Acoustic Characterization of PVC Sewer Pipes for Crack Detection Using Frequency Domain Analysis

Khan, Muhammad Safeer; Patil, Rajvardhan

doi:10.1109/isc2.2018.8656739

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…[ 35 ]. In pipe flow engineering, acoustics could allow for detecting fatigue cracks [ 36 ] and leaks [ 37 ] before they cause significant problems.…”

Section: Discussionmentioning

confidence: 99%

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Evaporation induced acoustic emissions in microfluidic vessels

Dutta,

Bieling,

Verbiest

2023

R. Soc. open sci.

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Fluid flow processes such as drainage and evaporation in porous media are crucial in geological and biological systems. The motion of the displacement front of a moving fluid through multi-phase interfaces is often associated with abrupt mechanical energy release, detectable as acoustic emissions (AEs). The exact origin of these pulses and their damping mechanisms are still subjects of debate. Here, we study the characteristics of such AEs during evaporation of water from artificial microfluidic vessels, inspired by the physiology of vascular water-transport in plants. From the extracted settling times of the recorded AEs, we identify three pulse types and attribute their origins to bubble formation, snap-off events and rapid pore invasion. We also show that the resonance frequencies between 10 and 70 kHz present in specific pulse types decrease with increasing vessel radius (ranging from 0.25 to 1.0 mm) and length (ranging from 2.5 to 10.0 mm). Our findings provide insight into evaporation-induced AEs from microfluidic systems, and their potential use in non-invasive inspection or vascular health monitoring.

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“…[ 35 ]. In pipe flow engineering, acoustics could allow for detecting fatigue cracks [ 36 ] and leaks [ 37 ] before they cause significant problems.…”

Section: Discussionmentioning

confidence: 99%

“…In microfluidics, acoustics could allow for the detection of fluid front displacement [24], and possibly even microdroplet size measurements similar to Lee et al [35]. In pipe flow engineering, acoustics could allow for detecting fatigue cracks [36] and leaks [37] before they cause significant problems.…”

Section: Impact and Scopementioning

confidence: 99%

Evaporation induced acoustic emissions in microfluidic vessels

Dutta,

Bieling,

Verbiest

2023

R. Soc. open sci.

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“…Nevertheless, the papers that focus on some topics, which do not relate to the vision-based sewer inspection, are not included in this paper. For example, the quality assessment for sewer images [ 25 ], pipe reconstruction, internal pipe structure, wall thickness measurement, and sewer inspections based on other sensors such as depth sensors [ 26 , 27 ], laser scanners [ 28 , 29 ], or acoustic sensors [ 30 , 31 ] are considered irrelevant topics. Figure 3 represents the number of articles including journals and conference papers in different time periods from 2000 to 2022.…”

Section: Survey Methodologymentioning

confidence: 99%

Vision-Based Defect Inspection and Condition Assessment for Sewer Pipes: A Comprehensive Survey

Wang

Dang

et al. 2022

Sensors

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Due to the advantages of economics, safety, and efficiency, vision-based analysis techniques have recently gained conspicuous advancements, enabling them to be extensively applied for autonomous constructions. Although numerous studies regarding the defect inspection and condition assessment in underground sewer pipelines have presently emerged, we still lack a thorough and comprehensive survey of the latest developments. This survey presents a systematical taxonomy of diverse sewer inspection algorithms, which are sorted into three categories that include defect classification, defect detection, and defect segmentation. After reviewing the related sewer defect inspection studies for the past 22 years, the main research trends are organized and discussed in detail according to the proposed technical taxonomy. In addition, different datasets and the evaluation metrics used in the cited literature are described and explained. Furthermore, the performances of the state-of-the-art methods are reported from the aspects of processing accuracy and speed.

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“…With the wide popularization of closed‐circuit television (CCTV) in the sewer system, automated defect detection via CCTV cameras has attracted the attention of many researchers from industry and academia in recent years. Compared to the methods with other data acquisition techniques (Khan & Patil, 2018; Lepot et al., 2017), CCTV inspection approaches are safe, portable, and economical (Czimmermann et al., 2020; Mostafa & Hegazy, 2021).…”

Section: Introductionmentioning

confidence: 99%

Attention‐guided multiscale neural network for defect detection in sewer pipelines

Wang

Dang

et al. 2023

Computer aided Civil Eng

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Sanitary sewer systems are major infrastructures in every modern city, which are essential in protecting water pollution and preventing urban waterlogging. Since the conditions of sewer systems continuously deteriorate over time due to various defects and extrinsic factors, early intervention in the defects is necessary to prolong the service life of the pipelines. However, prior works for defect inspection are limited by accuracy, efficiency, and economic cost. In addition, the current loss functions in object detection approaches are unable to handle the imbalanced data well. To address the above drawbacks, this paper proposes an automatic defect detection framework that accurately identifies and localizes eight types of defects in closed‐circuit television videos based on a deep neural network. First, an effective attention module is introduced and used in the backbone of the detector for better feature extraction. Then, a novel feature fusion mechanism is presented in the neck to alleviate the problem of feature dilution. After that, an efficient loss function that can reasonably adjust the weight of training samples is proposed to tackle the imbalanced data problem. Also, a publicly available dataset is provided for defect detection tasks. The proposed detection framework is robust against the imbalanced data and achieves a state‐of‐the‐art mean average precision of 73.4%, which is potentially applied in realistic sewer defect inspections.

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Acoustic Characterization of PVC Sewer Pipes for Crack Detection Using Frequency Domain Analysis

Cited by 8 publications

References 9 publications

Evaporation induced acoustic emissions in microfluidic vessels

Evaporation induced acoustic emissions in microfluidic vessels

Vision-Based Defect Inspection and Condition Assessment for Sewer Pipes: A Comprehensive Survey

Attention‐guided multiscale neural network for defect detection in sewer pipelines

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