Monte Carlo simulation as a tool for tunneling planning

Vargas, Juan P.; Koppe, Jair Carlos; Pérez, Sebastián

doi:10.1016/j.tust.2013.10.011

Cited by 14 publications

(8 citation statements)

References 6 publications

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“…For instance, Vargas et al (2014) implemented MCS to model opening excavation time, providing several managerial insights regarding the best-and worst-case planning scenarios. Tokdemir et al (2019) used MCS to quantify the consequences of risks associated with the delays in construction projects.…”

Section: Rimentioning

confidence: 99%

Integrating risk management's best practices to estimate deep excavation projects’ time and cost

Heravi

Taherkhani

Sobhkhiz

et al. 2021

BEPAM

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PurposeThis study provides an integrated risk-based cost and time estimation approach for deep excavation projects. The purpose is to identify the best practices in recent advances of excavation risk analysis (RA) and integrate them with traditional cost and time estimation methods.Design/methodology/approachThe implemented best practices in this research are as follows: (1) fault-tree analysis (FTA) for risk identification (RI); (2) Bayesian belief networks (BBNs), fuzzy comprehensive analysis and Monte Carlo simulation (MCS) for risk analysis; and (3) sensitivity analysis and root-cause analysis (RCA) for risk response planning (RRP). The proposed approach is applied in an actual deep excavation project in Tehran, Iran.FindingsThe results show that the framework proposes a practical approach for integrating the risk management (RM) best practices in the domain of excavation projects with traditional cost and time estimation approaches. The proposed approach can consider the interrelationships between risk events and identify their root causes. Further, the approach engages different stakeholders in the process of RM, which is beneficial for determining risk owners and responsibilities.Originality/valueThis research contributes to the project management body of knowledge by integrating recent RM best practices in deep excavation projects for probabilistic estimation of project time and cost.

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Section: Rimentioning

confidence: 99%

Integrating risk management's best practices to estimate deep excavation projects’ time and cost

Heravi

Taherkhani

Sobhkhiz

et al. 2021

BEPAM

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“…Unpredictable variations in the properties of rock mass can mean great economic losses regarding the execution of excavations, as they cause the need to change the adopted support, delay development and can even collapse the excavation. According to Vargas et al [10], both for the development of underground mining and civil excavations, the difficulties in forecasting productivity times of the unitary operations (drilling, blasting, hauling, and support) are highly costly. Each operation tends to present a certain distribution of occurrence probability.…”

Section: Introductionmentioning

confidence: 99%

Risk analysis by Monte Carlo simulation in underground rock excavation projects

Cardozo

Cordova²,

Petter³

2022

DYNA

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Underground excavations are among the most complex engineering works in existence, as they have many variables involved, from the working environment to the methods and equipment adopted for excavation. Historically, preliminary excavation projects have been developed based on empirical methods and qualitative or semi-quantitative classifications of rock mass. Given insufficient information regarding rock mass properties, due to technical limitations related to soundings and their interpretations, there is—from conceptual studies and project executions—great variability in the decisions to be made. Wrong decisions regarding the excavation method, support type, and projections of advances can be highly costly to the enterprise, leading to unplanned or unnecessary expenses and/or risks to human lives. Thus, this study proposes the use of quantitative Risk Analysis by Monte Carlo Simulations to determine the most likely support class to be applied in an underground excavation project.

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“…Nowadays, it is possible to find in the literature many alternatives available for reliability analysis of complex systems [9,10]. e systematic studies are usually developed considering techniques and methodologies as Reliability Block Diagrams (RBDs) [11,12], Fault Trees (FTs) [13], Reliability Graphics (RGs) [14], Petri Nets (PNs) [15], and Monte Carlo simulation [16][17][18] among others. More recently, other techniques have emerged such as Multistate Systems [19], Graph Topology [20], and fuzzy approaches [3] which have allowed to reveal subjacent connections rising from the process dynamic.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Impact of Virtual Standby Systems in Failure Propagation for Complex Wastewater Treatment Processes

et al. 2021

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This article proposes an original probabilistic modelling methodology named Virtual Standby (VSB), which enables a practical simulation, analysis, and evaluation of the impact on availability and reliability achieved by potential buffering policies on the performance of complex production systems. Virtual Standby (VSB) corresponds to a design and operational characteristic where some machines under a failure scenario are capable to provide for a limited time, continuity to the subsystems downstream before suffering delay which is currently not considered when assessing availability. This feature plays a relevant role on the propagation of the effect of a failure; indeed, it could prevent the propagation by guaranteeing the isolation time needed to recover from its failure, controlling and reducing the production losses downstream. A case study of the preliminary treatment process of a wastewater treatment facility (WWTF) is developed bearing in mind the systemic behaviour in the event of a failure and the specific features of each equipment. VSB is a big advantage for the representation of this complex processes because, among other things, it considers the impact of buffering policies on the perceived availability of the system. This model allows determining different production levels, with a better and easier fitting of the reliability, availability, and production forecast of the process. Finally, the comparison between the VSB simulation results with traditional procedures that do not consider the operational continuity under a failure scenario confirms the strength and precision of the proposal for complex systems.

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Monte Carlo simulation as a tool for tunneling planning

Cited by 14 publications

References 6 publications

Integrating risk management's best practices to estimate deep excavation projects’ time and cost

Integrating risk management's best practices to estimate deep excavation projects’ time and cost

Risk analysis by Monte Carlo simulation in underground rock excavation projects

Assessing the Impact of Virtual Standby Systems in Failure Propagation for Complex Wastewater Treatment Processes

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