Combining Monte Carlo Simulation and Bayesian Networks Methods for Assessing Completion Time of Projects under Risk

Namazian, Ali; Yakhchali, Siamak Haji; Yousefi, Vahidreza; Tamošaitienė, Jolanta

doi:10.3390/ijerph16245024

Cited by 23 publications

(15 citation statements)

References 29 publications

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“…Various types of risks with construction project time schedules were analysed with the use of such methods as naïve Bayesian classifiers [36], Bayesian belief networks [37], the logit and probit models [38], robustness [62][63][64][65], Monte Carlo simulation [66], etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Project Risk in the Context of Construction Schedules—Combined Monte Carlo Simulation and Time at Risk (TaR) Approach: Insights from the Fort Bema Housing Estate Complex

Sobieraj

Metelski

2022

Applied Sciences

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In this article, we present our own construction process model consisting of 16 stages and eight phases, which is particularly applicable to large investment projects. In the context of each project phase, we examine how the appropriate way of scheduling construction processes affects the problem of the risk of prolonging individual phases and the whole project, as well as of not meeting deadlines (which is one of the main problems faced by management practitioners in the construction industry). There are many methods for assessing risk in this context, but they tend to be overly complex and rarely used by construction practitioners. On the other hand, the risks associated with potential schedule delays can be considered holistically. One tool that can serve this purpose is the combined Monte Carlo simulation and Time-at-Risk (TaR) approach, which originates from the world of finance. We show how the implementation of the process model (individual phases) and the whole project can be considered in the context of the covariance matrix between all its phases and how changes in the arrangement of these phases can affect the risk of time extension of the whole project. Our study is based on simulation data for a large development project (Fort Bema/Parkowo-Leśne housing estate complex) in Bemowo, a district of Warsaw, carried out between 1999 and 2012. The entire investment project involved the construction of almost 120,000 m2 of floor space.

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Section: Literature Reviewmentioning

confidence: 99%

Project Risk in the Context of Construction Schedules—Combined Monte Carlo Simulation and Time at Risk (TaR) Approach: Insights from the Fort Bema Housing Estate Complex

Sobieraj

Metelski

2022

Applied Sciences

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“…MC is a traditional approach in the research area of uncertainty analysis in the construction business and can estimate the frequency of fatal incidents [42]. MC is calculated by Equation (2) [44,45]:…”

Section: Calculation Of Frequency Of Fatal Incidents Using Integrated...mentioning

confidence: 99%

Comparative Analysis of Degree of Risk between the Frequency Aspect and Probability Aspect Using Integrated Uncertainty Method Considering Work Type and Accident Type in Construction Industry

Jeong

2022

Applied Sciences

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Fatal incidents in the construction business are higher than in other industries. Previous studies concentrated on the frequency of fatal incidents based on safety management, however, the probability of fatal incidents might be more important than the frequency of fatal incidents. For instance, certain work types have low fatal incident cases but show a high probability of fatal incidents,. which means they are riskier than others. The purpose of this study is to analyze the level of risk by comparing the frequency of fatal incidents and probability of fatal incidents for 27 types of work and 18 types of accidents using an uncertainty analysis. This study is carried out in five stages from the collection of data to conducting the statistical analysis. The result of the research shows the estimated rank of frequency and probability for work and accident type, respectively. For instance, ‘reinforced concrete construction work’ (66.5 fatal incidents) showed the highest frequency work type, and ‘scaffold and demolition work’ (28.65‱) showed the highest fatality rate. This research addressed the uncertainty problem using an integrated time series and estimation method to compare the degree of risk from the viewpoint of frequency and probability aspects in the construction business.

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“…In the proposed approach, BN is used for the coherent monitoring of project completion time uncertainty, given partial completion of project. Namazian et al (2019) combined BN and MCS to assess the impact of risks on project completion time. Cheng et al (2019) combined the fuzzy set theory, BN and MCS to model the uncertainties affecting the installation duration of offshore wind turbines.…”

Section: Application Of Bayesian Network In Project Schedulingmentioning

confidence: 99%

Project scheduling and performance prediction: a fuzzy-Bayesian network approach

Rezakhani

2021

ECAM

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PurposeDespite the extensive research in project risk management and availability of several techniques and tools, quantifying uncertainty in project schedules remains a challenge. Current risk analysis models suffer from several shortcomings that need to be addressed to provide more reliable and valid schedules. This paper aims to present a dynamic decision support system with the purpose of providing project managers with necessary tool for making real-time informed decisions.Design/methodology/approachThe proposed approach incorporates the widely accepted critical path method (CPM) calculations in a Bayesian network (BN). BN is employed to conduct inferencing and causal analysis and provide probabilistic results, which can improve the decision-making process. Time parameters of each activity in the CPM network is modeled by a set of simulation nodes in the BN. Prior probability distribution of activities duration is extracted from experts using a fuzzy analytical solution.FindingsThe model proposed in this paper is able to address some key outstanding issues of current project scheduling techniques through: (1) modeling the causality among different sources of schedule uncertainty, (2) minimizing uncertainty in experts' evaluations, (3) assessing effects of unknown risk factors and (4) using actual activity data for learning the behavior of project and predicting crew productivity.Originality/valueThe purposed methodology provides a framework for the new generation of project schedule analysis tools that are better informed by available knowledge and data, and hence, more reliable and useful.

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Combining Monte Carlo Simulation and Bayesian Networks Methods for Assessing Completion Time of Projects under Risk

Cited by 23 publications

References 29 publications

Project Risk in the Context of Construction Schedules—Combined Monte Carlo Simulation and Time at Risk (TaR) Approach: Insights from the Fort Bema Housing Estate Complex

Project Risk in the Context of Construction Schedules—Combined Monte Carlo Simulation and Time at Risk (TaR) Approach: Insights from the Fort Bema Housing Estate Complex

Comparative Analysis of Degree of Risk between the Frequency Aspect and Probability Aspect Using Integrated Uncertainty Method Considering Work Type and Accident Type in Construction Industry

Project scheduling and performance prediction: a fuzzy-Bayesian network approach

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