Prediction models for sewer infrastructure utilizing rule-based simulation

Ruwanpura, Janaka Y.; Ariaratnam, Samuel T.; El-Assaly, Ashraf

doi:10.1080/10286600410001694192

Cited by 39 publications

(26 citation statements)

References 7 publications

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“…Six specific articles, published on the use of statistical or probabilistic models for wastewater pipe condition, performance assessment, and prediction modeling, stand out in the literature and are discussed in the following (Baik, 2006;Baur, 2001;Kleiner, 2001;Kleiner, 2004;Korving, 2008;Ruwanpura, 2004).…”

Section: Statistical Modelsmentioning

confidence: 99%

Development of wastewater pipe performance index and performance prediction model

Angkasuwansiri¹,

Sinha

2014

IJSMSS

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Water plays a critical role in every aspect of civilization: agriculture, industry, economy, environment, recreation, transportation, culture, and health. Much of America's drinking water and wastewater infrastructure; however, is old and deteriorating. A crisis looms as demands on these systems increase. The costs associated with renewal of these aging systems are staggering.There is a critical disconnect between the methodological remedies for infrastructure renewal problems and the current sequential or isolated manner of renewal analysis and execution. This points to the need for a holistic systems perspective to address the renewal problem. Therefore, new tools are needed to provide support for wastewater infrastructure decisions. Such decisions are necessary to sustain economic growth, environmental quality, and improved societal benefits.Accurate prediction of wastewater pipe structural and functional deterioration plays an essential role in asset management and capital improvement planning. The key to implementing an asset management strategy is a comprehensive understanding of asset condition, performance, and risk profile.The primary objective of this research is therefore to develop protocols and methods for evaluating the wastewater pipe performance. This research presents the life cycle of wastewater pipeline identifying the causes of pipe failure in different phases including design, manufacture, construction, operation and maintenance, and repair/rehabilitation/replacement. Various modes and mechanisms of pipe failure in wastewater pipes were identified for different pipe material which completed with results from extensive literature reviews, and interviews with utilities and pipe associations. After reviewing all relevant reports and utility databases, a set of standard pipe parameter list (data structure) and a pipe data collection methodology were developed. These parameters includes physical/structural, operational/functional, environmental and other parameters, for not only the pipe, but also the entire pipe system. This research presents a development of a performance index for wastewater pipes. The performance index evaluates each iii parameter and combines them mathematically through a weighted summation and a fuzzy inference system that reflects the importance of the various factors. The performance index were evaluated based on artificial data and field data to ensure that the index could be implemented to real scenarios. Developing a performance index led to the development of a probabilistic performance prediction model for wastewater pipes. A framework would enable effective and systematic wastewater pipe performance evaluation and prediction in asset management programs.iv Acknowledgement

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Section: Statistical Modelsmentioning

confidence: 99%

Development of wastewater pipe performance index and performance prediction model

Angkasuwansiri¹,

Sinha

2014

IJSMSS

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“…1 p Is a function includes the time calculations which divides into periods every period equal to four months.…”

Section: Prediction Of Crack Widthmentioning

confidence: 99%

“…[11] proposed a fuzzy set theory based approach for a pipe's condition assessment. [12] used rule-based simulation methodology to predict condition rating of sewers. The model predicted the condition rating of pipe based on age, material and length of pipe.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the fact that sewer pipelines are hidden from day to day view, deterioration can occur unnoticed and this can in turn lead to unexpected functional failures. Moreover, maintenance and rehabilitation of aging sewers have become an overload in terms of budget allocation and investment planning for towns [1]. Multiple objectives may exist for planning budget allocation that could be dependent upon certain constraints and available resources.…”

Section: Introductionmentioning

confidence: 99%

“…Multiple objectives may exist for planning budget allocation that could be dependent upon certain constraints and available resources. This makes the task of planning, prioritizing and allocating funds a complex exercise [1].…”

Section: Introductionmentioning

confidence: 99%

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

Khalifa¹,

Aboutabl²,

Barakat³

2014

IJCA

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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.

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