WP1 - Condition assessment and damage modelling
Main researcher: ir. Constantijn Martens
Affiliation: KU Leuven
Title PhD dissertation: Condition assessment and damage modelling of corroding reinforced concrete structures through Bayesian coupled learning model
Supervisors: Prof. dr. ir.-arch. Els Verstrynge, prof. dr. ir. Robby Caspeele
Work package description
As inspection data may concern data on specific point locations (e.g. core drilling, cover thickness, chloride content), grid data with a specific mesh size (e.g. potential mapping ) as well as zonal data (visual damage mapping), techniques are needed that allow for the integration of data sets with different spatial density. This will be achieved by modelling the damage by a random field. This results in zones with specific damage levels and related uncertainties.
A scheme for hierarchical classification of input data will be set up, dependent on their importance in the modelling and decision process, to enable dealing with lack of data and related uncertainties as encountered in real cases.
The identified damage levels will be linked to mechanical effects (rebar section reduction, bond loss, and concrete cracking). In this project, a practical approach is sought with the development of a Bayesian framework, using empirical relations and accounting for uncertainties (material, model, and measurement). In the adopted Bayesian methodology, the prior uncertainty of the damage model is updated to a posterior uncertainty on the basis of inspections.
Model uncertainties will be derived on the basis of comparisons with a limited number of experimental tests which are performed under controlled conditions (see WP4) and selected on-site case studies which might exhibit combined effects of mechanical and environment loading (cf. WP7). The latter will allow to assess model uncertainties in real conditions, by means of periodic inspection and vibration-based monitoring (WP3). The linking of the corrosion damage to mechanical effects leads to a parametrization of damage which is used as input parameters for performance prediction (WP2).
This workpackage aims for the following Deliverables (D) and Milestones (M):
- D1.1 – Report of an overview of available corrosion propagation models, related inspection techniques and their suitability for incorporation into the developed methodology
- D1.2 – Report which describes the hierarchical classification of input data and related uncertainties to deal with lack of inspection data in practical situations
- D1.3 – Algorithm for the Bayesian updating of damage processes on the basis of additional data with different spatial distribution and density
- M1.1 – Implemented techniques for integration of data sets with different spatial distribution and data density into the updating procedure (required input for WPs 2, 3 and 5)
- M1.2 – Implemented procedure to predict spatial distribution of structural cross-sectional properties for the performance prediction models based on the inspection data and developed degradation/damage models (required input for WP2)