scholarly journals Risk quantification using fuzzy-based Monte Carlo simulation

2020 ◽  
Vol 25 ◽  
pp. 87-98 ◽  
Author(s):  
Osama Moselhi ◽  
Mohammadjavad Arabpour Roghabadi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Construction Projects ◽  
Accurate Estimate ◽  
Cost Data ◽  
Project Cost ◽  
Cost Item ◽  
Project Data ◽  
User Friendly ◽  
The Impact

Estimating cost contingency of construction projects depends largely on data captured from previous projects and/or experience and judgment of members of project team. Mote Carlo simulation is commonly used in estimating contingency, where its accuracy was reported to depend on number of iterations used in the simulation process, probability density functions associated with each project cost item being considered and the correlation among these cost items. The literature reveals that the latter is the most important issue for accurate estimate of contingency. It, however, requires the calculation of coefficients of correlation among cost items based on captured historical records of cost data. Subjective correlation was introduced to alleviate the difficulties associated with the calculation of these coefficients. This paper presents a newly developed method for cost contingency estimation that considers subjective correlations and allows for contingency estimation with and without computer simulation. Unlike the methods reported in the literature, the present method considers uncertainty associated with the coefficients of correlation and utilizes earlier work of the first author in calculating the variance of total project cost. It also allows for assessing the impact of variable covariance matrix on the estimated project cost using a simple and user-friendly computational platform. The application of the developed method on cost data captured from two databases demonstrates its use and accuracy in estimating cost contingency. The results are compared to those produced by others using Monte Carlo Simulation with and without correlation using an actual project data.

scholarly journals Assessment of risk-forming factors of construction production in conditions of uncertainty

2021 ◽  
Vol 258 ◽  
pp. 09052
Author(s):  
Andrey Schreiber ◽  
Ivan Abramov ◽  
Zaid Al-Zaidi
Keyword(s):  
Risk Factors ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Construction Industry ◽  
Construction Projects ◽  
Accurate Estimate ◽  
Project Life Cycle ◽  
Materials Used ◽  
Project Life ◽  
The Impact

Many changes have occurred in the construction industry over the past few years as a result of the development of technologies and specifications of the materials used and modern technologies, which led to an increase in the accuracy and speed of implementation of various stages of the project life cycle, and it became important to anticipate external and internal risks of the project and plan a response to these risks, since they have the effect of an unaccounted price increase and an excess of the contract period. From this perspective, risk assessment was a necessary tool to determine the risks to which the project was exposed in order to find the best way to deal with them. The aim of the study is to identify the most significant types of risk factors faced by the construction industry, which lead to exceeding the specified time for implementation and to large losses, which helps stakeholders in this area to predict potential obstacles and be able to quickly make appropriate decisions. To achieve this goal, a questionnaire survey was conducted to collect information from specialists in the construction industry, as well as references in this field to express an opinion on the intensity of the impact of each studied risk factor. Thereafter, Monte Carlo simulations were used to assess the risk factors studied. The study found that Monte Carlo simulations, which depend on repeated scenarios hundreds or thousands of times, can provide an accurate estimate of the risks faced by investment and construction projects in conditions of uncertainty.

scholarly journals UNCERTAINTY OF BATHYMETRY MODELS AND EFFECT ON TSUNAMI MODELS

2020 ◽  
pp. 22
Author(s):  
Ignacio Sepulveda ◽  
Jennifer S. Haase ◽  
Philip L.-F. Liu ◽  
Mircea Grigoriu ◽  
Brook Tozer ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Field ◽  
Field Model ◽  
Tsunami Hazard ◽  
Response Uncertainty ◽  
Random Field Model ◽  
Three Stages ◽  
Hazard Assessments ◽  
The Impact

We describe the uncertainties of altimetry-predicted bathymetry models and then quantify the impact of this uncertainty in tsunami hazard assessments. The study consists of three stages. First, we study the statistics of errors of altimetry-predicted bathymetry models. Second, we employ these statistics to propose a random field model for errors anywhere. Third, we use bathymetry samples to conduct a Monte Carlo simulation and describe the tsunami response uncertainty. We found that bathymetry uncertainties have a greater impact in shallow areas. We also noted that tsunami leading waves are less affected by uncertainties.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/zzL_XWWAQ7o

scholarly journals Changes in the Portfolio Management and Construction under the Pandemic Era

