scholarly journals Monte Carlo approach to fuzzy AHP risk analysis in renewable energy construction projects

PLoS ONE ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. e0215943 ◽  
Author(s):  
Luis Serrano-Gomez ◽  
Jose Ignacio Munoz-Hernandez
Keyword(s):  
Monte Carlo ◽  
Renewable Energy ◽  
Risk Analysis ◽  
Construction Projects ◽  
Fuzzy Ahp ◽  
Monte Carlo Approach

Methodology of risk analysis by Monte Carlo Method applied to power generation with renewable energy

2014 ◽  
Vol 69 ◽  
pp. 347-355 ◽  
Author(s):  
Edinaldo José da Silva Pereira ◽  
João Tavares Pinho ◽  
Marcos André Barros Galhardo ◽  
Wilson Negrão Macêdo
Keyword(s):  
Power Generation ◽  
Monte Carlo ◽  
Renewable Energy ◽  
Monte Carlo Method ◽  
Risk Analysis

scholarly journals Construction schedule risk analysis – a hybrid machine learning approach

2022 ◽  
Vol 27 ◽  
pp. 70-93
Author(s):  
John Patrick Fitzsimmons ◽  
Ruodan Lu ◽  
Ying Hong ◽  
Ioannis Brilakis
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Hybrid Method ◽  
Construction Projects ◽  
Gaussian Mixture ◽  
Machine Intelligence ◽  
Accurate Estimation ◽  
Support Vector ◽  
Risk Simulation ◽  
The Uk

The UK commissions about £100 billion in infrastructure construction works every year. More than 50% of them finish later than planned, causing damage to the interests of stakeholders. The estimation of time-risk on construction projects is currently done subjectively, largely by experience despite there are many existing techniques available to analyse risk on the construction schedules. Unlike conventional methods that tend to depend on the accurate estimation of risk boundaries for each task, this research aims to proposes a hybrid method to assist planners in undertaking risk analysis using baseline schedules with improved accuracy. The proposed method is endowed with machine intelligence and is trained using a database of 293,263 tasks from a diverse sample of 302 completed infrastructure construction projects in the UK. It combines a Gaussian Mixture Modelling-based Empirical Bayesian Network and a Support Vector Machine followed by performing a Monte Carlo risk simulation. The former is used to investigate the uncertainty, correlated risk factors, and predict task duration deviations while the latter is used to return a time-risk simulated prediction. This study randomly selected 10 projects as case studies followed by comparing their results of the proposed hybrid method with Monte Carlo Simulation. Results indicated 54.4% more accurate prediction on project delays.

Wigner-Boltzmann Monte Carlo approach to nanodevice simulation: from quantum to semiclassical transport

2009 ◽  
Vol 8 (3-4) ◽  
pp. 324-335 ◽  
Author(s):  
Damien Querlioz ◽  
Huu-Nha Nguyen ◽  
Jérôme Saint-Martin ◽  
Arnaud Bournel ◽  
Sylvie Galdin-Retailleau ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Approach ◽  
Semiclassical Transport

scholarly journals On the stochastic uncertainties of thermally and optically stimulated luminescence signals: A Monte Carlo approach

2020 ◽  
Vol 219 ◽  
pp. 116945
Author(s):  
Vasilis Pagonis ◽  
Sebastian Kreutzer ◽  
Alex Roy Duncan ◽  
Ena Rajovic ◽  
Christian Laag ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Optically Stimulated Luminescence ◽  
Monte Carlo Approach ◽  
Stochastic Uncertainties
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