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.

• Keywords
• cured-in-place pipes, machine learning, non-destructive testing, pipe defects, polymers, retrofitting,
• Publication type
• Journal Article MeSH

The study of the resistance of plain concrete to high temperatures is a current topic across the field of civil engineering diagnostics. It is a type of damage that affects all components in a complex way, and there are many ways to describe and diagnose this degradation process and the resulting condition of the concrete. With regard to resistance to high temperatures, phenomena such as explosive spalling or partial creep of the material may occur. The resulting condition of thermally degraded concrete can be assessed by a number of destructive and nondestructive methods based on either physical or chemical principles. The aim of this paper is to present a comparison of nondestructive testing of selected concrete mixtures and the subsequent classification of the condition after thermal degradation. In this sense, a classification model based on supervised machine learning principles is proposed, in which the thermal degradation of the selected test specimens are known classes. The whole test set was divided into five mixtures, each with seven temperature classes in 200 °C steps from 200 °C up to 1200 °C. The output of the paper is a comparison of the different settings of the classification model and validation algorithm in relation to the observed parameters and the resulting model accuracy. The classification is done by using parameters obtained by the acoustic NDT Impact-Echo method and image-processing tools.

• Keywords
• Impact-Echo, concrete, high temperatures, image analysis, machine learning, nondestructive testing, resonance method,
• Publication type
• Journal Article MeSH

Historical buildings and monuments are largely made of brickwork. These buildings form the historical and artistic character of cities, and how we look after them is a reflection of our society. When assessing ceramic products, great emphasis is placed on their mechanical properties, whilst their durability is often neglected. However, the durability or resistance to weathering of masonry elements is just as important as their mechanical properties. Therefore, this work deals with predicting the durability of solid-fired bricks before they are used when reconstructing monuments and historical buildings. Durability prediction is assessed by identifying defects in the material's internal structure. These faults may not be visible on the element's surface and are difficult to detect. For this purpose, non-destructive electroacoustic methods, such as the resonant pulse method or the ultrasonic pulse method, were used. Based on an analysis of the initial and residual mechanical properties after freezing cycles, four durability classes of solid-fired bricks were determined. This work aimed to find a way to predict the durability (lifetime) of an anonymous solid-fired brick, expressed in terms of the number of freeze cycles the brick would last, based on non-destructive measurements.

• Keywords
• defects in the internal structure, machine learning, material durability, non-destructive testing, resonant pulse method, solid fired brick,
• Publication type
• Journal Article MeSH