Infrastructure Condition Prediction Models for Sustainable Sewer Pipelines

Chughtai, Fazal; Zayed, Tarek

doi:10.1061/(asce)0887-3828(2008)22:5(333)

Cited by 133 publications

(66 citation statements)

References 18 publications

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“…Because of rather substantial cost associated with data acquisition of sewer pipes, there is significant interest in maximizing the use of data. Prediction methods [32] [33] [34] [35] develop statistical, neural, or expert system deterioration models to predict pipe state, over time, on the basis of earlier observations.…”

Section: Underground Concrete Pipesmentioning

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

797

336

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(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 ...

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Section: Underground Concrete Pipesmentioning

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

797

336

View full text Add to dashboard Cite

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“…Otherwise, the deposits will accumulate upon the surface, further impeding the flow and potentially resulting in clogging, surcharge and ultimately flooding issues. [34] Moreover, an accurate underlying wall roughness is also required in the modelling of effective flushing strategies. [35,36] Furthermore, within DNs, biofilms also contribute to the production of unwelcome gases, namely hydrogen sulphide and methane, which present their own problems for the industry, ranging from odour and corrosion issues, to potentially endangering maintenance crews.…”

Section: Biofouling Within Pipelines -Practical Relevance and Impactsmentioning

confidence: 99%

Biofilm development in water distribution and drainage systems: dynamics and implications for hydraulic efficiency

Cowle

Babatunde

Rauen

et al. 2014

Environmental Technology Reviews

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Pipeline distribution systems account for the vast majority of the physical infrastructure in the water and wastewater industry. Their effective management represents the primary challenge to the industry, from both an operational and public health standpoint. Biofouling is ubiquitous within these systems, and it can significantly impede their efficiency, through increase in boundary shear and associated flow resistance caused by characteristic change in surface dynamics. Nonetheless, conventional pipeline design practices fail to take into account such effects, partly because research findings that could contribute to upgrade and optimize design practices appear scattered in the literature, and are often offering conflicting views as to its causes. This makes it difficult for the adoption of adequate predictive and preventative measures. The aim of this review is to update and contribute to a better understanding of the development and impact of biofilms and biofouling within water management pipelines, particularly within the academia and the general engineering community. The review has confirmed that the potential impact of biofouling on pipeline performance can be significant and that current design approaches are outdated for biofouled surfaces. Further research on this topic is therefore, essential, to ensure that both current and future systems are as effective as possible, both environmentally and financially. In particular, more advanced mathematical modelling frameworks which include the dynamic and case-specific nature of biofouling should be developed. Such a framework could give rise to a real-time monitoring platform to assist the adoption of more cost-effective approaches to maintain and repair the system.

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“…Regression models, a type of statistical model, are known to be better suited for identifying the basic relationships between the individual variables that contribute to the condition grade of the pipe, whereas artificial neural networks (intelligence-based) are more appropriate for a 'black box' approach and have better prediction capabilities (Tu, 1996). Some examples of sewer deterioration models include, fuzzy Markov deterioration models, decision tree-based deterioration models, multiple logistic regression and probabilistic neural network models (Syachrani et al, 2013;Mashford et al, 2010;Tran et al, 2009Tran et al, , 2010Younis and Knight, 2010;Khan et al, 2009;Chughtai and Zayed, 2008;Ariaratnam et al, 2001).…”

Section: Introductionmentioning

confidence: 97%