Deep learning-assisted automated sewage pipe defect detection for urban water environment management

Sun, Lianpeng; Zhu, Jin; Tan, Jinxin; Li, Xianfeng; Li, Ruohong; Deng, Huanzhong; Zhang, Xinyang; Liu, Bingyou; Zhu, Xinzhe

doi:10.1016/j.scitotenv.2023.163562

Cited by 8 publications

(3 citation statements)

References 88 publications

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“…They arise as a result of defects caused by aging of materials or external influences, such as natural corrosion, overpressure, improper installation, damage by third-party organizations, etc. [1,2]. Even small leaks during the year lead to significant losses of water, heat, and other resources.…”

Section: Introductionmentioning

confidence: 99%

Detection of Pipeline Leaks Using Fractal Analysis of Acoustic Signals

Zagretdinov,

Ziganshin,

Izmailova

et al. 2024

Fractal Fract

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In this paper, the possibility of using monofractal and multifractal analysis of acoustic signals of pipelines to detect leaks is considered. An experimental stand has been created to study the fractal characteristics of acoustic signals of pipelines with “slit” type defects. During the experiments, defects of the “slit” type pipeline with dimensions of 2 mm, 8 mm, and 20 mm were modeled. Detrended fluctuation analysis (DFA) and the multifractal detrended fluctuation analysis (MF-DFA) were used. As a result of the experimental studies, it was found that the occurrence of leakage leads to the occurrence of anticorrelated vibrations in a pipeline with multifractal properties. The analyses of acoustic signals by DFA and MF-DFA methods make it possible to reliably determine the leakage. The Hurst exponent and the width of the multifractal spectrum can serve as indicators of the occurrence of leaks in pipelines.

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Section: Introductionmentioning

confidence: 99%

Detection of Pipeline Leaks Using Fractal Analysis of Acoustic Signals

Zagretdinov,

Ziganshin,

Izmailova

et al. 2024

Fractal Fract

View full text Add to dashboard Cite

show abstract

“…BIX signified the ratio of fluorescence intensity at an excitation wavelength of 310 nm and an emission wavelength ranging from 380 nm to 430 nm. This ratio provided insights into the relative contribution of autochthonous CDOM within the water samples [14].…”

Section: Fluorescence Spectroscopy Analysis and Parafac Modelingmentioning

confidence: 99%

“…[12,13]. Moreover, this labor-intensive and error-prone identification method makes it difficult to obtain information on fine-grained sewer defects; therefore, it does not meet the increasing requirements of sewer defect detection [14]. Hence, there is a need to establish a straightforward and feasible evaluation approach for assessing external water infiltration in sewage networks.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Groundwater Infiltration in Sewer Networks Using Fluorescence Spectroscopy

Li,

Song

et al. 2023

Water

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Diagnosing water infiltration is imperative to assess the integrity and operation performance of sewer networks, which is challenging and costly due to the complex nature of these networks. This study proposes a simple approach to evaluate the extent of groundwater infiltration via a fluorescence spectroscopy method, i.e., the identification and quantification of the fluorescent signature components of the dissolved organic matter sewage. A newly built sewer network in Shantou, Southern China, was selected for the case study, and a mass balance method based on water quality characteristic factors (total phosphorus and NH4+-N) was applied in parallel for comparison. The results showed that the mass balance method was substantially influenced by fluctuations in sewage and external water concentrations, rendering it unreliable due to the extensive data and calculations required. Conversely, three-dimensional excitation–emission matrix–parallel factor analysis enabled the identification of terrestrial humus compounds as the signatures of underground water sources. The estimation indicates that the groundwater proportion across the four surveyed inspection wells along the pipeline network ranged from 10.8 ± 2.5% to 9.6 ± 3.5%, conforming to the allowable groundwater infiltration limits set for municipal sewage pipelines (10–15%). This study presents a simple method for the in-depth analysis of groundwater infiltration in urban sewage networks, providing valuable insights into maintaining water quality and network integrity.

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Deep learning in water protection of resources, environment, and ecology: achievement and challenges

Fu,

Jiang,

et al. 2024

Environ Sci Pollut Res

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Deep learning-assisted automated sewage pipe defect detection for urban water environment management

Cited by 8 publications

References 88 publications

Detection of Pipeline Leaks Using Fractal Analysis of Acoustic Signals

Detection of Pipeline Leaks Using Fractal Analysis of Acoustic Signals

Evaluation of Groundwater Infiltration in Sewer Networks Using Fluorescence Spectroscopy

Deep learning in water protection of resources, environment, and ecology: achievement and challenges

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