scholarly journals A Flexible Bridge Rating Method Based on Analytical Evidential Reasoning and Monte Carlo Simulation

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Saleh Abu Dabous ◽  
Ghadeer Al-Khayyat
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Paired Comparison ◽  
Research Work ◽  
Condition Assessment ◽  
Evidential Reasoning ◽  
Future Research ◽  
Assessment Practices ◽  
Condition Rating ◽  
Rating Method

Several bridge inspection standards and condition assessment practices have been developed around the globe. Some practices employ four linguistic expressions to rate bridge elements while other practices use five or six, or adopt numerical ratings such as 1 to 9. This research introduces a condition rating method that can operate under different condition assessment practices and account for uncertainties in condition assessment by means of the Evidential Reasoning (ER) theory. The method offers flexibility in terms of using default elements and their weights or selecting alternative set of elements and condition rating schemes. The implemented ER approach accounts for uncertainties in condition rating by treating the condition assessments as probabilistic grades rather than numerical values. The ER approach requires the assignment of initial basic beliefs or probabilities, and typically these initial beliefs are assigned by an expert. Alternatively, this research integrates the Monte Carlo Simulation (MCS) technique with the ER theory to quantitatively estimate the basic probabilities and to produce robust overall bridge condition ratings. The proposed method is novel to the literature and has the following features: (1) flexible and can be used with any number of bridge elements and any standard of condition grades; (2) intuitive and simple paired comparison technique is implemented to evaluate weights of the bridge elements; (3) the MCS technique is integrated with the ER approach to quantify uncertainties associated with the stochastic nature of the bridge deterioration process; (4) the method can function with limited data and can incorporate new evidence to update the condition rating; (5) the final rating consists of multiple condition grades and is produced as a distributed probabilistic assessment reflecting the condition of the bridge elements collectively. The proposed method is illustrated with a real case study, and potential future research work is identified.

Estimating Weights of Evaluation Factors on Basis of Monte Carlo Simulation

2012 ◽  
Vol 268-270 ◽  
pp. 1735-1740
Author(s):  
Yan Fei Tian ◽  
Li Wen Huang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Process ◽  
Stochastic Simulation ◽  
Research Work ◽  
Simulation Method ◽  
Distribution Functions ◽  
Probability Process ◽  
Evaluation Work

Although the value of factor weight in an evaluation work is deterministic, the solving process is random, so connection between weight solution with digital characteristics or distribution functions of specific random variables or random process could be build. Using stochastic simulation method to get a lot of random solutions to the problem, expectation of the random solutions can be used as a estimation solution. On basis of idea of Monte Carlo simulation, this paper analyzed the probability process of calculating factor weight, and provided the procedures of estimating factor weight by means of Monte Carlo simulation. Through discussion and example in this paper, feasibility and validity of this method were proved, which may make foreshadowing for follow-up research work.

Analysis on Motion Precision Reliability of Hydraulic Support

2011 ◽  
Vol 48-49 ◽  
pp. 224-227
Author(s):  
Dong Chen Qin ◽  
Qiang Zhu ◽  
Hong Xia Wu ◽  
Zhe Feng Guo
Keyword(s):  
Mathematical Model ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Theoretical Calculation ◽  
Calculation Method ◽  
Effective Length ◽  
Future Research ◽  
Hydraulic Support ◽  
Continuous Contact ◽  
Continuous Contact Model

In order to research the motion precision reliability of hydraulic support when the influence of the bar length error and gap error is considered, the motion trace mathematical model for the top beam of hydraulic support is established, with the calculation method of motion precision reliability and the effective length of bar based on continuous contact model. Taking some type of hydraulic support as an example, its motion precision reliability is calculated and analyzed. The Monte Carlo simulation is also used to verify the model, and the T-R curve of the gap error and the reliability is plotted. The results from simulation accord with those from the theoretical calculation, which verifies the model established and can provide some valuable reference for the related future research.

