scholarly journals Bayesian Monte Carlo Simulation–Driven Approach for Construction Schedule Risk Inference

2021 ◽  
Vol 37 (2) ◽  
pp. 04020115
Author(s):  
Long Chen ◽  
Qiuchen Lu ◽  
Shuai Li ◽  
Wenjing He ◽  
Jian Yang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Bayesian Monte Carlo ◽  
Schedule Risk ◽  
Construction Schedule

Analysis of Project Shedule Risk Indexes in PERT Network Using Monte Carlo Simulation

2013 ◽  
Vol 760-762 ◽  
pp. 2205-2211 ◽  
Author(s):  
Yuan Liu ◽  
Zhuo Fu Wang
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Analysis ◽  
Risk Index ◽  
Critical Index ◽  
Project Schedule ◽  
Schedule Risk ◽  
Schedule Risk Analysis ◽  
Construction Schedule

It is convenient and effective to use Monte Carlo simulation (MCS) technique in project schedule risk analysis and assessment, but at the same time, the indexes put forward by scholars up to now is quite few, leaving construction schedule risk assessment still difficult to carry out. Therefore, based on PERT network assumption, the shortcomings of current project schedule risk indexes are summarized and new project schedule risk index is put forward to estimate the criticality of each activity and path to provide more information for project schedule controllers. In the case studied, with the application of the new index, the critical index of each activity is given and divided into five levels, and the new index put forward in this paper shows great superiority over the classic indexes.

Risk Management of Construction Schedule by PERT with Monte Carlo Simulation

2014 ◽  
Vol 548-549 ◽  
pp. 1646-1650 ◽  
Author(s):  
Yang Liu ◽  
Yan Li
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Critical Path ◽  
Total Duration ◽  
Control Points ◽  
Schedule Management ◽  
Simulation Results ◽  
Critical Paths ◽  
Construction Schedule

It has been proved that the construction schedule management was an uncertain problem. Traditional CPM method was a good way to define the total duration and critical paths but can not solve uncertainty. The paper use CPM to define the duration and critical path firstly, then defined the parameters with Delphi and make Monte Carlo simulation. Through simulation results, it is found that the probability to finish the work on time was only 35.3%. The following step is to make sensitivity analysis, through the calculation, the work which has large influence was found and treat as key control points. It is proved that Monte Carlo simulation is useful to solve the problem of construction schedule management.

Study on Application of Monte Carlo Simulation in Construction Schedule Management

2013 ◽  
Vol 859 ◽  
pp. 284-288
Author(s):  
Yang Liu ◽  
Yan Li
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Critical Path ◽  
Total Duration ◽  
Control Points ◽  
Schedule Management ◽  
Simulation Results ◽  
Critical Paths ◽  
Construction Schedule

It has been proved that the construction schedule management was an uncertain problem. Traditional CPM method was a good way to define the total duration and critical paths but can not solve uncertainty. The paper use CPM to define the duration and critical path firstly, then defined the parameters with Delphi and make Monte Carlo simulation. Through simulation results, it is found that the probability to finish the work on time was only 68%. The following step is to make sensitivity analysis, through the calculation, the work which has large influence was found and treat as key control points. It is proved that Monte Carlo simulation is useful to solve the problem of construction schedule management.

SCHEDULE RISK ANALYSIS OF RAILWAY PROJECTS USING MONTE CARLO SIMULATION FOR IMPROVED PROJECT MANAGEMENT

2018 ◽  
Vol 5 (2) ◽  
Author(s):  
Motlatso Mabeba ◽  
Jan Harm C Pretorius ◽  
Leon Pretorius
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Analysis ◽  
Road Transport ◽  
Time Frame ◽  
Rail Transport ◽  
Logistics Industry ◽  
The Poor ◽  
Schedule Risk ◽  
Schedule Risk Analysis

Railways have been used throughout history for the transportation of goods. Even though the inception of rail transport improved civilization, due to its inefficiencies, road transport is at present dominating the freight and logistics industry. Company A, which has the largest market share in the rail freight business, has embarked on projects to improve rail efficiencies by moving higher volumes of freight timeously. Most of the projects embarked on by Company A have failed largely due to the poor planning of the projects in the feasibility stages. Most of the planning schedules are overoptimistic and unrealistic making them unreliable and difficult to track. The scope of this study was to investigate the way in which planning schedules of Company A are developed by undertaking a schedule risk analysis on one of the planning schedules titled 'Design of railway exchange yard' and using Monte Carlo simulation to validate the schedule. If projects of Company A can be planned better, using schedule risk analysis, projects can become more successful and completed within the required time frame.

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.

Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

1990 ◽  
Vol 48 (2) ◽  
pp. 264-265
Author(s):  
D. R. Liu ◽  
S. S. Shinozaki ◽  
R. J. Baird
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Compound Semiconductors ◽  
Energy Dispersive Analysis ◽  
X Ray ◽  
Metastable Alloys ◽  
Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

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