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LIBS is a complementary method to XRF and can detect all elements without the need for vacuum conditions. Automated systems are already commercially available capable of scanning surfaces with a resolution of up to 0.1 mm within a few minutes. In addition to possible applications in R&D, LIBS is also used for practical applications in building materials laboratories and even on-site.
In view of ageing infrastructure facilities, a reliable assessment of the condition of concrete structures is of increasing interest. For concrete structures, the ingress of potential harmful ions is affecting the serviceability and eventually structural performance. Pitting corrosion induced by penetrating chlorides is the dominant deterioration mechanism. Condition assessment based on frequently performed chloride profiling can be useful to identify the extent and evolution of chloride ingress. This could prove to be more economical than extensive repairs, especially for important infrastructure facilities.
Currently the most common procedure for determining the chloride content is wet chemical analysis with standard resolution of 10 mm. The heterogeneity is not considered. LIBS is an economical alternative for determining the chloride content at depth intervals of 1 mm or less. It provides 2D distributions of multiple elements and can locate spots with higher concentrations. The results are directly correlated to the mass of binder and can also be performed on-site with a mobile LIBS-System.
The application of a LIBS-system is presented. Calibration is required for quantitative analysis. Concrete cores were drilled, sliced and analyzed to determine the 2D-distribution of harmful elements. By comparing the chloride ingress and the carbonation, the interaction of both processes can be visualized in a measurement that takes less than 10 minutes for a 50 mm x 100 mm drill core.
A leaflet on the use of LIBS for the chloride ingress assessment has been completed.
Machine learning based multi-sensor fusion for the nondestructive testing of corrosion in concrete
(2020)
Half-cell potential mapping (HP) is the most popular non-destructive testing method (NDT) for locating corrosion damage in concrete. It is generally accepted that HP is susceptible to environmental factors caused by salt-related deterioration, such as different moisture and chloride gradients. Additional NDT methods are able to identify distinctive areas but are not yet used to estimate more accurate test results. We present a Supervised Machine Learning (SML) based approach to data fusion of seven different signal features to obtain higher quality information. SMLs are methods that explore (or learn) relationships between different (sensor) data from predefined data labels. To obtain a representative, labelled data set we conducted a comprehensive experiment simulating the deterioration cycle of a chloride exposed device in the laboratory. Our data set consists of 18 measurement campaigns, each containing HP, Ground Penetrating- Radar, Microwave Moisture and Wenner resistivity data. We compare the performance of different ML approaches. Many outperform the best single method, HP. We describe the intrinsic challenges posed by a data-driven approach in NDT and show how future work can help overcome them.