Reliability Analysis of Uncertain Input Variables for Cracked Structures

2011 ◽  
Vol 52-54 ◽  
pp. 1358-1363 ◽  
Author(s):  
M.R.M. Akramin ◽  
A. Zulkifli ◽  
M. Mazwan Mahat
Keyword(s):  
Monte Carlo Simulation ◽  
Finite Element ◽  
Monte Carlo ◽  
Failure Probability ◽  
Probabilistic Analysis ◽  
Research Work ◽  
Adaptive Mesh ◽  
Element Analysis ◽  
Monte Carlo Simulation Technique ◽  
Cracked Structures

Probabilistic analysis aims at providing an assessment of cracked structures and taking relevant uncertainties into account in a rational quantitative manner. The main focus of this research work is on uncertainties aspect which relates with the nature of crack in materials. By using cracked structures modelling, finite element calculation, generation of adaptive mesh, sampling of cracked structure including uncertainties factors and probabilistic analysis using Monte Carlo method, the rigidity of cracked structures is estimated. Assessment of the accuracy in probabilistic structures is essential when limited amount of data is available. The hybrid finite element and probabilistic analysis represents the failure probability of the structures. The probability of failure caused by uncertainties relates to loads and material properties of the structure are estimated using Monte Carlo simulation technique. Numerical examples are presented to show probabilistic analysis based on Monte Carlo simulation provides accurate estimates of failure probability. The comparison shows that the combination between finite element analysis and probabilistic analysis provides a simple and realistic of quantify the failure probability.

scholarly journals Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
Ammar M. A. Abu Znaid ◽  
Mohd. Yamani Idna Idris ◽  
Ainuddin Wahid Abdul Wahab ◽  
Liana Khamis Qabajeh ◽  
Omar Adil Mahdi
Keyword(s):  
Wireless Sensor Networks ◽  
Monte Carlo ◽  
Sensor Networks ◽  
Sequential Monte Carlo ◽  
Research Work ◽  
Computational Cost ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Future Research ◽  
Advantages And Disadvantages

The advancement of digital technology has increased the deployment of wireless sensor networks (WSNs) in our daily life. However, locating sensor nodes is a challenging task in WSNs. Sensing data without an accurate location is worthless, especially in critical applications. The pioneering technique in range-free localization schemes is a sequential Monte Carlo (SMC) method, which utilizes network connectivity to estimate sensor location without additional hardware. This study presents a comprehensive survey of state-of-the-art SMC localization schemes. We present the schemes as a thematic taxonomy of localization operation in SMC. Moreover, the critical characteristics of each existing scheme are analyzed to identify its advantages and disadvantages. The similarities and differences of each scheme are investigated on the basis of significant parameters, namely, localization accuracy, computational cost, communication cost, and number of samples. We discuss the challenges and direction of the future research work for each parameter.

Statistical Analysis of Automated versus Manual Pavement Condition Surveys

2005 ◽  
Vol 1940 (1) ◽  
pp. 54-62 ◽  
Author(s):  
Jason M. McQueen ◽  
David H. Timm
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Regression Analysis ◽  
Condition Index ◽  
Pavement Management ◽  
Severity Level ◽  
Pavement Condition ◽  
Condition Rating ◽  
Pavement Condition Index ◽  
Level 3

The Alabama Department of Transportation (ALDOT) has used a vendor to perform automated pavement condition surveys for the Alabama pavement network since 1997. In 2002, ALDOT established a quality assurance (QA) program to check the accuracy of the automated pavement condition data. The QA program revealed significant discrepancies between manual and automatically collected data. ALDOT uses a composite pavement condition index called pavement condition rating (PCR) in its pavement management system. The equation for PCR was developed in 1985 for use with manual pavement condition surveys; however, ALDOT continues to use it with data from automated condition surveys. Since the PCR equation was developed for manual surveys, the discrepancies between the manual and automated data led ALDOT to question the continuity between its manual and automated pavement condition survey programs. A regression analysis was completed to look for any systematic error or general trends in the error between automated and manual data. Also, Monte Carlo simulation was used to determine which distress parameters most influence the PCR and whether they require more accuracy. The regression analysis showed the following general trends: automated data overreport outside wheelpath rut depth, under-report alligator severity Level 1 cracking, and overreport alligator severity Level 3 cracking. Through Monte Carlo simulation, it was determined that all severity levels of transverse cracking, block cracking, and alligator cracking data require greater accuracy.

