Stochastic Multi-Objective Optimization-Based Life Cycle Cost Analysis for New Construction Materials and Technologies

The sustainability of transportation infrastructure depends on the adoption of new construction materials and technologies that can potentially improve performance and productivity. However, most agencies would like to evaluate these new materials and technologies at both the project and network levels before replacing the traditional ones. It also remains a challenge to reliably estimate the costs and lifetime performance of new construction materials and technologies because of limited implementation data. To address these issues, this paper presents a comprehensive bottom-up methodology based on Life Cycle Cost Analysis (LCCA) to integrate project- and network-level analysis that can fast-track the acceptance of new materials or technologies. Hypothesized improvement rates are applied to the deterioration functions of existing materials to represent the expected improved performance of a new material compared with a conventional material with relatively similar characteristics. This new approach with stochastic treatment allows us to probabilistically evaluate new materials with limited data for their future performance. Feasible maintenance and rehabilitation schedules are found for each facility at the project level and near-optimal investment strategies are identified at the network level by using a metaheuristic evolutionary algorithm while satisfying network-wide constraints. This provides an effective solution to many issues that have not been fully addressed in the past, including the trade-off between multiple objectives, effects of time, uncertainty, and outcome interpretation. A hypothetical bridge deck system from New Jersey’s bridge inventory database is used to demonstrate the applicability of the proposed methodology in constructing a planning and management decision-support procedure.

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    • The Standing Committee on Bridge Management (AHD35) peer-reviewed this paper (19-05920). © National Academy of Sciences: Transportation Research Board 2019.
  • Authors:
    • Gao, Jingqin
    • Ozbay, Kaan
    • Nassif, Hani
    • Kalan, Onur
  • Publication Date: 2019-11

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  • English

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  • Accession Number: 01707985
  • Record Type: Publication
  • Files: NTL, TRIS, TRB, ATRI
  • Created Date: Jun 16 2019 3:04PM