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 and serviceability must be reached to ensure the operating status of the bridge network. On that account, the recent collapses of road bridges have led the technical–scientific community and society to reflect on the effectiveness of their management. Bridges in a network are likely to share coinciding environmental conditions but may be subjected to distinct structural deterioration processes over time depending on their age, location, structural type, and other aspects. This variation is usually not considered in the bridge management predictions. For instance, the Brazilian standards consider a constant inspection periodicity, regardless of the bridges' singularities. Consequently, it is helpful to pinpoint and split the bridge network into classes sharing equivalent deterioration trends to obtain a more precise prediction and improve the frequency of inspections. This work presents a representative database of the Brazilian bridge network, including the most relevant data obtained from inspections. The database was used to calibrate two independent predictive models (Markov and artificial neural network). The calibrated model was employed to simulate different scenarios, resulting in significant insights to improve the inspection periodicity. As a result, the bridge's location accounting for the differentiation of exposure was a critical point when analyzing the bridge deterioration process. Finally, the degradation models developed following the proposed procedure deliver a more reliable forecast when compared to a single degradation model without parameter analysis. These more reliable models may assist the decision process of the bridge management system (BMS). ; : This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 769255. ...