Probabilistic-based assessment of existing steel-concrete composite bridges – Application to Sousa River Bridge

Abstract

This paper presents a framework to assess the safety of existing structures, combining deterministic model identification and reliability assessment techniques, considering both load-test and complementary laboratory test results. Firstly, the proposed framework, as well as the most significant uncertainty sources are presented. Then, the developed model identification procedure is described. Reliability methods are then used to compute structural safety, considering the updated model from model identification. Data acquisition, such as that collected by monitoring, non-destructive or material characterization tests, is a standard procedure during safety assessment analysis. Hence, Bayesian inference is introduced into the developed framework, in order to update and reduce the statistical uncertainty. Lastly, the application of this framework to a case study is presented. The example analyzed is a steel and concrete composite bridge.The load test, the developed numerical model and the obtained results are discussed in detail. The use of model identification allows the development of more reliable structural models, while Bayesian updating leads to a significant reduction in uncertainty. The combination of both methods allows for a more accurate assessment of structural safety.

Recycled concrete bridge engineering based on artificial intelligence algorithm

“…When preparing RC, the general proportioning methods are: equal mass substitution method, that is, when preparing RC, calculate the replacement rate and use the recycled coarse aggregate of the quality required to replace the natural aggregate of the same quality; Equal volume replacement method, that is, when preparing RC, calculate the volume of recycled coarse aggregate required by the replacement rate, and use it to replace the natural aggregate of the same volume; When preparing RC, the treatment of recycled coarse aggregate generally includes surface dry saturation method and additional water method to ensure that the mix proportion of RC meets the requirements. Surface dry saturation method is to pre saturate the recycled coarse aggregate before mixing, and place it in the air after soaking for 24 hours until the recycled coarse aggregate reaches the surface dry saturation state; The additional water formula is used to calculate the water required for the recycled coarse aggregate to reach the surface dry saturation state and the mass after the surface dry saturation under the environment without drying conditions, and directly add water to make the recycled aggregate reach the surface dry saturation state [9][10].…”

Steel‐Concrete Composite Bridges: Design, Life Cycle Assessment, Maintenance, and Decision‐Making

“…ese activities can be preventive when related with the design of infrastructures preventing the possibility of damage [126] or corrective, with a repair approach. Focusing on the search in SCCBs, two main trends have been established: repair and renovation [127,128] and the evaluation of bridge conditions [129][130][131].…”

“…e numerical model proposed by the author is capable of assessing the maintenance needs of the bridge by the infrastructure manager. Moreover, Matos et al [131] proposed a model that is capable of introducing data from the bridge condition and uses Bayesian inference [132,133] to reduce the uncertainty of model parameters, allowing stakeholders to take better decisions according to maintenance.…”

Optimal Deep Learning Assisted Design of Socially and Environmentally Efficient Steel Concrete Composite Bridges under Constrained Budgets

“…These activities can be preventive when related with the design of infrastructures preventing the possibility of damage [136] or corrective, with a repair approach. Focusing on the search in SCCBs, two main trends have been established: repair and renovation [137], [138] and the evaluation of bridge conditions [139]- [141].…”

“…The numerical model proposed by the author is capable to assess the maintenance needs of the bridge by the infrastructure manager. Moreover, Matos et al [141] proposed a model that is capable of introducing data from the bridge condition and uses Bayesian inference [142], [143] to reduce the uncertainty of model parameters, allowing stakeholders to take better decisions according to maintenance.…”