2021 ◽  
Vol 275 ◽  
pp. 01005
Author(s):  
Ruipeng Tan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Stock Returns ◽  
Financial Market ◽  
Portfolio Management ◽  
Portfolio Construction ◽  
Sample Data ◽  
Before And After ◽  
Reduce Risk ◽  
The Impact

This paper focuses on comparing portfolio management and construction before and after the coronavirus. First, this paper presents the importance of building up portfolios for investors to diversify their risks. Theories on portfolio management are discussed in this section to show how they have been developed to help on investing and reduce risk. Then, the paper moves on to show the impact of the pandemic on the financial market and portfolio management. Sample data on tech stock returns are collected to perform a Monte Carlo simulation on portfolio construction to find out the efficient portfolio before and after the COVID-19 outbreak. The efficient portfolio is build based on the Markowitz theory to find the combination. Comparisons between these portfolio constructions are made to find out the changes in portfolio management and construction under the pandemic era. In conclusion, this paper presents how pandemic has changed and impacted the investments and lists recommendations on future portfolio management and construction.

scholarly journals Social-network-based water supply network sectorization methodology using monte carlo simulation to predict economical and operational benefits

2017 ◽  
Vol 26 ◽  
pp. 44-53
Author(s):  
Enrique Campbell ◽  
Amilkar Illaya-Ayza ◽  
Joaquín Izquierdo ◽  
Rafael Pérez-García ◽  
Idel Montalvo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Water Supply ◽  
Optimization Method ◽  
Supply Network ◽  
Water Supply Network ◽  
Water Supply Management ◽  
Net Profit ◽  
Short Run ◽  
The Impact

Water Supply Network (WSN) sectorization is a broadly known technique aimed at enhancing water supply management. In general, existing methodologies for sectorization of WSNs are limited to assessment of the impact of its implementation over reduction of background leakage, underestimating increased capacity to detect new leakage events and undermining appropriate investment substantiation. In this work, we raise this issue and put in place a methodology to optimize sectors' design. To this end, we carry out a novel combination of the Short Run Economic Leakage Level concept (SRELL- corresponding to leakage level that can occur in a WSN in a certain period of time and whose reparation would be more costly than the benefits that can be obtained). With a non-deterministic optimization method based on Genetic Algorithms (GAs) in combination with Monte Carlo simulation. As an example of application, methodology is implemented over a 246 km pipe-long WSN, reporting 72 397 $/year as net profit.

scholarly journals An assessment of cost escalation in building construction project

2021 ◽  
Vol 39 (4) ◽  
pp. 1029-1034
Author(s):  
A. Nazif ◽  
A.K. Mustapha ◽  
F. Sani
Keyword(s):  
Construction Projects ◽  
Building Construction ◽  
Project Cost ◽  
Mean Values ◽  
Design Error ◽  
Project Construction ◽  
Effective Cost ◽  
Building Project ◽  
The Impact ◽  
The Relationship

Estimating of cost for building construction projects with minimum error at the conceptual stage of project development is quite  essential for planning. This study seeks to evaluate factors responsible for cost escalation of building construction projects.  Questionnaires were administered to examine and assess these factors. Subsequently, the mean score value of each factor was determined. In addition, Correlation and Linear regression analyses were used to establish the relationship between these factors. Factors responsible for cost escalation in projects were examined as well as the impact of those factors, and occurrence of those factors on project cost. The result of the analysis showed that, the most agreed factors responsible for project cost escalation were; inadequate supervision, irregular payment, and design error, having high mean values of 4.25, 4.20, and 4.15, respectively. Also, correlation analysis result established that the factors responsible for cost escalation and the impact of cost escalation had significant R and R2 of 0.81 and 0.70 respectively. Addressing these factors would go a long way in reducing the escalation of building project cost. Never the less, an effective cost management strategy is absolutely necessary to safeguard and sustain the construction  industry. Keywords: cost escalation, building project, construction, regression analysis

Benchmarking Fuel Economy of Connected and Automated Vehicles in Real World Driving Conditions via Monte Carlo Simulation

