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Abstract: Nondestructive evaluation (NDE) methods have received growing acceptance in many testing tasks in the assessment of concrete infrastructures. Substantial progress in NDE methods for concrete structures can be achieved by discussing the specifics of each testing scenario. Because of the large variety of testing scenarios, the testing tasks must be isolated into subtasks, which can then be solved by NDE methods. A classification scheme for NDE tasks is described and discussed in this paper. Four major groups have been identified: the construction process, the concrete structure, physical or chemical processes, and material properties. For each of these groups, a number of subtasks are described. Typical parameter ranges and Resolution requirements are illustrated and major influencing factors listed. Reference specimens may be designed to be used for performance evaluation, validation, and certification of NDE methods. The classifications can also be used to draft a research road map that benefits both the owners of infrastructure and the instrument developers.
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.
The integral collection of information such as strains, cracks, or temperatures by ultrasound offers the best prerequisites to monitor structures during their lifetime. In this paper, a novel approach is proposed which uses the collected information in the coda of ultrasonic signals to infer the condition of a structure. This approach is derived from component tests on a reinforced concrete beam subjected to four-point bending in the lab at Ruhr University Bochum. In addition to ultrasonic measurements, strain of the reinforcement is measured with fiber optic sensors. Approached by the methods of moment-curvature relations, the steel strains serve as a reference for velocity changes of the coda waves. In particular, a correlation between the relative velocity change and the average steel strain in the reinforcement is derived that covers 90% of the total bearing capacity. The purely empirical model yields a linear function with a high level of accuracy (R 2 =0.99, R2=0.99, RMSE≈90μ
RMSE≈90μ strain).