Analytische Chemie
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In the field of non-destructive testing (NDT) in civil engineering, a large number of measurement data are collected. Although they serve as a basis for scientific analyses, there is still no uniform representation of the data. An analysis of various distributed data sets across different test objects is therefore only possible with high manual effort.
We present a system architecture for an integrated data management of distributed data sets based on Semantic Web technologies. The approach is essentially based on a mathematical model - the so-called ontology - which represents the knowledge of our domain NDT. The ontology developed by us is linked to data sources and thus describes the semantic meaning of the data. Furthermore, the ontology acts as a central concept for database access. Non-domain data sources can be easily integrated by linking them to the NDT construction ontology and are directly available for generic use in the sense of digitization. Based on an extensive literature research, we outline the possibilities that this offers for NDT in civil engineering, such as computer-aided sorting, analysis, recognition and explanation of relationships (explainable AI) for several million measurement data.
The expected benefits of this approach of knowledge representation and data access for the NDT community are an expansion of knowledge through data exchange in research (interoperability), the scientific exploitation of large existing data sources with data-based methods (such as image recognition, measurement uncertainty calculations, factor analysis, material characterization) and finally a simplified exchange of NDT data with engineering models and thus with the construction industry.
Ontologies are already the core of numerous intelligent systems such as building information modeling or research databases. This contribution gives an overview of the range of tools we are currently creating to communicate with them.
This talk demonstrates the results of the IGSTC-project entitled "NDT-Data Fusion".
Project approach:
Nondestructive testing (NDT) of concrete buildings allows to coordinate efficient repair measures. Multi-sensor platforms collect large data sets. Nevertheless, data analysis is typically performed manually. Data Fusion uses the full potential of a multi-sensory data set in order to:
improve information quality (reliability, robustness, accuracy, clarity, completeness) and enables automated algorithm based data analysis.
We present the project achievements, namely:
- Development of building scanner system for multisensory NDT
- Laboratory multi sensor investigations
- Development of data fusion concept for honeycombing and pitting corrosion
- Field testing
Half-cell potential mapping (HP) is the most popular nondestructive test (NDT)-method for the localization of corrosion damage in concrete. It is generally recognized, that HP is prone to the environmental factors that arise from salt induced deterioration, such as varying moisture and chloride gradients. Additional NDT-methods are capable to determine distinctive areas, but cannot yet be used to estimate more accurate testing results. We introduce a supervised machine learning (SML) based approach for data fusion to make use of the additional sensor information. SML are methods that explore relations between different (sensor) data from predefined data labels. We use a simple linear classifier named logistic regression to distinguish defect and intact areas. The test performance improves drastically compared to the best single method, HP. In order to generate representative, labeled data we conducted a comprehensive experiment that simulates the deterioration-cycle of a chloride-exposed building part in the lab. Our data set consist of 18 measurement campaigns, each containing HP-, ground-penetrating-radar-, microwave-moisture-, and Wenner-resistivity-data. We detail the challenges that arise with a data driven approach in NDT and how we addressed them.