2020 ◽  
Author(s):  
Shreshta Rajakumar Deshpande ◽  
Shobhit Gupta ◽  
Dennis Kibalama ◽  
Nicola Pivaro ◽  
Marcello Canova
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Fuel Economy ◽  
Real World ◽  
Human Subjects ◽  
Driver Model ◽  
Traffic Information ◽  
Vehicle Speed ◽  
Instrumented Vehicle ◽  
The Impact

Abstract Connectivity and automation have accelerated the development of algorithms that use route and real-time traffic information for improving energy efficiency. The evaluation of such benefits, however, requires establishing a reliable baseline that is representative of a real-world driving environment. In this context, virtual driver models are generally adopted to predict the vehicle speed based on route data and presence of lead vehicles, in a way that mimics the response of human drivers. This paper proposes an Enhanced Driver Model (EDM) that forecasts the human response when driving in urban conditions, considering the effects of Signal Phasing and Timing (SPaT) by introducing the concept of Line-of-Sight (LoS). The model was validated against data collected on an instrumented vehicle driven on public roads by different human subjects. Using this model, a Monte Carlo simulation is conducted to determine the statistical distribution of fuel consumption and travel time on a given route, varying driver behavior (aggressiveness), traffic conditions and SPaT. This allows one to quantify the impact of uncertainties associated to real-world driving in fuel economy estimates.

scholarly journals Evaluating network importance measures based on the construction spectrum

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983083
Author(s):  
Yongjun Du ◽  
Zhenggeng Ye ◽  
Pan Zhang ◽  
Yaqi Guo ◽  
Zhiqiang Cai
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Network Structure ◽  
Network Reliability ◽  
Equal Probability ◽  
Numerical Example ◽  
Estimate Reliability ◽  
The Impact

The construction spectrum is a useful tool to investigate the network reliability, which only depends on network structure and is called structure invariant. Importance measures are efficient tools to quantify and rank the impact of edges within a network. This study considers the K-terminal network with n edges and assumes that edges fail with an equal probability. The article focuses on investigating the importance measures of individual edge for the K-terminal network, including reliability achievement worth and reliability reduction worth, via the construction spectrum–based method. Generally, we establish the equations for reliability achievement worth and reliability reduction worth using the construction spectrum and determine the conditions under which the importance rankings generated by reliability achievement worth and reliability reduction worth only depend on the network structure through the construction spectrum. Similar results are obtained with reliability achievement worth and reliability reduction worth for pair of edges. A construction spectrum–based Monte-Carlo simulation is used to estimate reliability achievement worth and reliability reduction worth. Finally, a numerical example is presented to illustrate the application of these measures.

scholarly journals Practical Risk Assessment of Ground Vibrations Resulting from Blasting, Using Gene Expression Programming and Monte Carlo Simulation Techniques

2020 ◽  
Vol 10 (2) ◽  
pp. 472 ◽  
Author(s):  
Amir Mahdiyar ◽  
Danial Jahed Armaghani ◽  
Mohammadreza Koopialipoor ◽  
Ahmadreza Hedayat ◽  
Arham Abdullah ◽  
...  
Keyword(s):  
Gene Expression ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Gene Expression Programming ◽  
Sensitivity Analyses ◽  
Accurate Estimation ◽  
Blasting Operation ◽  
Simulation Techniques ◽  
Input Variables ◽  
The Impact

Peak particle velocity (PPV) is a critical parameter for the evaluation of the impact of blasting operations on nearby structures and buildings. Accurate estimation of the amount of PPV resulting from a blasting operation and its comparison with the allowable ranges is an integral part of blasting design. In this study, four quarry sites in Malaysia were considered, and the PPV was simulated using gene expression programming (GEP) and Monte Carlo simulation techniques. Data from 149 blasting operations were gathered, and as a result of this study, a PPV predictive model was developed using GEP to be used in the simulation. In order to ensure that all of the combinations of input variables were considered, 10,000 iterations were performed, considering the correlations among the input variables. The simulation results demonstrate that the minimum and maximum PPV amounts were 1.13 mm/s and 34.58 mm/s, respectively. Two types of sensitivity analyses were performed to determine the sensitivity of the PPV results based on the effective variables. In addition, this study proposes a method specific to the four case studies, and presents an approach which could be readily applied to similar applications with different conditions.

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