TY - JOUR
T1 - Life Cycle Assessment Framework for the U.S. Bridge Inventory
AU - Babanajad, Saeed
AU - Bai, Yun
AU - Wenzel, Helmut
AU - Wenzel, Moritz
AU - Parvardeh, Hooman
AU - Rezvani, Ali
AU - Zobel, Robert
AU - Moon, Franklin
AU - Maher, Ali
N1 - Publisher Copyright:
© National Academy of Sciences: Transportation Research Board 2018.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - The effective management of bridges requires a good understanding of their life expectancies. Improved prediction of bridge service life is required to be developed in order to better understand bridge deterioration and to find more effective maintenance and repair strategies. These models are integral components of the Long-Term Bridge Performance Program (LTBP), a 20-year research effort initiated by the U.S. Federal Highway Administration (FHWA) to improve the understanding of bridge performance. In this paper, the development of a life expectancy model framework, as part of the research effort in this program, is presented. The framework is established based on a semi-probabilistic approach to adherently maintain the advantages of both deterministic and probabilistic techniques. The modeling follows a step-by-step process which incorporates data collected from historical records, training the data, creating a model based on the most suitable approach, and reducing the associated uncertainties. The basic model is first trained by the network of bridge inventory and the uncertainties are reflected by determining lower and upper margins. Then the model is improved by introducing the new knowledge gained from the external attributes influencing the structure. Finally, the condition states of the bridge components are employed directly to refine the model for realistic assessment. The developed model is later automated into the Bridge Portal, the main core of the bridge-performance data warehouse. A detailed example using the Mid-Atlantic cluster bridge inventory data is presented in this paper to illustrate the application of the method described above.
AB - The effective management of bridges requires a good understanding of their life expectancies. Improved prediction of bridge service life is required to be developed in order to better understand bridge deterioration and to find more effective maintenance and repair strategies. These models are integral components of the Long-Term Bridge Performance Program (LTBP), a 20-year research effort initiated by the U.S. Federal Highway Administration (FHWA) to improve the understanding of bridge performance. In this paper, the development of a life expectancy model framework, as part of the research effort in this program, is presented. The framework is established based on a semi-probabilistic approach to adherently maintain the advantages of both deterministic and probabilistic techniques. The modeling follows a step-by-step process which incorporates data collected from historical records, training the data, creating a model based on the most suitable approach, and reducing the associated uncertainties. The basic model is first trained by the network of bridge inventory and the uncertainties are reflected by determining lower and upper margins. Then the model is improved by introducing the new knowledge gained from the external attributes influencing the structure. Finally, the condition states of the bridge components are employed directly to refine the model for realistic assessment. The developed model is later automated into the Bridge Portal, the main core of the bridge-performance data warehouse. A detailed example using the Mid-Atlantic cluster bridge inventory data is presented in this paper to illustrate the application of the method described above.
UR - https://www.scopus.com/pages/publications/85060943104
UR - https://www.scopus.com/pages/publications/85060943104#tab=citedBy
U2 - 10.1177/0361198118781396
DO - 10.1177/0361198118781396
M3 - Article
AN - SCOPUS:85060943104
SN - 0361-1981
VL - 2672
SP - 82
EP - 92
JO - Transportation Research Record
JF - Transportation Research Record
IS - 12
ER -