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The measurement of electrical resistivity is widely used in civil engineering for structural inspection and material characterisation. Measuring devices employ electrodes to apply current and measure potential differences. The resistivity of concrete structures is determined from the applied current, measured voltage, and a geometric factor influenced by the structure’s geometry, electrodes spacing and positioning, and the presence of rebars. Several methods exist to determine this factor, including analytical approaches, finite element modelling, and experimental procedures. However, the latter two approaches are often time-consuming, and in practice—particularly during on-site inspections—a rapid evaluation of the geometric factor is required to adapt the measurement strategy in real time. This study introduces a fast approximation model for computing the geometric factor in four-probe devices using the Wenner array. The model is based on a neural network trained on a database generated by finite element simulations. It covers cylinders and prisms of various sizes, with and without rebars, and generalises results from finite-size slabs to large-scale structures. The model’s predictions were validated against published data. Furthermore, a graphical user interface (GUI) application has been developed to enable rapid, real-time evaluation of geometric factors, supporting researchers and inspectors in non-destructive testing (NDT).
Karam et al. (Fri,) studied this question.