Stochastic Multi-Objective Optimization-Based Life Cycle Cost Analysis for New Construction Materials and Technologies
Jingqin Gao, Kaan Ozbay, Hani Nassif, Onur Kalan
Civil and Urban Engineering
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.
Transportation Research Record
Dive into the research topics of 'Stochastic Multi-Objective Optimization-Based Life Cycle Cost Analysis for New Construction Materials and Technologies'. Together they form a unique fingerprint.
Life cycle Engineering & Materials Science 91%
Costs Engineering & Materials Science 40%
Bridge decks Engineering & Materials Science 20%
Patient rehabilitation Engineering & Materials Science 16%
Evolutionary algorithms Engineering & Materials Science 16%
Deterioration Engineering & Materials Science 15%
Sustainable development Engineering & Materials Science 15%
Productivity Engineering & Materials Science 14%
Gao, J., Ozbay, K., Nassif, H., & Kalan, O. (2019). Stochastic Multi-Objective Optimization-Based Life Cycle Cost Analysis for New Construction Materials and Technologies. Transportation Research Record. https://doi.org/10.1177/0361198119853578
Stochastic Multi-Objective Optimization-Based Life Cycle Cost Analysis for New Construction Materials and Technologies. / Gao, Jingqin; Ozbay, Kaan; Nassif, Hani; Kalan, Onur.
In: Transportation Research Record, 01.01.2019.
Gao, J, Ozbay, K, Nassif, H & Kalan, O 2019, 'Stochastic Multi-Objective Optimization-Based Life Cycle Cost Analysis for New Construction Materials and Technologies', Transportation Research Record. https://doi.org/10.1177/0361198119853578
Gao J, Ozbay K, Nassif H, Kalan O. Stochastic Multi-Objective Optimization-Based Life Cycle Cost Analysis for New Construction Materials and Technologies. Transportation Research Record. 2019 Jan 1. https://doi.org/10.1177/0361198119853578
Gao, Jingqin ; Ozbay, Kaan ; Nassif, Hani ; Kalan, Onur. / Stochastic Multi-Objective Optimization-Based Life Cycle Cost Analysis for New Construction Materials and Technologies. In: Transportation Research Record. 2019.
@article{dac4d0a719304e0188f7d962db977027,
title = "Stochastic Multi-Objective Optimization-Based Life Cycle Cost Analysis for New Construction Materials and Technologies",
abstract = "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{\textquoteright}s bridge inventory database is used to demonstrate the applicability of the proposed methodology in constructing a planning and management decision-support procedure.",
author = "Jingqin Gao and Kaan Ozbay and Hani Nassif and Onur Kalan",
journal = "Transportation Research Record",
publisher = "US National Research Council",
T1 - Stochastic Multi-Objective Optimization-Based Life Cycle Cost Analysis for New Construction Materials and Technologies
AU - Gao, Jingqin
AU - Ozbay, Kaan
AU - Nassif, Hani
AU - Kalan, Onur
N2 - 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.
AB - 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.
JO - Transportation Research Record
JF - Transportation Research Record