Sewer Deterioration Models Research Articles

Vulnerability hotspot mapping (VHM) of sewer pipes based on deterioration factors

ABSTRACT A decentralized, and sustainable sewer inspection plan at the city scale demands multiple inspection methods with different areas of impact. While sewer deterioration models offer great areas of impact, spatial mapping can assess the vulnerability of the system without prior knowledge of the pipe’s structural health condition. In this study, we assess and prioritize sewer deterioration factors such as pipe age, material, sewer type, node degree, flow velocity, and surface vegetation for vulnerability hotspot mapping of a sewer system in Dresden, Germany. The validation and sensitivity analyses revealed that flow velocity, pipe age, and surface vegetation are the most sensible factors to model, respectively. The linear model resulted in 76% efficiency and a mean squared error of 0.918 while it was improved with a random forest algorithm which points out vulnerability mapping potential as an early sewer inspection method at the city scale.

On the influence of input data uncertainty on sewer deterioration models – a case study in Norway

Visual inspection is currently the industry standard for assessing sewer and stormwater pipelines – a method prone to uncertainties as shown by previous studies. The data gathered from the visual inspection procedures is the main information base on which rehabilitation and replacement strategies are founded in current practice. Consequently, this study evaluates the quality of visual inspection data by quantifying the uncertainty and assessing its impact on the output of a deterioration model. The study was carried out by re-classifying pipe condition classes using the same video footage and transferring differences in the classifications into a distribution that was used as a measure of input data uncertainty. This quantified uncertainty was then propagated into a deterioration model using a Monte Carlo approach to assess its impact on the model behaviour. Results show that there is a considerable uncertainty in condition classes coded according to the Norwegian standard, and that it is comparable to uncertainties estimated in other studies using various European coding systems. The uncertainty assessment indicates that the uncertainties have a considerable impact on the model predictions, which in consequence demonstrates that the uncertainty in the visual inspection methodology can heavily influence the decisions for rehabilitation and replacement strategies.

Modified sewer asset management to accommodate London’s future sustainable development

PurposeThis paper is about best practice in managing legacy drainage assets to support sustainable urban regeneration. The purpose of this paper is to describe best practice sewer asset management (AM) and to adjust the current reactive maintenance approach for sewers, to one that accommodates long-term operational and town planning needs. The development of an improved sewer deterioration model (DM) provided an important tool for this.Design/methodology/approachThis research adopts a mixture of qualitative and quantitative approaches to analyse a total network length of 24,252 km which represents 703,156 records of historic sewer structural condition inspection data. This was used to build an improved DM. These models were used as inputs into a proactive AM approach that improves upon recommendations in the Sewerage Rehabilitation Manual developed by Water Research Centre.FindingsThis is a paradigm shift and goes beyond the current culture of OFWAT (Water Services Regulation Authority) supervision, five-year asset management period and occasional environmental penalties. A new legislative model may be needed; especially because a report by UKWIR (Water Industry Research) in 2015 identified that nationally the rate of sewer network deterioration is outpacing available investment and significant health problems may arise in addition to those from developmental pressures.Research limitations/implicationsThe authors have researched and managed old sewer networks and present a review of the new issues raised by intensive development, particularly for the London region, but applicable elsewhere, and how these must lead to a modified risk, and novel incentive-based approach to AM, if the system is not to fail.Originality/valueLarge, legacy databases of several decades of sewer network performance records have been combined and analysed as stratified, heterogeneous sets with Gaussian distributions; thereby improving on previous assumptions of homogeneous data. The resulting rigorous DMs are the foundation of new approaches to sustainable risk management of large urban networks.

Practical benchmarking of statistical and machine learning models for predicting the condition of sewer pipes in Berlin, Germany

Abstract Deterioration models can be successfully deployed only if decision-makers trust the modelling outcomes and are aware of model uncertainties. Our study aims to address this issue by developing a set of clearly understandable metrics to assess the performance of sewer deterioration models from an end-user perspective. The developed metrics are used to benchmark the performance of a statistical model, namely, GompitZ based on survival analysis and Markov-chains, and a machine learning model, namely, Random Forest, an ensemble learning method based on decision trees. The models have been trained with the extensive CCTV dataset of the sewer network of Berlin, Germany (115,258 inspections). At network level, both models give satisfactory outcomes with deviations between predicted and inspected condition distributions below 5%. At pipe level, the statistical model does not perform better than a simple random model, which attributes randomly a condition class to each inspected pipe, whereas the machine learning model provides satisfying performance. 66.7% of the pipes inspected in bad condition have been predicted correctly. The machine learning approach shows a strong potential for supporting operators in the identification of pipes in critical condition for inspection programs whereas the statistical approach is more adapted to support strategic rehabilitation planning.

