scholarly journals Risk management of a torrential flood construction project using the Monte Carlo simulation

2016 ◽  
pp. 29-44 ◽  
Author(s):  
Aleksandar Baumgertel ◽  
Nada Dragovic ◽  
Tijana Vulevic
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Risk Analysis ◽  
Quantitative Methods ◽  
Flow Profile ◽  
Total Cost ◽  
The Monte Carlo Method ◽  
Relevant Variables

Projects for the regulation of torrent basins carry various unforeseen adverse effects that may result in breached deadlines, increased costs, a reduction of quality etc. The paper presents the basic characteristics and most frequent risks associated with erosion control. Furthermore, it provides an overview of risk management through its basic stages - starting from risk identification and risk analysis to risk responses, including the methods used for risk analysis. As a part of quantitative methods for risk analysis, the Monte Carlo method is presented as the one most frequently used in simulations. The Monte Carlo method is a stochastic simulation method consisting of the following stages: the identification of criterion and relevant variables, the allocation of probability for relevant variables, the determination of correlation coefficient among relevant variables, simulation execution and result analysis. This method was applied in the analysis of the total cost of the project for the basin regulation of the Dumaca River in order to determine the funding that would be used as a backup in case of unforeseen events with a negative impact. The project for the regulation of the Dumaca River includes basin regulation in the form of complex flow profile and the lining of zones where necessary in terms of stability. The total cost is presented as a sum of costs of all works (preliminary works, earthworks, masonry works, concrete works and finishing works). The Monte Carlo simulation for cost analysis is carried out using the Oracle Crystal Ball software with its basic steps described in the paper. A sum of funding needed as a financial backup in case of unforeseen events with negative effects is obtained as the simulated total cost of the project.

scholarly journals Technical-Economic Analysis of Grapple Saw

2020 ◽  
Vol 41 (2) ◽  
pp. 219-229 ◽  
Author(s):  
Ricardo Hideaki Miyajima ◽  
Paulo Torres Fenner ◽  
Gislaine Cristina Batistela ◽  
Danilo Simões
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Distributions ◽  
Simulation Method ◽  
Forest Harvesting ◽  
Production Costs ◽  
Wood Harvesting ◽  
Food And Agriculture ◽  
The Monte Carlo Method

The processing of Eucalyptus logs is a stage that follows the full tree system in mechanized forest harvesting, commonly performed by grapple saw. Therefore, this activity presents some associated uncertainties, especially regarding technical and silvicultural factors that can affect productivity and production costs. To get around this problem, Monte Carlo simulation can be applied, or rather a technique that allows to measure the probabilities of values from factors that are under conditions of uncertainties, to which probability distributions are attributed. The objective of this study was to apply the Monte Carlo method for determining the probabilistic technical-economical coefficients of log processing using two different grapple saw models. Field data were obtained from an area of forest planted with Eucalyptus, located in the State of São Paulo, Brazil. For the technical analysis, the time study protocol was applied by the method of continuous reading of the operational cycle elements, which resulted in production. As for the estimated cost of programmed hour, the applied methods were recommended by the Food and Agriculture Organization of the United Nations. The incorporation of the uncertainties was carried out by applying the Monte Carlo simulation method, by which 100,000 random values were generated. The results showed that the crane empty movement is the operational element that most impacts the total time for processing the logs; the variables that most influence the productivity are specific to each grapple saw model; the difference of USD 0.04 m3 in production costs was observed between processors with gripping area of 0.58 m2 and 0.85 m2. The Monte Carlo method proved to be an applicable tool for mechanized wood harvesting for presenting a range of probability of occurrences for the operational elements and for the production cost.

scholarly journals Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport

2020 ◽  
Vol 10 (12) ◽  
pp. 4229 ◽  
Author(s):  
Alexander Heilmeier ◽  
Michael Graf ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
State Of The Art ◽  
Probability Distributions ◽  
Strategic Decisions ◽  
The Monte Carlo Method ◽  
Random Events ◽  
Example Variability

Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers.

