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This book grew out of a course on polymer coatings held by the author at the university. It shall serve as an introduction to the wide field of polymer coatings. It is not intended to cover completely all facts on polymer coatings.
All chapters are well structured. Selected figures and formulas illustrate the content and contribute to a good understandability. At the end of each chapter some hints for further reading are provided.
Finally, the book fulfills its intention to introduce into the topic by providing basics on science and technology of polymer coatings, suitable for chemists in industry, materials scientists, polymer chemists and physical chemists.
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.