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Often 3D‐printing and additive manufacturing are mentioned as the 3rd industrial revolution. In this conjunction this book provides a brief overview on additively manufactured metal pieces.
This book fulfills its intension to serve as an educational guide, providing a holistic picture encompassing science, technology and applications for the real‐life use of Metal Additive Manufacturing.
In 2019 CRC Press published the "Steel‐Reinforced Concrete Structures" as a paperback. This book does not vary in terms of content from the hardback publication of 2008. But it is first reviewed here in corrosion news. The book provides eight of nine chapters in which the author discusses technical aspects of assessment and repair of steel‐reinforced concrete structures.
To sum up, this book is recommended for students and people involved with the assessment and repair of reinforced‐concrete structures, who want to get a quick introduction into the different aspects of the field. Readers also learn much about the economic interactions besides the topic. However, for experienced technicians and corrosion experts, this book may not be exact enough in both the electrochemical description of deterioration processes and the practical aspects of repair and control methods. Despite that, the discussions of the author are scientifically correct.
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.