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dc.contributor.advisorBeheiry, Salwa
dc.contributor.advisorAbu-Lebdeh, Ghassan
dc.contributor.authorLabban, Tarek
dc.date.accessioned2012-09-16T07:48:25Z
dc.date.available2012-09-16T07:48:25Z
dc.date.issued2012-05
dc.identifier.other35.232-2012.19
dc.identifier.urihttp://hdl.handle.net/11073/4073
dc.descriptionA Master of Science thesis in Engineering Systems Management, Construction Management Theme by Tarek Labban entitled, "A Framework for Predictive Modeling in Sustainable Projects," submitted in May 2012. Thesis advisor is Dr. Salwa Beheiry and thesis co-advisor is Dr. Ghassan Abu-Lebdeh. Available are both soft and hard copies of the thesis.en_US
dc.description.abstractProject management systems typically target the completion of a project within budget, on time, and fulfilling stakeholders' expectations and environmental needs from the project. The aim becomes harder to achieve when talking about sustainable construction project particularly in the transport sector. Many construction projects in the transportation sector are delayed and have major cost overruns. Most of these projects, in the planning stage, lack predictive mechanisms to support the team's decision making process. This study reviewed a considerable amount of literature on sustainable construction projects and the transportation sector. It was clear from the literature that project teams can benefit from further research and model theory building to help in predicting the cost, schedule, environmental impacts, and safety levels of the projects. Moreover, substantial research has shown the relationship between planning and project outcomes. Thus, this research used documented sustainable management practices and planning indicators and developed a predictive model for absolute cost, absolute schedule, relative cost, relative schedule, environmental impacts, and safety levels. Using this predictive model, the project team will have a solid decision making tool during the planning phase of the project. The predictive model has 50 sub-inputs combined into ten index/input variables and six outputs from several mathematical algorithms. The inputs and the outputs were combined in a format capable of applying multiple regression analysis or artificial neural networks. This model framework will provide a base for future data collections, validation and more detailed feedback and model review. Furthermore, the project team can take the decision to proceed or stop the project at the end of the planning phase or find an alternative project that has better predicted values. Search Terms: Sustainable Construction, Transportation Projects, Planning Indicators, Cost, Schedule, Environmental Impacts, Safety Levels.en_US
dc.description.sponsorshipCollege of Engineeringen_US
dc.description.sponsorshipDepartment of Industrial Engineeringen_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesMaster of Science in Engineering Systems Management (MSESM)en_US
dc.relation.ispartofseriesAmerican University of Sharjah Student Worken_US
dc.subjectSustainable constructionen_US
dc.subjectTransportation projectsen_US
dc.subjectPlanning indicatorsen_US
dc.subjectCosten_US
dc.subjectScheduleen_US
dc.subjectEnvironmental impactsen_US
dc.subjectSafety levelsen_US
dc.subject.lcshSustainable buildingsen_US
dc.subject.lcshDesign and constructionen_US
dc.subject.lcshModel theoryen_US
dc.subject.otherPredictive modelingen_US
dc.titleA Framework for Predictive Modeling in Sustainable Projectsen_US
dc.typeThesisen_US


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