Project: Multi-layer Bayesian life-cycle methodology for the structural assessment of existing concrete structures

2020-10-01 – 2024-09-30

Abstract

Repair and upgrading of structures are becoming increasingly important and are associated to an increasing part of activities in the construction sector. Current state of the art lacks a coupled Bayesian framework that enables to update through-life performance predictions, optimize inspections and interventions and fully exploit knowledge available through information gathered or modelled, limiting the exploitation of the benefits of applying a life-cycle framework.

The project is unique in developing a multi-layer Bayesian approach that enables to perform a coherent through-life performance quantification, life-cycle management and decision making for existing concrete structures taking into account all available information from inspections, monitoring and structural modelling. The fundamental research is related to:
- Bayesian updated modelling of degradation and damage progress with a focus on parameter updating using destructive and NDT measurements, enabling to optimize inspection-based assessment schemes.
- A multifunctional beam and plate FE model for the performance prediction and the optimization of inspections and interventions, enabling to incorporate time-dependent degradation effects, spatial variability and updated information based on investigations.
- A probabilistic framework for the assessment and optimization of combined inspection and monitoring strategies considering uncertainties, exploiting information from spatial degradation and performance modelling.
- A methodology for multi-objective performance-based optimization of maintenance strategies and repair or strengthening interventions in relation to the specified safety level, durability objectives and budgetary constraints.
- A novel integrated BIM-environment for life-cycle management, structuring and integrating all relevant information from inspections, performance simulations and optimization processes, based on linked-data and a conceptual framework for a digital twin approach.