Development of a Risk Assessment Module for Bridge Management Systems in New Jersey

Bridges are critical for the mobility of our society and its economic growth. Available funds for bridge repair, maintenance, and rehabilitation are limited. The Moving Ahead for Progress in the 21st Century Act (MAP-21) introduced several new parameters for improving the management of bridge assets, such as bridge element evaluation, life-cycle analysis, and risk-based performance indicators. Risk-based methods account for the uncertainties embedded into engineering variables and long-term evaluations. The objective of this paper is to identify, assess, and quantify structural risk components to bridges using probabilistic risk methodologies and data from the National Bridge Inventory database. The aim is to simplify the implementation of risk-based ranking procedures into bridge management system packages according to the MAP-21 vision. Therefore, machine learning techniques are employed to facilitate the introduction of probabilistic risk methods into bridge management systems. The procedure is described for seven hazards that are pertinent to bridges in New Jersey: overloading, fatigue, seismic, flooding, scour, vehicle and vessel collision. Risk values are computed in monetary terms to homogenize the comparison among bridges for different hazards. The analysis is performed on 5,534 bridges, showing that seismic events and fatigue resulting from truck overloading are the most dominant hazards in New Jersey, for which about 97.0% and 29.0% of bridges show some level of risk. The main limitation of the proposed framework is the lack of accurate data from bridge inventories necessary to thoroughly perform a fully structural probabilistic analysis of bridges and to minimize engineering judgment.

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A A Review of Bridge Management System: Deterioration Models

Asian Journal of Engineering and Technology ◽

10.24203/ajet.v8i2.6263 ◽

2020 ◽

Vol 8 (2) ◽

Author(s):

Jean De Dieu Gatesi ◽

Joao Agostinho Chingui ◽

Ergashev Botir

Keyword(s):

Conservation Status ◽

Developed Countries ◽

The State ◽

Management Systems ◽

Bridge Management ◽

Bridge Management System ◽

Managerial Performance ◽

Explanatory Variables ◽

The Road ◽

Bridge Management Systems

In the last decades, in a large part of developed countries, a strong investment was made in the construction of infrastructures, namely bridges. However, the state of the road bridges changes with age due to several factors, which cause a progressive worsening of their degradation and a consequent decrease in the strength of the structure, if measures are not implemented that somehow cancel or counteract these effects, being of great importance correct management of the conservation status of these works of art. Good managerial performance is essential especially in view of the limited financial resources or under the bias of their best use. Through computerized bridge management systems or “Bridge Management Systems”, deterioration rates are introduced through algorithms that may vary according to the type of structure, its location, constituent material, or even the environment in which it is inserted, providing important subsidies for decision making. Deterioration Models that may be deterministic, such as least squares-based regression, or probabilistic, such as Markov chains are considered in this review paper. Other methodologies use artificial intelligence processes (natural algorithms or neural networks). The objective of the article is to describe the existing techniques, evaluating their limitations, specificities, and potential. Based on the awareness of the importance of deterioration models, it is possible to verify the fundamental role of routine inspections for the management of construction parks. The inspection of bridges can develop beyond the simple verification of the condition of the works in the present, providing subsidies for a better understanding of the behavior of these works over time. In the same way, inspection methods could provide for the collection of data relevant to the manager's knowledge containing information about possible decisive agents in the deterioration rates. In the long term, the development and inclusion of explanatory variables in bridge management could bring great benefits to the industry. The development of mechanistic models used in conjunction with deterioration models could provide accurate results in predicting the state of condition. In addition, the development of these models would provide benefits in understanding the mechanisms of deterioration of the bridge components.

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