ABSTRACT
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.
ABSTRACT
The development of smart materials is a basic prerequisite for the development of new technologies enabling the continuous non-destructive diagnostic analysis of building structures. Within this framework, the piezoresistive behavior of fly ash geopolymer with added carbon black under compression was studied. Prepared cubic specimens were doped with 0.5, 1 and 2% carbon black and embedded with four copper electrodes. In order to obtain a complex characterization during compressive loading, the electrical resistivity, longitudinal strain and acoustic emission were recorded. The samples were tested in two modes: repeated loading under low compressive forces and continuous loading until failure. The results revealed piezoresistivity for all tested mixtures, but the best self-sensing properties were achieved with 0.5% of carbon black admixture. The complex analysis also showed that fly ash geopolymer undergoes permanent deformations and the addition of carbon black changes its character from quasi-brittle to rather ductile. The combination of electrical and acoustic methods enables the monitoring of materials far beyond the working range of a strain gauge.
ABSTRACT
The electrical properties of concrete are gaining their importance for the application in building construction. In this study, graphite powder was added to alkali-activated slag mortar as an electrically conductive filler in order to enhance the mortar's conductive properties. The amount of graphite ranged from 1% to 30% of the slag mass. The effect of the graphite powder on the resistivity, capacitance, mechanical properties, and microstructure of the composite was investigated. Selected mixtures were then used for the testing of self-sensing properties under compressive loading. The results show that the addition of an amount of graphite equal to up to 10% of the slag mass improved the electrical properties of the alkali-activated slag. Higher amounts of filler did not provide any further improvement in electrical properties at lower AC frequencies but caused a strong deterioration in mechanical properties. The best self-sensing properties were achieved for the mixture with 10 wt% of graphite, but only at low compressive stresses of up to 6 MPa.
