Abstracts – Browse Results
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Abdollahipour, S (2013) Multi-parameter bidding in highway construction and rehabilitation projects, Unpublished PhD Thesis, , Oklahoma State University.
Alroomi, A S (2013) Integrative framework for sustainable development of the cost estimating profession, Unpublished PhD Thesis, , Oklahoma State University.
Brown, B H J (1974) An econometric forecasting model for a segment of the construction market, Unpublished PhD Thesis, , Oklahoma State University.
Duffy, G A (2009) Linear scheduling of pipeline construction projects with varying production rates, Unpublished PhD Thesis, , Oklahoma State University.
Eldin, N N (1987) Methodology for project management control in the construction industry, Unpublished PhD Thesis, , Oklahoma State University.
Ghulman, B A (2000) Predicting construction cost growth in ODOT's paving projects using information available at the bidding time, Unpublished PhD Thesis, , Oklahoma State University.
Hajji, A M (2013) Development of a productivity-based economic, energy, environmental model for heavy duty diesel construction equipment, Unpublished PhD Thesis, , Oklahoma State University.
Johnson, L R (1969) A competitive strategy model for highway construction proposals, Unpublished PhD Thesis, , Oklahoma State University.
Karimi, B (2018) Evaluation of emissions reduction strategies for heavy duty diesel construction equipment, Unpublished PhD Thesis, , Oklahoma State University.
Kim, J (2022) A multimodal approach to improve fire safety on construction sites, Unpublished PhD Thesis, , Oklahoma State University.
King-Lewis, A (2020) Diversity and inclusion of women in the construction industry, Unpublished PhD Thesis, , Oklahoma State University.
Lee, B-H (2011) Forecasting wheat yield and quality conditional on weather information and estimating construction costs of agricultural facilities, Unpublished PhD Thesis, , Oklahoma State University.
Lusby, A K (2003) The effect of increased public investment in transportation infrastructure on oklahoma's economic development, Unpublished PhD Thesis, , Oklahoma State University.
Miller, R H (1973) Life cycle system model for estimating construction equipment ownership costs, Unpublished PhD Thesis, , Oklahoma State University.
Rainer, R K (1968) Predicting productivity of one or two elevators for construction of high-rise buildings, Unpublished PhD Thesis, , Oklahoma State University.
Shararah, H H (1981) Evaluation of wall construction techniques based upon economical factors influencing labor and materials, Unpublished PhD Thesis, , Oklahoma State University.
Spencer, G R (1987) Integration of cost estimating with critical path scheduling, Unpublished PhD Thesis, , Oklahoma State University.
Syachrani, S (2010) Advanced sewer asset management using dynamic deterioration models, Unpublished PhD Thesis, , Oklahoma State University.
- Type: Thesis
- Keywords: accuracy; highway; replacement; utilities; asset management; deterioration; land use; life cycle; risk assessment; cost analysis; life cycle cost; neural network
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/855633518
- Abstract:
The main purpose of this study is to develop Dynamic Deterioration Model that is capable of doing individual and group predictions. The model considers the potential effect of location related attributes (e.g. land use, highway crossing) in addition to physical and operational attributes (e.g. root problem, grease problem). To avoid a uniform treatment to the entire network, the new approach includes clustering model that groups sewer pipes based on their location related attributes and detail operational conditions. Later, the results from group prediction models are applied in pipe material selection based life cycle cost analysis while the results from individual prediction models are used in risk assessment model for the prioritization of future pipe replacement. The study shows that the patterns of deterioration among pipes within a network are indeed vary depending on their physical and operational conditions. The utilization of location related attributes and operational condition data are shown to be helpful to efficiently group pipes with a network into several clusters representing different pattern of deterioration. The comparison among three modeling techniques shows that decision tree has the best accuracy over regression and neural networks models. In the case of pipe material selection, a location sensitive life cycle cost analysis proven to generate a better recommendation that helps utilities to avoid making a wrong decision that may cost the agencies unnecessary expenses throughout the asset life. The uses of deterioration model for individual prediction in conjunction with semi parametric survival analysis is proven to be capable of quantitatively measures the criticality of individual pipe segment. Overall, the optimal utilization of agency's owned data is not only improves the accuracy of the outcomes but also shows how the agency can benefit from data collection efforts throughout the years.
Tamimi, M F (2022) Reliability and sensitivity analysis of civil and marine structures using machine-learning-assisted simulation, Unpublished PhD Thesis, , Oklahoma State University.
Trost, S M (1998) A quantitative model for predicting the accuracy of early cost estimates for construction projects in the process industry, Unpublished PhD Thesis, , Oklahoma State University.
Zeitoun, A A (1992) Evaluation of cost and schedule growth trends during construction, Unpublished PhD Thesis, , Oklahoma State University.