scholarly journals C-beta energy converter efficiency modeling

2019 ◽  
Vol 222 ◽  
pp. 02012
Author(s):  
Oleg Kuznetsov ◽  
Viktor Chepurnov ◽  
Albina Gurskaya ◽  
Mikhail Dolgopolov ◽  
Sali Radzhapov
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Theoretical Models ◽  
Maximum Efficiency ◽  
Equivalent Circuits ◽  
Optimal Parameters ◽  
Energy Converter ◽  
Electron Hole ◽  
The Monte Carlo Method

To construct beta converters with maximum efficiency it is necessary to carry out the theoretical calculation in order to determine their optimal parameters - the geometry of the structure, the thickness of the deposition of the radioisotope layer, the depth and the width of the p-n junction, and others. To date, many different theoretical models and calculations methods had been proposed. There are fairly simple theoretical models based on the Bethe-Bloch formula and the calculation of the rate of generation of electron-hole pairs, and on calculations by equivalent circuits. Also, the Monte-Carlo method is used for theoretical modeling of beta converters. This paper explores beta converter optimization using the Monte-Carlo method. The purpose of the study is to conduct Monte-Carlo simulation of the beta converter to determine its optimal parameters.

Exit Condition for Probabilistic Assessment Using Monte Carlo Method

2010 ◽  
Vol 10 (1) ◽  
pp. 1-9
Author(s):  
Jakub Valihrach ◽  
Petr Konečný
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Optimization Design ◽  
Random Variable ◽  
Probability Of Failure ◽  
Probabilistic Assessment ◽  
Theoretical Derivation ◽  
The Monte Carlo Method ◽  
The Relationship

Exit Condition for Probabilistic Assessment Using Monte Carlo Method This paper introduces a condition used to exit a probabilistic assessment using the Monte Carlo simulation, and to evaluate it with regard to the relationship between the computed estimate of the probability of failure and the target design probability. The estimation of probability of failure is treated as a random variable, considering its variance that is dependent on the number of performed Monte Carlo simulation steps. After theoretical derivation of the decision condition, it is tested numerically with regard to its accuracy and computational efficiency. The condition is suitable for optimization design using the Monte Carlo method.

The Research of Construction Project Risk Management Based on Monte Carlo Method and Fuzzy Mathematics

2012 ◽  
Vol 442 ◽  
pp. 341-345
Author(s):  
Si Dong Xu ◽  
Xiao Li Cai ◽  
Wei Liu
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Qualitative Analysis ◽  
Quantitative Research ◽  
Construction Project ◽  
Negative Influence ◽  
Fuzzy Mathematics ◽  
Project Risk

A lot of risks of management occur under the process of the construction project, and it will always bring more tremendous negative influence to the project goal. Therefore, there should have the effective risk method for the project superintendent to forecast the risk occurrence and reduce the loss which the risk brings. There are lots of methods about risks management under the process of the construction project at present and most of the method emphasize particularly on qualitative analysis. This article presents a method which base on the knowledge of Monte Carlo Simulation. It will combine the qualitative analysis and quantitative research and puts forward a new solution.

MONTE CARLO SIMULATIONS FOR THE RISK MANAGEMENT OF ENVIRONMENTAL POLLUTION

2010 ◽  
Vol 12 (01) ◽  
pp. 87-101
Author(s):  
OSAMA A. B. HASSAN
Keyword(s):  
Risk Management ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Monte Carlo Simulations ◽  
Environmental Pollution ◽  
Human Health ◽  
Stochastic Models ◽  
Chemical Pollution ◽  
The Monte Carlo Method

This article attempts to adapt the Monte Carlo method to the quantitative risk management of environmental pollution. In this context, the feasibility of stochastic models to quantitatively evaluate the risk of chemical pollution is first discussed and then linked to a case study in which Monte Carlo simulations are applied. The objective of the case study is to develop a Monte Carlo scheme for evaluating the pollution in a lake environment. It is shown that the results can be of interest as they define the risk margins that are important to the sustainability of the ecosystem in general, and human health in particular. Moreover, assessing the environmental pollution with the help of the Monte Carlo method can be feasible and serve the purpose of investigating and controlling the environmental pollution, in the long and short terms.