A method to consider replaced pipes in modeling of sewer pipe deterioration

ABSTRACTStatistical models that predict the deterioration of sewer pipes are useful for planning financial resources required for sewer renewal. Usually, data that are available to calibrate these models solely concern pipes that are still in place, leading to underestimated deterioration rates. A new method is proposed to consider possible past replacement of pipes in the statistical modeling of their deterioration. The proposed method considers the aging of pipes, simulated with a Cox model, and their probability to be replaced separately. Application to a synthetic sewer network, for which it was assumed that information regarding all pipe replacements over the lifetime of the network was available, showed that the proposed method allows for improved predictions of the sewer deterioration model, when compared to predictions of a model calibrated without considering the information about replaced pipes.

Improving the benefits of sewer condition deterioration modelling through information content analysis.

As urban sewer infrastructures age, it becomes increasingly important to make effective decisions to maintain the structural condition of the sewers at an acceptable level. To support the decision-making process, the utility manager can apply sewer deterioration models. However, the quality of the decision support from such models is dependent on the accuracy and reliability of the predictions, and previous research has shown that sewer deterioration predictions can be unreliable. In this paper it is shown, by numerical experiment and analysis of information content, how the accuracy of sewer deterioration models is inhibited by data heterogeneity. The data heterogeneity arises when the condition class is used as a response variable, because the condition class is an aggregation of different failure modes, and contains information that does not describe structural deterioration. Based on these findings, the paper suggests changes to be implemented in the condition classification standard, which can mitigate heterogeneity and improve prediction reliability. The suggestions for improvement include distinguishing between structural and functional defect codes, defining new condition metrics better suited for deterioration modelling, and registration of detailed defect codes to allow distinction of different failure mechanisms.

A qualitative analysis of early defects present in PVC-U sewers but not observed in rigid pipes

The objective of the study was to analyze early defects in PVC-U sewer pipes that are not observed in rigid pipes. Field investigations were conducted on newly laid PVC-U sewer pipes in more than a dozen cities in Poland using CCTV equipment and a special device for measuring deflections. The analysis focused on qualitative data concerning defects observed in PVC-U sewers. Three types of defects, i.e. dents, deflections and buckling, were thoroughly analyzed to determine whether trenchless rehabilitation of such pipes is possible and to improve the existing sewer deterioration models. The major conclusions concern the types, sizes and frequencies of defects in PVC-U pipes, their causes, and the possibility of future rehabilitation as well as the improvement in the deterioration models.

Evaluating the role of deterioration models for condition assessment of sewers

Ensuring reliable structural condition of sewers is an important criterion for sewer rehabilitation decisions. Deterioration models applied to sewer pipes support the rehabilitation planning by means of prioritising pipes according to their current and predicted structural status. There is a benefit in applying such models if sufficient inspection data for calibration, an appropriate deterioration model, and adequate covariates to explain the variability in the conditions are available. In this paper it is discussed up to what level the application of sewer deterioration models can be beneficial under limited data availability. The findings show that the indirect nature of the explanatory covariates which are commonly used in sewer deterioration models makes it difficult to harness any benefit from modelling sewer conditions at a network level, but that the deterioration model application still may be beneficial for prioritising inspection candidates. The prediction power of the current sewer deterioration models is limited by the adequacy of the explanatory variables available, and by the fact that different failure modes are mixed in the aggregated condition class, and not modelled explicitly.

Assessing impacts of the private sewer transfer on UK utilities

In October 2011, Section 105A (S105A) of the Water Act of 2003 transferred the ownership of previously privately owned sewers to the ten water and sewerage companies operating in England and Wales. In light of this recent legislative change, this paper discusses the asset management challenges associated with the private sewer transfer before exploring a decision-making framework used by South West Water to establish a more accurate understanding of the extent and likely condition of their newly transferred network. The framework has allowed South West Water to initiate a proactive asset management programme with the aim of addressing the deteriorating condition of these assets while also tackling their associated serviceability performance. Initially, a number of geospatial (GIS) tools are used to provide an estimate of the likely extent of the transferred network before a well-established public sewer deterioration model is used to predict the condition and operational performance of these S105A assets over time. The outputs from this study have better equipped decision makers with information surrounding the deterioration and collapse rates of these newly transferred sewers over time. The information has been used to help formulate strategic business planning decisions, for example, by using cost vs. benefit analysis tools to model different investment scenarios across the network.

