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Abdollahipour, S (2013) Multi-parameter bidding in highway construction and rehabilitation projects, Unpublished PhD Thesis, , Oklahoma State University.
Alroomi, A S (2013) Integrative framework for sustainable development of the cost estimating profession, Unpublished PhD Thesis, , Oklahoma State University.
Brown, B H J (1974) An econometric forecasting model for a segment of the construction market, Unpublished PhD Thesis, , Oklahoma State University.
Duffy, G A (2009) Linear scheduling of pipeline construction projects with varying production rates, Unpublished PhD Thesis, , Oklahoma State University.
Eldin, N N (1987) Methodology for project management control in the construction industry, Unpublished PhD Thesis, , Oklahoma State University.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A (2000) Predicting construction cost growth in ODOT's paving projects using information available at the bidding time, Unpublished PhD Thesis, , Oklahoma State University.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Development of a productivity-based economic, energy, environmental model for heavy duty diesel construction equipment, Unpublished PhD Thesis, , Oklahoma State University.
Johnson, L R (1969) A competitive strategy model for highway construction proposals, Unpublished PhD Thesis, , Oklahoma State University.
Karimi, B (2018) Evaluation of emissions reduction strategies for heavy duty diesel construction equipment, Unpublished PhD Thesis, , Oklahoma State University.
Kim, J (2022) A multimodal approach to improve fire safety on construction sites, Unpublished PhD Thesis, , Oklahoma State University.
King-Lewis, A (2020) Diversity and inclusion of women in the construction industry, Unpublished PhD Thesis, , Oklahoma State University.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yield and quality conditional on weather information and estimating construction costs of agricultural facilities, Unpublished PhD Thesis, , Oklahoma State University.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K (2003) The effect of increased public investment in transportation infrastructure on oklahoma's economic development, Unpublished PhD Thesis, , Oklahoma State University.
Miller, R H (1973) Life cycle system model for estimating construction equipment ownership costs, Unpublished PhD Thesis, , Oklahoma State University.
Rainer, R K (1968) Predicting productivity of one or two elevators for construction of high-rise buildings, Unpublished PhD Thesis, , Oklahoma State University.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.
Tamimi, M F (2022) Reliability and sensitivity analysis of civil and marine structures using machine-learning-assisted simulation, Unpublished PhD Thesis, , Oklahoma State University.
- Type: Thesis
- Keywords: failure; reliability; uncertainty; deterioration; learning; life cycle; life cycle cost; probability; quantification; machine learning; Monte Carlo simulation; simulation
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/2822544516
- Abstract:
Civil and marine structures are subjected to various deterioration mechanisms due to aggressive environmental effects or mechanical loads. In order to maintain an acceptable performance level of these structures, previous research have focused on developing methodologies to quantify their reliability and provide optimized management plans that can reduce the life-cycle cost and failure risk. However, the successful implementation of these methodologies is contingent upon the ability to consider various uncertainties associated with structural performance. These include uncertainties associated with environmental and human-induced stressors, as well as those affecting material and geometrical characterization as well as performance prediction models. Monte Carlo simulation (MCS) with a sufficient number of samples can provide accurate quantification of the structural performance under uncertainty. However, for complex problems that require detailed finite element (FE) modeling to predict the system performance, the computational cost can be very high. This problem can be addressed by using advanced sampling techniques that can provide an accurate estimation of the reliability with a significantly lower number of samples. Another approach is to use surrogate models to establish an accurate approximation of the complex system behavior. These models can provide statistically equivalent results of a complex simulation model, with no known closed-form solution, through a limited number of original model executions. The proposed research focuses on developing probabilistic approaches for the performance assessment of civil and marine structures using machine-learning-assisted MCS. In this approach, machine learning is used to generate a surrogate model of the system response and is next integrated into the MCS to quantify the failure probability of the structure. Sensitivity analysis is conducted to identify the key contributing variables that significantly affect the system response. This process helps reduce the number of random variables associated with the problem resulting in a more efficient probabilistic simulation process. The developed approach was applied to solve two major research problems in civil and marine engineering: (a) reliability quantification of eccentrically loaded steel connections employing both welds and bolts for force transfer and (b) characterizing the crack propagation in stiffened panels and quantifying the reliability of ship hulls under realistic loading conditions.
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.