Probabilistic Risk Analysis for Highway Construction Management

2014 ◽  
Vol 584-586 ◽  
pp. 2363-2367
Author(s):  
Zeng Zhong Wang ◽  
Bin Shi
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Risk Analysis ◽  
Construction Management ◽  
Management Practices ◽  
Highway Construction ◽  
Computational Method ◽  
Probabilistic Risk Analysis ◽  
Probabilistic Risk

Construction management is the totality of activities that address the managerial and technological aspects of highway construction, conducted during the planning, design, construction, and post-construction phases of the project, for the purpose of achieving scope, quality level, cost, schedule and other project performance objectives. The ability to better understand potential risks and how to manage those yields benefits far in excess of the costs of adopting risk management practices. In this paper, the most common set of processes including risk identification, assessment, analysis, mitigation, allocation, monitoring and updating are discussed, and the relationship between the computational method (the model) and its required inputs and available outputs is illustrated. The methodology and systematic process of probabilistic risk analysis, Monte Carlo simulation methods is conducted, and a comprehensive case study of probabilistic risk analysis for a highway construction management is given out, in which the sensitivity analysis and other output from the simulation model are illustrated to support the risk management plan. It is obviously that the probabilistic risk analysis of Monte Carlo simulation could provide very important information for a decision maker.

scholarly journals A Hybrid Method for Evaluating of Lightning Performance of Overhead Lines based on Monte Carlo Procedure

2016 ◽  
Vol 67 (4) ◽  
pp. 246-252
Author(s):  
Reza Shariatinasab ◽  
Pooya Tadayon ◽  
Akihiro Ametani
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Risk Analysis ◽  
Hybrid Method ◽  
Overhead Lines ◽  
Monte Carlo Procedure ◽  
Acceptable Accuracy ◽  
The Monte Carlo Method ◽  
Analytical Relations

Abstract This paper proposes a hybrid method for calculating lightning performance of overhead lines caused by direct strokes by combining Lattice diagram together with the Monte Carlo method. In order to go through this, firstly, the proper analytical relations for overvoltages calculation are established based on Lattice diagram. Then, the Monte Carlo procedure is applied to the obtained analytical relations. The aim of the presented method that will be called ‘ML method’ is simply estimation of the lightning performance of the overhead lines and performing the risk analysis of power apparatus with retaining the acceptable accuracy. To confirm the accuracy, the calculated results of the presented ML method are compared with those calculated by the EMTP/ATP simulation.

scholarly journals Examining the Value of Monte Carlo Simulation for Project Time Management

2018 ◽  
Vol 24 (1) ◽  
pp. 11
Author(s):  
Goran Avlijas
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Time Management ◽  
Research Question ◽  
Relevant Literature ◽  
Management Tool ◽  
Project Duration ◽  
Schedule Management ◽  
The Monte Carlo Method

Research Question: This paper investigates whether the Monte Carlo simulation can be widely used as a practicable method for the analysis of the risks that impact project duration. Motivation: The main goal was to explore the use of the Monte Carlo simulation for project time management, and shed some light on the key benefits and drawbacks of this method. The paper reviewed the existing literature considering traditional use of the Monte Carlo for quantitative project risk analysis (such as Kwak & Ingall, 2007; Hulett, 2017) and elaborated the issue by suggesting potential improvements in terms of method modification for schedule management, such as event chain methodology proposed by Agarwal & Virine (2017). Another goal was to examine the capability of user-friendly software to provide project managers with some of these benefits. Idea: The core idea of this paper was to evaluate the value of the Monte Carlo method for project time and schedule management, by matching traditional foundations with modern techniques. Data: The paper used the secondary data extracted from relevant literature and project examples. A literature review reveals how the application of the Monte Carlo simulation evolved as a project management tool, along with specific benefits and concerns for its application. Tools: A detailed application of the Monte Carlo in predicting project duration is provided, and the applicability and viability of the method are proven through a case demonstration. Following the presentation of a practical example and discussion of the main features, some limitations and potential improvements to the Monte Carlo method are suggested. Findings: Even with the existence of certain limitations, the Monte Carlo simulation remains the primary method for quantitative analysis of project risks. Despite the Monte Carlo having been found to be applicable, adaptable and predictive of total project duration, it is found to be insufficiently used by practitioners. Contribution: The paper urges the need for research on successful diffusion of the Monte Carlo simulation and helps practitioners to understand the adaptability of the Monte Carlo simulation as a tool for risk quantification and its use for effective duration planning of their projects.  

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