No abstract

Data processing techniques for Ground Penetrating Radar (GPR) image mining provide essential information to optimize maintenance management of Water Supply Systems (WSSs). These techniques aim to elaborate on radargrams in order to produce meaningful graphical representations of critical buried components of WSSs. These representations are helpful non-destructive evaluation tools to prevent possible failures in WSSs by keeping them adequately monitored. This paper proposes an integrated multi-criteria decision making (MCDM) approach to prioritize various data processing techniques by means of ranking their outputs, namely their resulting GPR image representations. The Fuzzy Analytic Hierarchy Process (FAHP) is applied to weight various evaluation criteria, with the purpose of managing vagueness and uncertainty characterizing experts' judgments. Then, the ELimination Et Choix Traduisant la RÉalité III (ELECTRE III) method is used to obtain the final ranking of alternatives. A real case study, focusing on a set of four GPR images as outputs of different data processing techniques, is presented to prove the usefulness of the proposed hybrid approach. In particular, the GPR images are ranked according the evaluation of five criteria namely visualization, interpretation, identification of features, extraction of information and affordability. The findings offer a structured support in selecting the most suitable data processing technique(s) to explore WSS underground. In the presented case, two options, namely the variance filter and the subtraction methods, offer the best results.

This paper presents some aspects regarding time propagation of underground water leakage in controlled laboratory conditions using a drilled PVC pipe and interpreting ground penetrating radar (GPR) images. GPR pre-processed images are interpreted for an easy identification and extraction of surfaces and volumes of water leakage. Finally, the temporal evolution of a water leak is shown using 3D models based on interpretation of GPR images. Water volumes obtained using this approach can be easily observed by personnel who lack highly specialized training in the analysis of raw data. The results of this study are promising and can help develop techniques to validate non-destructive models for the identification, distribution, and prediction of water leaks in water supply systems using GPR.

In this paper, a ground penetrating radar (GPR) is used as a non-destructive method to assess the buried elements of water supply systems (WSSs). The aim is the detection of various pipe materials (such as plastic and metallic, among others), and the identification of other important aspects (e.g. water leakage). This work seeks to use the visualization advantages of the subsoil characteristics provided by pre-processed GPR images. These features, which are represented as anomalies into the images, are extracted and merged to generate 3D models. The 3D representations obtained facilitate elucidation by personnel non-highly skilful in the interpretation of data from non-destructive techniques. The work is performed on GPR images of WSS pipes taken from strategic locations of urban environments. The goal is to promote the use of these technologies in the WSSs intended to generate relevant information that allows the adequate and dynamic technical management of these systems. The results and analyses are presented in this paper.

This work focuses on the use of easy-to-apply procedures that allow rapid visualization of components of water supply systems (WSSs) by non-highly qualified personnel. We use a methodology that does not alter the conditions and characteristics of the environment (nondestructive methods), specifically the study of images obtained with ground penetrating radar (GPR). The study is based on the analysis and interpretation of the wave amplitude, then applying a series of image corrections, so that the display and handling of data is improved. The results are promising as a subjective and repeatable methodology to visualize buried pipes efficiently. The goal is to generate know-how to be able to train intelligent systems for the characterization of components of WSSs.