sensitivity corresponding to every local region can be calculated by using linear approximation of the limit state function. The reliability and reliability sensitivity can be estimated by the expectation of all the conditional reliability and reliability sensitivity. Three examples testify the applicability, validity and accuracy of the proposed method. The results computed by the Local Monte Carlo Simulation method and the Monte Carlo method are compared, which demonstrates that, without losing precision, the computational cost by the former method is much less than the later.

scholarly journals Technology Identification, Evaluation, Selection, and Optimization of a HALE Solar Aircraft

2020 ◽  
Vol 10 (21) ◽  
pp. 7593
Author(s):  
Ju-Yeol Yun ◽  
Ho-Yon Hwang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Regression Equations ◽  
Design Objective ◽  
Design Variables ◽  
Objective Parameter ◽  
Identification Evaluation ◽  
Design Requirements ◽  
Technology Identification ◽  
Independent Design

In this paper, sensitivity analysis and optimization of a high altitude long endurance (HALE) solar aircraft was implemented. Zephyr S was referred to for the aircraft conference configuration, and OpenVSP and XFLR5 were employed to create configuration and perform aerodynamic analysis. In the conceptual design stage of the HALE solar aircraft, technology identification, evaluation, and selection (TIES) methodology was employed. According to the design requirements, problem definition was established, and design goal, variations, and targeted values were set up to implement independent design variables to meet the design requirements. Based on the design of experiments (DOE), modeling of the relationship between design objective parameters and independent design values was implemented. The independent design variables with the largest influence were selected in the screening test. By employing the selected independent design variables, regression equations and sensitivity profiles were produced through response surface method. Inter-factor relationship was easily analyzed through the sensitivity profile. Regression equations were employed in the Monte Carlo simulation to draw design objective parameter values for 10,000 combinations of independent design variables. As a result of the Monte Carlo simulation, the design feasibility of design objective parameters was assessed. Optimization was performed using the desirability function of JMP software, and constraints were applied to each design objective parameter to derive the optimum values of independent design variables. Then, the values of optimized design independent variables were applied to the solar aircraft design framework and analyzed for the endurance flight performance. By comparing the endurance of the optimized configuration with the reference configuration, it was confirmed that the endurance could be improved by using the methodology proposed in this study.

Efficient Reliability Analysis of Structural Systems With a High Number of Limit States

2010 ◽  
Author(s):  
Arvid Naess ◽  
Bernt J. Leira ◽  
Olexandr Batsevych
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Limit State ◽  
Computational Cost ◽  
Estimation Method ◽  
Structural Systems ◽  
Approximation Procedure ◽  
Tail Probabilities ◽  
Critical Limit ◽  
Failure Probabilities

In principle, the reliability of complicated structural systems can be accurately predicted by standard Monte Carlo simulation methods, but the computational burden may be prohibitive. A new Monte Carlo based method for estimating system reliability that aims at reducing the computational cost is therefore proposed. It exploits the regularity of tail probabilities to set up an approximation procedure for the prediction of the far tail failure probabilities based on the estimates of the failure probabilities obtained by Monte Carlo simulation at more moderate levels. In the paper the usefulness and accuracy of the estimation method is illustrated by application to some particular examples of structures with several thousand potentially critical limit state functions. The effect of varying the correlation of the load components is also investigated.

Reliability Analysis With Monte Carlo Simulation and Dependent Kriging Predictions

2016 ◽  
Vol 138 (12) ◽  
Author(s):  
Zhifu Zhu ◽  
Xiaoping Du
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
Surrogate Model ◽  
Limit State ◽  
Convergence Criterion ◽  
State Function ◽  
Kriging Method ◽  
Learning Function ◽  
Highly Nonlinear

Reliability analysis is time consuming, and high efficiency could be maintained through the integration of the Kriging method and Monte Carlo simulation (MCS). This Kriging-based MCS reduces the computational cost by building a surrogate model to replace the original limit-state function through MCS. The objective of this research is to further improve the efficiency of reliability analysis with a new strategy for building the surrogate model. The major approach used in this research is to refine (update) the surrogate model by accounting for the full information available from the Kriging method. The existing Kriging-based MCS uses only partial information. Higher efficiency is achieved by the following strategies: (1) a new formulation defined by the expectation of the probability of failure at all the MCS sample points, (2) the use of a new learning function to choose training points (TPs). The learning function accounts for dependencies between Kriging predictions at all the MCS samples, thereby resulting in more effective TPs, and (3) the employment of a new convergence criterion. The new method is suitable for highly nonlinear limit-state functions for which the traditional first- and second-order reliability methods (FORM and SORM) are not accurate. Its performance is compared with that of existing Kriging-based MCS method through five examples.

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