A A Review of Bridge Management System: Deterioration Models
Keywords:Bridge Management; Deterioration Models; Markov chains
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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Copyright (c) 2020 Jean De Dieu Gatesi, Joao Agostinho Chingui, Ergashev Botir
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