Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Risk-Informed Prediction of Dredging Project Duration Using Stochastic Machine Learning
Dredging engineering projects are complex because they involve greater uncertainty from the natural environment, social needs, government policy and many stakeholders. Engineering companies submit tenders that draw on similar cases undertaken in recent years. However, weather, earthquakes, typhoons and other disasters often change landforms. Therefore, evaluating the duration of dredging projects with reference to only a few previous cases is inadequate, often leading to an unnecessarily long construction duration if the scope of the project is not clearly defined at the early phase. The goal of this investigation aimed to estimate project duration at the beginning of construction and the probability of risk. Evolutionary machine learning was used to build a deterministic model of dredging project duration. Monte Carlo simulation was then utilized to establish the probabilistic distribution of the project duration based on historical patterns. The analytical outputs are displayed through a graphical user interface that provides project coordinators with a means of assessing the uncertainty of project duration in the initial phase of the project. This study will provide a practical reference for contractors and the Water Resources Agency.
Risk-Informed Prediction of Dredging Project Duration Using Stochastic Machine Learning
Dredging engineering projects are complex because they involve greater uncertainty from the natural environment, social needs, government policy and many stakeholders. Engineering companies submit tenders that draw on similar cases undertaken in recent years. However, weather, earthquakes, typhoons and other disasters often change landforms. Therefore, evaluating the duration of dredging projects with reference to only a few previous cases is inadequate, often leading to an unnecessarily long construction duration if the scope of the project is not clearly defined at the early phase. The goal of this investigation aimed to estimate project duration at the beginning of construction and the probability of risk. Evolutionary machine learning was used to build a deterministic model of dredging project duration. Monte Carlo simulation was then utilized to establish the probabilistic distribution of the project duration based on historical patterns. The analytical outputs are displayed through a graphical user interface that provides project coordinators with a means of assessing the uncertainty of project duration in the initial phase of the project. This study will provide a practical reference for contractors and the Water Resources Agency.
Risk-Informed Prediction of Dredging Project Duration Using Stochastic Machine Learning
Jui-Sheng Chou (Autor:in) / Ji-Wei Lin (Autor:in)
2020
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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