Underground Utilities Imaging and Diagnosis
| Autor: | Wallace W.L. Lai |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Point (typography)
Process (engineering) 020208 electrical & electronic engineering 02 engineering and technology 010502 geochemistry & geophysics 01 natural sciences Data science Field (computer science) Variety (cybernetics) Task (project management) Visual inspection 0202 electrical engineering electronic engineering information engineering Robot 0105 earth and related environmental sciences TRACE (psycholinguistics) |
| Zdroj: | Urban Informatics ISBN: 9789811589829 |
| DOI: | 10.1007/978-981-15-8983-6_24 |
| Popis: | The invisible and congested world of underground utilities (UU) is an indispensable mystery to the general public because their existence is invisible until problems happen. Their growth aligns with the continuous development of cities and the ever-increasing demand for energy and quality of life. To satisfy a variety of modern requirements like emergency or routine repair, safe dig and excavation, monitoring, maintenance, and upscaling of the network, two basic tasks are always required. They are mapping and imaging (where?), and diagnosis (how healthy?). This chapter gives a review of the current state of the art of these two core topics, and their levels of expected survey accuracy, and looks forward to future trends of research and development (Sects. 24.1 and 24.2). From the point of view of physics, a large range of survey technologies is central to imaging and diagnosis, having originated from electromagnetic- and acoustic-based near-surface geophysical and nondestructive testing methods. To date, survey technologies have been further extended by multi-disciplinary task forces in various disciplines (Sect. 24.3). First, it involves sending and retrieving mechanical robots to survey the internal confined spaces of utilities using careful system control and seamless communication electronics. Secondly, the captured data and signals of various kinds are positioned, processed, and in the future, pattern-recognized with a database to robustly trace the location and diagnose the conditions of any particular type of utilities. Thirdly, such a pattern-recognized database of various types of defects can be regarded as a learning process through repeated validation in the laboratory, simulation, and ground-truthing in the field. This chapter is concluded by briefly introducing the human-factor or psychological and cognitive biases, which are in most cases neglected in any imaging and diagnostic work (Sect. 24.4). In short, the very challenging nature and large demand for utility imaging and diagnostics have been gradually evolving from the traditional visual inspection to a new era of multi-disciplinary surveying and engineering professions and even towards the psychological part of human–machine interaction. |
| Databáze: | OpenAIRE |
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