scholarly journals OPTIMIZATION OF PRESS-FIT PROCESSES

2021 ◽  
Vol 55 (2) ◽  
pp. 207-212
Author(s):  
Gašper Gantar ◽  
Peter Göncz ◽  
Miha Kovačič
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Input Parameter ◽  
Research Work ◽  
Rejection Rate ◽  
The Core ◽  
Press Fit ◽  
Stage Of Development ◽  
Fe Simulations ◽  
The Press

The press-fit process is an efficient, low-cost method for joining parts. The parts that must be joined interfere with each other’s occupation of space; therefore, contact dimensions and their tolerances influence the quality of the assembly. The traditional method for the selection of contact dimensions and their tolerances is based on engineering experience. The idea of the research work presented in this paper is to optimize the press-fit process at an early stage of development process, involving prediction and optimization of the joining force and consequently the prediction and minimization of the rejection rate. Accordingly, several finite-element (FE) simulations of the press-fit process for predicting the joining forces were conducted, considering input-parameter variations (material properties: yield stress, hardening exponent; geometry: shaft diameter, guide diameter of the core, functional diameter of the core; friction coefficient). Based on FE simulations and 47 different input-parameter-variation results, the empirical model for predicting the joining force using the response-surface methodology (RSM) was obtained. By using RSM and a stochastic Monte Carlo simulation, the rejection rate was also determined. The predicted and the actual rejection rates for selected process parameters were 1.4 % and 1.5 %, respectively. Consequently, the press-fit process can also be optimized to reduce the rejection rate using the same Monte Carlo simulation. The results of the analysis show that the rejection rate can be reduced from 1.4 % to 0.2 %.

scholarly journals MONTE-CARLO APPROACH FOR MEASURING ADJUSTING COST RISKS VALUES OF RESIDENTIAL BUILDING PROJECT’S WHOLE LIFE CYCLE FROM CLIENTS’ PERSPECTIVE IN THE UNITED ARAB EMIRATES

2021 ◽  
Vol 6 (1) ◽  
pp. 72-89
Author(s):  
Ahmed Sadek
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
United Arab Emirates ◽  
Monte Carlo Model ◽  
Residential Building ◽  
Statistical Modelling ◽  
Future Research ◽  
Unique Contribution ◽  
Monte Carlo Approach ◽  
Whole Life Cycle

Purpose – Although projects’ experts always take into consideration the related cost-risks. They are experiencing the challenge of not being able to finish the project within the estimated budget. Latest cost-risks studies concentrated on modelling and estimating risks at the preconstruction stage. This article aims to approach Monte-Carlo simulation using stochastic mathematical modelling to measure cost-risks error (i.e., adjusting cost-risks). Methodology – The approach of this research is solely quantitative. It is using statistical modelling and simulations to ensure the accuracy and precision of the developed Monte-Carlo model. However, this study is utilizing Microsoft Office Excel Software Mersenne twister algorithm to generate random numbers to ensure most accurate Monte-Carlo approach. The mathematical equations system is built into Excel. Findings – The research outputs are considered significant in project management body of knowledge. This is because of the resulted evidence that is proving the applicability to measure cost risks error using Monte-Carlo simulation. This study presented cost risks and differentiated between contractors’ and clients’ views.    Unique contribution to theory, practice and policy – The originality of this article comes from providing the first Monte-Carlo approach for measuring projects’ cost-risks error from client’s perspective. The theoretical-implications, practical-implications, and limitations are presented in the conclusion for future research.

