Smart Sensing technologies can play an important role in the conditional assessment of concrete sewer
pipe linings. In the long-term, the permeation of acids can
deteriorate the pipe linings. Currently, there are no proven
sensors available to non-invasively estimate the depth of acid
permeation in real-time. The electrical resistivity measurement
on the surface of the linings can indicate the sub-surface acid
moisture conditions. In this study, we consider acid permeated
linings as a two resistivity layer concrete sample, where the
top resistivity layer is assumed to be acid permeated and the
bottom resistivity layer indicates normal moisture conditions.
Firstly, we modeled the sensor based on the four-probe Wenner
method. The measurements of the developed model were
compared with the previous studies for validation. Then, the
sensor model was utilized to study the effects of electrode
contact area, electrode spacing distance and two resistivity
layered concrete on the apparent resistivity measurements. All
the simulations were carried out by varying the thickness of
top resistivity layer concrete. The simulation study indicated
that the electrode contact area has very minimal effects on
apparent resistivity measurements. Also, an increase in apparent
resistivity measurements was observed when there is an increase
in the distance of the electrode spacing. Further, a machine
learning approach using Gaussian process regression modeling
was formulated to estimate the depth of acid permeated layer
EVALUATION OF REINFORCING STEEL OF CHLORIDE INDUCED CORROSION USING HALF-CELL POTENTIAL AND CONCRETE RESISTIVITY MEASUREMENTS