Sewer deterioration modeling with condition data lacking historical records

Accurate predictions of future conditions of sewer systems are needed for efficient rehabilitation planning. For this purpose, a range of sewer deterioration models has been proposed which can be improved by calibration with observed sewer condition data. However, if datasets lack historical records, calibration requires a combination of deterioration and sewer rehabilitation models, as the current state of the sewer network reflects the combined effect of both processes. Otherwise, physical sewer lifespans are overestimated as pipes in poor condition that were rehabilitated are no longer represented in the dataset. We therefore propose the combination of a sewer deterioration model with a simple rehabilitation model which can be calibrated with datasets lacking historical information. We use Bayesian inference for parameter estimation due to the limited information content of the data and limited identifiability of the model parameters. A sensitivity analysis gives an insight into the model's robustness against the uncertainty of the prior. The analysis reveals that the model results are principally sensitive to the means of the priors of specific model parameters, which should therefore be elicited with care. The importance sampling technique applied for the sensitivity analysis permitted efficient implementation for regional sensitivity analysis with reasonable computational outlay. Application of the combined model with both simulated and real data shows that it effectively compensates for the bias induced by a lack of historical data. Thus, the novel approach makes it possible to calibrate sewer pipe deterioration models even when historical condition records are lacking. Since at least some prior knowledge of the model parameters is available, the strength of Bayesian inference is particularly evident in the case of small datasets.

Identifying biases in deterioration models using synthetic sewer data

The assessment and validation of sewer deterioration models is difficult because reliable data are missing. This makes it hard to find the most suitable model for a particular application. A network condition simulator (NetCoS) is used to generate synthetic sewer data for defined test scenarios. Thereby, the deterioration and replacement of pipes, the expansion of the sewer network, and classification errors are considered. Based on such synthetic data, deterioration models are calibrated and their results compared with the predefined scenario. While this approach is not capable of proving that a model performs correctly on a real application, it highlights the strengths and weaknesses of a model. The influence of condition classification errors and the age of the sewer system is investigated for two deterioration models. The results show, that classification errors can introduce substantial biases in the parameter estimation of the Markov model while in comparison the applied cohort model is fairly robust. Young sewer systems with fewer pipes in bad condition states on the other hand, have a very strong influence on the parameter uncertainties of the cohort model while the Markov model proved to be less sensitive.

A Survival Analysis Model for Sewer Pipe Structural Deterioration

Abstract: The structural state of sewer systems is often quantified using condition classes. The classes are based on the severity of structural defects observed on individual pipes within the system. Here, a survival analysis model was developed to predict the overall structural state of a sewer network based on camera inspection data from a sample of pipes in the system. The convolution product was used to define the survival functions for cumulative staying times in each condition class. An original calibration procedure for the sewer deterioration model was developed to overcome the censored nature of data (left censored and right censored) available for the calibration of sewer deterioration models. The exponential and Weibull functions were used to represent the distribution of waiting times in each deterioration state. Cross‐validation tests showed that the Weibull function led to greater uncertainty than the exponential function for the simulated proportion of pipes that are in a deteriorated state. Using various sample sizes for model calibration, these cross‐validation tests also showed that the model's results are robust to smaller calibration sample sizes. This confirms the model's potential for predicting the overall state of deterioration of a sewer network when only a small proportion of the pipes have been inspected.

Network condition simulator for benchmarking sewer deterioration models

An accurate description of aging and deterioration of urban drainage systems is necessary for optimal investment and rehabilitation planning. Due to a general lack of suitable datasets, network condition models are rarely validated, and if so with varying levels of success. We therefore propose a novel network condition simulator (NetCoS) that produces a synthetic population of sewer sections with a given condition-class distribution. NetCoS can be used to benchmark deterioration models and guide utilities in the selection of appropriate models and data management strategies. The underlying probabilistic model considers three main processes: a) deterioration, b) replacement policy, and c) expansions of the sewer network. The deterioration model features a semi-Markov chain that uses transition probabilities based on user-defined survival functions. The replacement policy is approximated with a condition-class dependent probability of replacing a sewer pipe. The model then simulates the course of the sewer sections from the installation of the first line to the present, adding new pipes based on the defined replacement and expansion program. We demonstrate the usefulness of NetCoS in two examples where we quantify the influence of incomplete data and inspection frequency on the parameter estimation of a cohort survival model and a Markov deterioration model. Our results show that typical available sewer inventory data with discarded historical data overestimate the average life expectancy by up to 200 years. Although NetCoS cannot prove the validity of a particular deterioration model, it is useful to reveal its possible limitations and shortcomings and quantifies the effects of missing or uncertain data. Future developments should include additional processes, for example to investigate the long-term effect of pipe rehabilitation measures, such as inliners.