Reliability Prediction of Remanufactured Product: A Welding Repair Process Case Study

2006 ◽  
Author(s):  
Marcos Esterman ◽  
Philip Gerst ◽  
Elizabeth DeBartolo ◽  
Michael H. Haselkorn
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Process Control ◽  
Failure Modes ◽  
Welding Process ◽  
Reliability Prediction ◽  
Future Research ◽  
Control Parameters ◽  
Reliability Performance

Many experts agree that effective management of system reliability and reliability validation during product development is a key to achieve superior time to market and life cycle quality. However, reliability performance prediction is a common problem faced by all product developers and it is usually a difficult task. A related problem is to determine the reliability performance of a remanufactured product. Clearly, the remanufacturer would like to know the expected reliability of their product before entering it into service, but unlike an original manufacturer, they will typically have much less information available to them. In this paper, a general framework for reliability prediction in a remanufacturing environment is proposed. A case study of a remanufactured engine cylinder head that has had a fatigue crack repaired by a welding process will be presented in order to illustrate the process. The approach combines the use of Failure Modes and Effects Analysis (FMEA), Experimental Model Building, Monte Carlo Simulation and Linear Elastic Fracture Mechanics (LEFM) to generate a reliability estimate. The FMEA and physical modeling will be used to generate a model that relates the welding process control parameters to the fatigue performance of the test specimens. Monte Carlo Simulation techniques and LEFM will build on the above model to relate the process control parameters to the reliability performance. The paper concludes by discussing the utility of such a model and approach, and presents the future research agenda.

scholarly journals Kinematic Reliability Solution of 3-UPS-PU Parallel Mechanism Based on Monte Carlo Simulation

2015 ◽  
Vol 9 (1) ◽  
pp. 324-332 ◽  
Author(s):  
Guohua Cui ◽  
Muyuan Sun ◽  
Liang Yan ◽  
Hongjuan Hou ◽  
Haiqiang Zhang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Inverse Kinematics ◽  
Parallel Mechanism ◽  
Solution Method ◽  
Simulation Method ◽  
Inverse Kinematic ◽  
Future Research ◽  
Position And Orientation ◽  
Kinematic Solution

In order to research kinematic reliability of 3-UPS-PU parallel mechanism, the structure and kinematics analysis were performed. Inverse kinematics equation can be derived by homogeneous coordinate transformation formula. Position and orientation output error forward kinematics model was obtained by the differential transformation on the basis of inverse kinematic solution of the position. The curves of the position and orientation output errors can be plotted with a large batch production by adopting Monte-Carlo simulation method. Then kinematic reliability of the mechanism can be solved through the probability statistics method and theoretical solution method respectively. Finally, these two methods were compared with each other. The results illustrate that the results of the two methods are basically consistent, and the mechanism can be work reliably and stably under general operations, which provides some valuable references for the related future research.

Electron Scattering in Solids

1990 ◽  
Vol 48 (2) ◽  
pp. 4-5
Author(s):  
Ryuichi Shimizu ◽  
Ze-Jun Ding
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Angular Distribution ◽  
Elastic Scattering ◽  
Electron Scattering ◽  
Cross Sections ◽  
Expansion Method ◽  
Electron Excitation ◽  
Partial Wave Expansion ◽  
Backscattered Electrons

Monte Carlo simulation has been becoming most powerful tool to describe the electron scattering in solids, leading to more comprehensive understanding of the complicated mechanism of generation of various types of signals for microbeam analysis.The present paper proposes a practical model for the Monte Carlo simulation of scattering processes of a penetrating electron and the generation of the slow secondaries in solids. The model is based on the combined use of Gryzinski’s inner-shell electron excitation function and the dielectric function for taking into account the valence electron contribution in inelastic scattering processes, while the cross-sections derived by partial wave expansion method are used for describing elastic scattering processes. An improvement of the use of this elastic scattering cross-section can be seen in the success to describe the anisotropy of angular distribution of elastically backscattered electrons from Au in low energy region, shown in Fig.l. Fig.l(a) shows the elastic cross-sections of 600 eV electron for single Au-atom, clearly indicating that the angular distribution is no more smooth as expected from Rutherford scattering formula, but has the socalled lobes appearing at the large scattering angle.

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