Challenging Issues in Modeling Deterioration of Combined Sewers

Wirahadikusumah, Reini D.; Abraham, Dulcy M.; Iseley, Tom

doi:10.1061/(asce)1076-0342(2001)7:2(77)

Cited by 162 publications

(61 citation statements)

References 8 publications

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“…The number of observed data in this study is considered small compared to the number of model parameters (i.e. transition probabilities) required by the non-stationary Markov model as can be seen in the non-stationary Markov model developed for sewers by Wirahadikusumah et al (2001).…”

Section: Resultsmentioning

confidence: 99%

“…When presented in diagram, the condition shown in line could provide a better visualization and closer approximation to the true deteriorated condition. This practice was also used in other papers (Wirahadikusumah et al 2001;Micevski et al 2002, Baik et al 2006. Some predictive ranges collapsed into a point value and some observed values were outside the ranges.…”

Section: Resultsmentioning

confidence: 99%

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Markov and Neural Network Models for Prediction of Structural Deterioration of Storm-Water Pipe Assets

Tran

Perera

2010

J. Infrastruct. Syst.

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Abstract. Stormwater pipe networks in Australia are designed to convey water from rainfall and surface runoff. They do not transport sewerage. Their structural deterioration is progressive with aging and will eventually cause pipe collapse with consequences of service interruption. Predicting structural condition of pipes provides vital information for asset management to prevent unexpected failures and to extend service life. This study focused on predicting the structural condition of stormwater pipes with two objectives. The first objective is the prediction of structural condition changes of the whole network of stormwater pipes by a Markov model at different times during their service life. This information can be used for planning annual budget and estimating the useful life of pipe assets. The second objective is the prediction of structural condition of any particular pipe by a neural network model. This knowledge is valuable in identifying pipes that are in poor condition for repair actions. A case study with CCTV inspection snapshot data was used to demonstrate the applicability of these two models.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Markov and Neural Network Models for Prediction of Structural Deterioration of Storm-Water Pipe Assets

Tran

Perera

2010

J. Infrastruct. Syst.

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“…Multiple regression models (Madanat et al 1995, Wirahadikusumah et al 2001 were employed in first attempts at modelling deterioration of infrastructure facilities because of their simplicity in mathematical operations and capability to describe the direct relationship between the input factors and the outcome. However, they fail to reflect the probabilistic nature in the deterioration process, require assumptions to be made on data errors that are difficult to verify and, finally, try to fit data-sensitive sample means from a limited data set to a full population mean .…”

Section: Existing Deterioration Modelsmentioning

confidence: 99%

“…A few Markov models for sewers using different techniques to calibrate Markov transition probability have been developed. They are non-linear optimisation (Wirahadikusumah et al 2001), expert opinion (Kathula 2001) and rule-based simulation (Ruwanpura et al 2003), which can be used to predict pipe condition at cohort (group) level. Madanat et al (1995) proposed a probit technique to link input factors with the targets in a Markov model for the deterioration of bridge decks.…”

Section: Existing Deterioration Modelsmentioning

confidence: 99%

Application of probabilistic neural networks in modelling structural deterioration of stormwater pipes

Tran

Perera

et al. 2006

Urban Water Journal

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In Australia, when stormwater systems were first introduced over 100 years ago, they were constructed independently of the sewer systems, and they are normally the responsibility of the third level of government, i.e., local government or city councils. Because of the increasing age of these stormwater systems and their worsening performance, there are serious concerns in a significant number of city councils regarding their deterioration. A study has been conducted on the structural deterioration of concrete pipes that make up the bulk of the stormwater pipe systems in these councils. In an attempt to look for a reliable deterioration model, a probabilistic neural network (PNN) model was developed using the data set supplied from participating councils. The PNN model was validated with snapshot-based sample data, which makes up the data set. The predictive performance of the PNN model was compared with a traditional parametric model using discriminant analysis on the same data set. Structural deterioration was hypothesised to be influenced by a set of explanatory factors, including pipe design and construction factors-such as pipe size, buried depth-and site factorssuch as soil type, moisture index, tree root intrusion, etc. The results show that the PNN model has a better predictive power and uses significantly more input variables (i.e., explanatory factors) than the discriminant model. More importantly, the key factors for prediction in the PNN model are difficult to interpret, suggesting that besides prediction accuracy, model interpretation is an important issue for further investigation.

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“…This has led to the development of proactive management strategies where work is prioritised based on both the estimated sewer condition and the consequences of a failure (Le Gauffre et al, 2007;Wirahadikusumah, Abraham, & Iseley, 2001). Although these proactive strategies are increasingly applied to main sewers, the lateral sewers transporting sewage from properties to main sewers (schematised in Figure 1) are generally subject to repair only after an incident occurs.…”

Section: Introductionmentioning

confidence: 99%

Analysing spatial patterns in lateral house connection blockages to support management strategies

Post

Langeveld

Clemens

2016

Structure and Infrastructure Engineering

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a faculty of civil engineering and geosciences, Section Sanitary engineering, delft university of technology, delft, the netherlands; b Partners4urbanwater, nijmegen, the netherlands; c deltares, delft, the netherlands ABSTRACTThe performance of lateral house connections has a direct impact on sewer serviceability. Despite the potential consequences of a blockage, these components are generally maintained with a reactive approach. As inspection data on the condition of lateral house connections are scarce, this study adopts a statistical procedure to support proactive strategies by analysing spatial blockage patterns to identify system parts with higher blockage incidences. First, a Monte Carlo simulation test provides insight into whether the spatial variation of the blockage likelihood is significant. This justifies the identification of explanatory factors by means of a bootstrapped generalised additive model. Application of the procedure to two databases containing 10 years of lateral house connection blockage data, revealed factors such as building age, sewer system type and ground settlement rate to explain spatial differences in the blockage likelihood. Furthermore, a likelihood ratio test demonstrated that the addition of a spatial smoother improved model performance. This smoother was able to account for additional spatial variation caused by explaining factors for which no data were available. The procedure provides key information for inspection and rehabilitation strategies by taking into account the model performance in assessing the trade-off between costs and benefits in terms of serviceability.

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Challenging Issues in Modeling Deterioration of Combined Sewers

Cited by 162 publications

References 8 publications

Markov and Neural Network Models for Prediction of Structural Deterioration of Storm-Water Pipe Assets

Markov and Neural Network Models for Prediction of Structural Deterioration of Storm-Water Pipe Assets

Application of probabilistic neural networks in modelling structural deterioration of stormwater pipes

Analysing spatial patterns in lateral house connection blockages to support management strategies

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