Improvement of the Inspection Interval of Highway Bridges through Predictive Models of Deterioration

Bridges have substantial significance within the transport system, considering that their functionality is essential for countries’ social and economic development. Accordingly, a superior level of safety serviceability must be reached to ensure operating status bridge network. On account, recent collapses road bridges led technical–scientific community society reflect on effectiveness management. in network are likely share coinciding environmental conditions but may subjected distinct structural deterioration processes over time depending age, location, type, other aspects. This variation usually not considered management predictions. For instance, Brazilian standards consider constant inspection periodicity, regardless bridges’ singularities. Consequently, it helpful pinpoint split into classes sharing equivalent trends obtain more precise prediction improve frequency inspections. work presents representative database network, including most relevant data obtained from The was used calibrate two independent predictive models (Markov artificial neural network). calibrated model employed simulate different scenarios, resulting significant insights periodicity. As result, bridge’s location accounting differentiation exposure critical point when analyzing process. Finally, degradation developed following proposed procedure deliver reliable forecast compared single without parameter analysis. These assist decision process system (BMS).