Non-Destructive Characterization of Cured-in-Place Pipe Defects.

Sewage and water networks are crucial infrastructures of modern urban society. The uninterrupted functionality of these networks is paramount, necessitating regular maintenance and rehabilitation. In densely populated urban areas, trenchless methods, particularly those employing cured-in-place pipe technology, have emerged as the most cost-efficient approach for network rehabilitation. Common diagnostic methods for assessing pipe conditions, whether original or retrofitted with-cured-in-place pipes, typically include camera examination or laser scans, and are limited in material characterization. This study introduces three innovative methods for characterizing critical aspects of pipe conditions. The impact-echo method, ground-penetrating radar, and impedance spectroscopy address the challenges posed by polymer liners and offer enhanced accuracy in defect detection. These methods enable the characterization of delamination, identification of caverns behind cured-in-place pipes, and evaluation of overall pipe health. A machine learning algorithm using deep learning on images acquired from impact-echo signals using continuous wavelet transformation is presented to characterize defects. The aim is to compare traditional machine learning and deep learning methods to characterize selected pipe defects. The measurement conducted with ground-penetrating radar is depicted, employing a heuristic algorithm to estimate caverns behind the tested polymer composites. This study also presents results obtained through impedance spectroscopy, employed to characterize the delamination of polymer liners caused by uneven curing. A comparative analysis of these methods is conducted, assessing the accuracy by comparing the known positions of defects with their predicted characteristics based on laboratory measurements.

The Effect of Specimen Size on Acoustic Emission Parameters and Approximate Position of Defects Obtained during Destructive Testing of Cementitious and Alkali-Activated Degraded Fine-Grained Materials.

Two sizes of test samples were selected to investigate the effect of size on the level of degradation. The smaller test specimens had dimensions of 40 × 40 × 160 mm, and the larger ones had dimensions of 100 × 100 × 400 mm. Both sizes of test specimens were always made of the same mortar. In one case, Blast Furnace Cement was chosen as the binder. In the other case, it was an alkali-activated material as a possibly more environmentally economical substitute. Both types of material were deposited in three degrading solutions: magnesium sulphate, ammonium nitrate and acetic acid. The reference set was stored in a water bath. After six months in the degradation solutions, a static elastic modulus was determined for the specimens during this test, and the acoustic emission was measured. Acoustic emission parameters were evaluated: the number of hits, the amplitude magnitude and a slope from the amplitude magnitude versus time (this slope should correspond to the Kaiser effect). For most of the parameters studied, the size effect was more evident for the more degraded specimens, i.e., those placed in aggressive solutions. The approximate location of emerging defects was also determined using linear localisation for smaller specimens where the degradation effect was more significant. In more aggressive environments (acetic acid, ammonium nitrate), the higher resistance of materials based on alkaline-activated slag was more evident, even in the case of larger test bodies. The experiments show that the acoustic emission results agree with the results of the static modulus of elasticity.