Neuro-Fuzzy Approaches for Sanitary Sewer Pipeline Condition Assessment

Chae, Myeongju; Abraham, Dulcy M.

doi:10.1061/(asce)0887-3801(2001)15:1(4)

Cited by 82 publications

(40 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because buried concrete pipes are patterned and poorly lit, robust feature extraction is an essential step and appears throughout the pipe inspection literature. Methods include edge detection [25] [22] or the Hough transform [22] for edge/line detection, image segmentation [26] and background subtraction [18] for foreground object extraction, methods of image registration [18] and optical flow [24] for the tracking and association of objects in successive video frames, particularly relevant in CCTV imaging. More advanced methods include texture-based methods, including co-occurrence [21] and histograms of oriented gradients [23], and multi-resolution or wavelet-based approaches [29] [17].…”

Section: Feature Extractionmentioning

confidence: 99%

“…Nevertheless, the limitations of the preceding paragraph notwithstanding, neural approaches have seen rather significant application in buried pipe inspection. In most cases, the neural network is preceded by computer vision approaches for feature extraction, followed by neural learning [29] [27] [20] [21] or neuro-fuzzy approaches [26] [88].…”

Section: Neural Modelsmentioning

confidence: 99%

See 1 more Smart Citation

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

Koch

Georgieva

Kasireddy

et al. 2015

Advanced Engineering Informatics

764

336

View full text Add to dashboard Cite

(2015) A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Advanced Engineering Informatics, 29 (2). pp. 196-210. ISSN 1474-0346 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/31986/1/Manuscript_KochEtAl_2015_accepted.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the Creative Commons Attribution Non-commercial No Derivatives licence and may be reused according to the conditions of the licence. For more details see: http://creativecommons.org/licenses/by-nc-nd/2.5/ A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 Abstract:To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research. Keywords:Computer Vision, Infrastructure, Condition assessment, Defect detection, Infrastructure monitoring Research Highlights: Visual inspection of civil infrastructure is essential for condition assessment.  We focus on concrete bridges, tunnels, underground pipes, and asphalt pavements.  Accordingly, we review the latest computer vision based defect detection methods.  Using computer vision most relevant types of defects can be automatically detected.  Automatic defect properties retrieval and assessment has not been achieved yet . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 ...

show abstract

Section: Feature Extractionmentioning

confidence: 99%

Section: Neural Modelsmentioning

confidence: 99%

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

Koch

Georgieva

Kasireddy

et al. 2015

Advanced Engineering Informatics

764

336

View full text Add to dashboard Cite

show abstract

“…The neural network analysis technique was found helpful in identifying four categories of sewer defects: cracks, joint displacements, reduction of cross-sectional area. [6] Developed an automated sewer inspection data interpretation system. The other approach is to predict a sewer's existing condition prior to its detailed inspection for selective, cost effective sewer inspection.…”

Section: Related Workmentioning

confidence: 99%

A New Image Model for Predicting Cracks in Sewer Pipes based on Time

Khalifa¹,

Aboutabl²,

Barakat³

2014

IJCA

View full text Add to dashboard Cite

Sewer overflows may cause communities to be vulnerable to various health problems and other monetary losses. This puts a lot of burden on responsible to minimize end user complaints. Therefore, crack prediction would be helpful to facilitate decision makers to control sewer overflow problems and prioritize inspection and rehabilitation needs .The accurate prediction of current underground sewer pipe cracks must be done before any pipe crashing with enough period of time to enable rehabilitation and replacement intervals, appropriate remedial methods and preventing sewer pipes crashing. Unfortunately, traditional technologies and models approaches have been limited to predict the development of sewer pipe cracks. In this paper, we address the problem of crack prediction of such cracks. This paper provides a proposed model which predict crack and cracks developments in any period of time that may occur in weak areas of a network of pipes.. We evaluate our results by comparing them with those obtained by many other models. The accuracy percentage of this model exceeds 90% and outperforms other approaches.

show abstract

“…Over the past two decades, soft computing techniques have been developed to assess the condition of civil infrastructure. Some of these techniques include artificial neural networks (Sinha 2004Kuzniar et al 2006, Bayesian network (Naidu et al 2006), neuro-fuzzy approach (Chae and Abraham 2001), fuzzy synthetic evaluation , and fuzzy rule-based modelling (Najjaran et al 2006). However, these techniques do not rationally account for confirmatory and/or contradictory information.…”

Section: Introductionmentioning

confidence: 99%

Condition Assessment of Buried Pipes Using Hierarchical Evidential Reasoning Model

Bai

Sadiq

Najjaran

et al. 2008

J. Comput. Civ. Eng.

View full text Add to dashboard Cite

/npsi/ctrl?lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. For the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous.http://dx.doi.org/10.1061/(ASCE) 0887-3801(2008)22:2(114) Journal of Computing in Civil Engineering, 22, 2, pp. 114-122, 2008-03-01 Condition assessment of buried pipes using hierarchical evidential reasoning model Hua, B.; Sadiq, R.; Najjaran, H.; Rajani, B. B.http://irc.nrc-cnrc.gc.ca Condition assessment of buried pipes using hierarchical evidential reasoning model NRCC-48714Bai, H.; Sadiq, R.; Najjaran, H.; Rajani, B. The material in this document is covered by the provisions of the Copyright Act, by Canadian laws, policies, regulations and international agreements. Such provisions serve to identify the information source and, in specific instances, to prohibit reproduction of materials without written permission. For more information visit http://laws.justice.gc.ca/en/showtdm/cs/C-42Les renseignements dans ce document sont protégés par la Loi sur le droit d'auteur, par les lois, les politiques et les règlements du Canada et des accords internationaux. Ces dispositions permettent d'identifier la source de l'information et, dans certains cas, d'interdire la copie de documents sans permission écrite. Pour obtenir de plus amples renseignements : http://lois.justice.gc.ca/fr/showtdm/cs/C-42 Condition Assessment of Buried Pipes using Hierarchical Evidential Reasoning (HER) ModelHua Bai, Rehan Sadiq, Homayoun Najjaran, Balvant Rajani Abstract: Effective inspection and monitoring practices for the condition assessment of pipes ensure better decision(s) for repair or replacement before they fail. Pipe deterioration is a physical manifestation of the aging process in which many factors can contribute to structural failure. Various technologies/ techniques have been developed during the last few years to inspect/monitor piping systems, but how to intelligently interpret the collected data remains a challenge.In this paper, a new approach based on hierarchical evidential reasoning (HER) is proposed.This approach uses Dempster-Shafer (D-S) theory to make inferences for condition assessment of buried pipes. A hierarchical evidential reasoning model can help combine different distress indicators (bodies of evidence) at different hierarchical levels using D-S rule of combination.The proposed hierarchical evidential reasoning method is demonstrated with an example of condition assessment for a large ...

show abstract

Neuro-Fuzzy Approaches for Sanitary Sewer Pipeline Condition Assessment

Cited by 82 publications

References 4 publications

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

A New Image Model for Predicting Cracks in Sewer Pipes based on Time

Condition Assessment of Buried Pipes Using Hierarchical Evidential Reasoning Model

Contact Info

Product

Resources

About