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Thesis defences

PhD Oral Exam - Mohammed Mawlana, Building Engineering

Improving Stochastic Simulation-based Optimization for Selecting Construction Method of Precast Box Girder Bridges


Date & time
Tuesday, July 7, 2015
1 p.m. – 4 p.m.
Cost

This event is free

Organization

School of Graduate Studies

Contact

Sharon Carey
514-848-2424 ext. 3802

Wheel chair accessible

Yes

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Highway infrastructures in North America are approaching or have surpassed their service life; as a result, a large amount of reconstruction work is expected on existing highways. Current practices in the construction industry suggest that urban highway construction projects often overrun in budget and time. Bridges are crucial elements of urban highways, therefore, efficient planning of the construction of bridges is deemed necessary. Bridge construction operations are characterized as equipment-intensive, repetitive, have cyclic nature and involve high uncertainties. Without selecting the best construction method and the optimum number of equipment and crews, projects will take longer and cost more than necessary.

The efficiency of stochastic simulation-based optimization is affected by: (1) the number of solutions generated by the optimization algorithm; and (2) the number of simulation replications required for each solution to achieve a desirable statistical estimate. As a result, a compromise between the accuracy of the estimate of a solution and the optimality of that solution must be made. Moreover, comparing the performance of different Pareto solutions based on the mean values is not accurate because the means of the two objectives (i.e., cost and time) are not always the means of the joint distribution of the two objectives. Finally, the resulting Pareto solutions are not necessarily achievable.

The objectives of this research are: (1) to develop a stochastic simulation-based multi-objective optimization model for the construction of precast concrete box girder bridges that is capable of finding near optimum construction scenarios and simultaneously minimizing the project’s total duration and cost; (2) to increase the quality of the optimum solutions, increase the confidence in the optimality of the optimum solutions, and reduce the computation time required for performing the stochastic simulation-based multi-objective optimization by incorporating variance reduction techniques; (3) to further reduce the computation time of the optimization by performing parallel computing on a single multi-core processor; and (4) to reduce project risk and provide the decision makers with more accurate and useful information to plan and manage their projects using the joint probability.

In this research, a model is proposed that is capable of selecting the optimum construction method of precast box girder bridges and the number of equipment and crews that will minimize project cost, duration, or project cost and duration. Moreover, this model takes the different overtime policies and the settings of the casting yard into consideration. Furthermore, the proposed model aims to minimize the number of simulation replications while maintaining a desirable estimate of the candidate solutions. Additionally, the proposed model is implemented using parallel computing in order to reduce the computation time. Also, a new method that can accurately estimate the objectives’ values along with their joint probability is presented. This method provides achievable Pareto solutions.

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