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Abusnina, H (2019) Combining engineering and data-driven approaches to model the risk of excavation damage to underground natural gas facilities, Unpublished PhD Thesis, , Rutgers The State University of New Jersey, School of Graduate Studies.
Ertekin, A O (2013) Probabilistic life cycle cost optimization of bridges, Unpublished PhD Thesis, , Rutgers The State University of New Jersey, School of Graduate Studies.
Jawad, D J (2003) Life cycle cost optimization for infrastructure facilities, Unpublished PhD Thesis, , Rutgers The State University of New Jersey, School of Graduate Studies.
- Type: Thesis
- Keywords: genetic algorithms; optimization; economic theory; pavement; traffic; cost optimization; infrastructure management; life cycle; rehabilitation; cost analysis; life cycle cost; risk analysis; Monte Carlo simulation; simulation
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/305312981
- Abstract:
Life cycle cost optimization is one of the most renowned concepts used for achieving sustainable infrastructures. The research presented herein involves the development of a hybrid model (LCCOM) for optimizing life cycle cost in transportation infrastructure, particularly for pavement structures at the project-level. The basic output of the LCCOM is the life cycle strategy for an infrastructure facility that can bring about the highest gains to the society. The LCCOM is formulated as a mixed-integer nonlinear optimization model. The formulation derives its analytical framework from the economic theory of life cycle cost analysis. It integrates an array of engineering and economic models that dynamically interrelate over the life cycle of the facility. These models include pavement performance prediction, traffic analysis, cost and effectiveness of rehabilitation activities, costs to users, accidents costs, agency costs, and discounting models. The LCCOM employs evolutionary algorithms, namely genetic algorithms as the search tool for arriving at the optima. Optimization in the LCCOM is fundamentally driven by the tradeoff between the agency costs and the monetary worth of the societal costs. This intrinsic tradeoff in the LCCOM counterbalances the need for including undue constraints and, hence, enhances the performance of the genetic algorithms to a great extent. A distinct feature of the LCCOM is the pairing of the genetic algorithm as an optimization tool with Monte Carlo simulations as a risk analysis technique. This feature enhances the capabilities of the LCCOM in carrying out the optimization probabilistically and thus takes into account the inherent uncertainties that exist in the assumptions column of the problem. The research presented in this dissertation validates the probabilistic optimization in infrastructure management and opens the door to further explorations of the potentials of this tool. In addition to the development of the LCCOM, other major outcomes of the conducted research include: (1) a concise manuscript that establishes the guidelines for conducting life cycle cost analysis in pavement projects; (2) a meticulous study on how to choose the discount rate when performing life cycle cost analysis (LCCA) for transportation projects; and (3) a back-to-basics analysis of the appropriate discount rate for the evaluation of the emerging intelligent transportation systems.
Yang, C (2023) Reliability-based methodology for design and evaluation of concrete bridge decks, Unpublished PhD Thesis, , Rutgers The State University of New Jersey, School of Graduate Studies.
Yu, Y (2022) Use inspired research in using virtual reality for safe operation of exemplar critical infrastructure systems, Unpublished PhD Thesis, , Rutgers The State University of New Jersey, School of Graduate Studies.