Potential mapping: reliability assessment and integration of the inspection data in degradation models

• Applicant: Professor Dr.-Ing. Christoph Gehlen
• Subject Area: Construction Material Sciences, Chemistry, Building Physics
• Term: from 2015 to 2017
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 274637702

In order to enable mobility of our modern civilization the maintenance of the ageing infrastructure requires knowledge, experience and professionalism of the judging engineer. In addition use of inspection data is needed to support decision-making and to reduce the inspection effort.The objective of this research project is to support the engineer with a tool that enables a reliable and spatially differentiated identification of corroding areas in reinforced concrete structures. Nowadays, probabilistic models for calculating the service life of reinforced concrete structures including initiation and propagation period of reinforcement corrosion are available. These models solely provide localized information, in other words in a structure assessed the failure probability is assumed to be equal at any location. Thus, spatial variability of material resistances and exposure are ignored. With the help of potential mapping spatial differentiated information about corrosion condition in reinforced concrete structure is available. Up to now, this spatial information is not used and is therefore lost.The reliability of the inspection method has to be known to enable a probabilistic update of the service life prediction. First the main factors influencing potential fields and their correlation will be analyzed. Afterwards, the reliability of potential mapping will be expressed through the evaluation of the probability of detection (POD). Then, the spatial differentiated update of the service life prediction by potential mapping data can be performed. This evaluation algorithm will be validated on suitable existing reinforced concrete structures.Due to this algorithm a condition-based inspection and maintenance planning can be done for the actual first time. Inspection data and probabilistic service life prediction can be combined mathematically to give an accurate picture of the condition of investigated reinforced concrete structures.

• DFG Programme: Research Grants