A probability model for investigating the trend of structural deterioration of wastewater pipelines

Canada’s infrastructure is aging and deteriorating. New legislation requires the municipalities to estimate operating and capital expenditures for running the systems into the future and to develop financial sustainability plans. Wastewater pipelines deterioration is currently not well understood and realistic deterioration models need to be developed.This paper demonstrates how the condition assessment data from trenchless visual inspections of wastewater pipelines can be used to understand the performance of wastewater pipelines. A new ordinal regression model for the deterioration of wastewater pipelines based on cumulative logits is elaborated. The model is presented using the Generalized Linear model formulation and takes into account the interaction effect between the explanatory variables. The new model is demonstrated and validated using the City of Niagara Falls high quality wastewater collection network condition assessment data for reinforced concrete (RC) and vitrified clay (VC) pipes.This new model is found to represent the City of Niagara Falls RC and VC pipes’ deterioration behavior for pipes in service for up to 110 years. RC pipes deterioration is found to be age dependent while VC pipes deterioration is not age dependent. This finding is contrary to other deterioration model studies that indicate that VC deterioration is age dependent. The service life for RC pipes is estimated to be approximately 75 years while VC pipes are found to have an indefinite service life if installed without structural damage.The cumulative logit model can be used to determine wastewater pipelines’ service life, predict future condition states, and estimate networks’ maintenance and rehabilitation expenditures. The latter is critical if realistic wastewater networks’ future maintenance and operation budgets are to be developed for the life of assets and to meet new regulatory reporting requirements. Further research is required to validate this new methodology for other networks and the deterioration modeling of pipe materials other than RC and VC.     

Integrated performance assessment model for water distribution networks

The American infrastructure report card in 2013 rated the US water system infrastructure with grade of ‘D’. The Canadian infrastructure report card in 2012 stated that around 15.4% of Canada’s water infrastructure has a condition of fair to very poor. Thus, there is a critical need to develop efficient inspection, maintenance and rehabilitation plans for water distribution networks. However, such plans require an assessment tool to evaluate the performance and condition of water distribution networks. Therefore, the main purpose of this paper is to develop an integrated performance assessment model for water distribution networks. Two modules were developed to assess the performance of water pipelines and accessories, respectively. A third module was developed to assess the performance of water segments that includes pipelines and accessories. Moreover, a fourth module was built to assess the performance of each sub-network and the entire network based on the segments’ connection type using a reliability-based approach. To assess the performance of the water distribution network, the critical factors affecting its pipelines and accessories were identified and studied. The fuzzy analytic network process technique was used to obtain the importance weights of the identified factors.     

A condition assessment model for oil and gas pipelines using integrated simulation and analytic network process

Even though they are safe and economical transportation means of gas and oil products around the world, pipelines can be subject to failure and degradation generating hazardous consequences and irreparable environmental damages. Therefore, gas and oil pipelines need to be effectively monitored and assessed for optimal and safe operation. Many models have been developed in the last decade to predict pipeline failures and conditions. However, most of these models used corrosion features as the sole factor to assess the condition of pipelines. Therefore, the objective of this paper was to develop a condition assessment model of oil and gas pipelines that considers several factors besides corrosion. The proposed model, which uses both analytic network process and Monte Carlo simulation, considers the uncertainty of the factors affecting pipeline condition and the interdependency relationships between them. The performance of the model was tested on an existing offshore gas pipeline in Qatar and was found to be satisfactory. The model will help pipeline operators to assess the condition of oil and gas pipelines and hence prioritise their inspections and rehabilitation requirements.     

Condition-based maintenance decision support system for oil and gas pipelines

The 2013 report card of America's infrastructure has scored the condition of oil and gas pipelines as D+ which means that such pipelines are in a relatively poor condition. More than 10,000 failures have been recorded in the US. These failures have resulted in environmental, health and property damages. Therefore, there is a definite need to give more attention to the maintenance of oil and gas pipelines. This paper develops a comprehensive model for the maintenance planning of oil and gas pipelines. The model selects rehabilitation/repair alternatives for oil and gas pipelines based on their condition during their service life. These alternatives are then used to calculate the cash flow throughout the service life of these infrastructures. The model, which uses Monte Carlo simulation and fuzzy approach to address the uncertainties in the estimation of the maintenance operation costs and the economic parameters, calculates the Equivalent Uniform Annual Worth of the identified alternatives. The optimum maintenance programmes consist of the alternatives that have the lowest life cycle cost of oil and gas pipelines. The model is expected to support pipeline operators in the maintenance decision-making process of oil and gas pipelines.     

Continuation ratio model for the performance behavior of wastewater collection networks

Canada’s aged wastewater infrastructure is failing. New financial and environmental regulatory requirements demand municipalities to estimate operating and capital expenditures for running the systems into the future, and to develop plans for financial sustainability while protecting public health and the environment. Presently, wastewater pipelines’ deterioration is not well understood and realistic deterioration models need to be developed.This paper presents a new ordinal regression model for the deterioration of wastewater pipelines based on continuation ratio logits. The model is presented using the generalized linear model formulation, and takes into account the ordinal nature of the dependent variable and the interaction effects between explanatory variables. The model provides estimates of conditional probabilities for a pipeline to advance beyond a particular internal condition grade – to worse condition – depending on pipe material and age. The model development and validation procedure is demonstrated using high quality condition assessment data for reinforced concrete (RC) and vitrified clay (VC) pipes from the City of Niagara Falls wastewater collection system.The new model is found to represent the RC and VC pipes’ degradation behavior for in-service pipes up to 110 years of age at the City of Niagara Falls wastewater collection system. RC pipes’ deterioration is found to be age dependent while VC pipes’ deterioration is not age dependent. The VC pipe finding is contrary to other deterioration model studies that indicate that the type of pipe material is not significant and that the deterioration of VC pipes is age dependent. The analysis shows, for example, that the predicted conditional probability for RC pipes to advance beyond internal condition grade 3 is estimated to be 60% at 40 years of age and it increases to 90% at 80 years. Similarly, there is a 60% chance of advancing beyond grade 4 to collapsed/collapse imminent condition at 80 years of age for RC pipes. VC pipes are found to have an indefinite service life if installed without structural damage. However, VC pipes exhibited relatively higher conditional probabilities than RC pipes for advancing to worse internal condition grades for pipes up to 65 years of age. Poor installation practices that resulted in pipe defects, such as open/displaced joints and defective connections are deemed to be the factors that resulted in VC pipe deterioration.The findings from the continuation ratio model can be used for risk-based policy development for maintenance management of wastewater collection systems. The proposed model can help in devising appropriate intervention plans and optimum network maintenance management strategies based on pipelines’ age, material type, and internal condition grades. These predictions are critical if realistic wastewater networks’ future maintenance and operation budgets are to be developed over the life of asset and to meet new regulatory reporting requirements. Further research is required to validate the proposed model in other networks and to determine if the method can be used to model the deterioration of pipe materials other than RC and VC.     

Identifying bridge structural condition development trends via categorical inspection condition rating with case studies

Inspection data in a bridge management system (BMS) may provide us with comprehensive information about a structure's condition history. Identification of bridge structural condition development trends from historic inspection data for maintenance planning has drawn more and more attention, particularly from bridge agencies, who prefer ‘worst-first’ maintenance and rehabilitation strategies. In this paper, rather than correlating inspection data with fundamental structural deterioration models, a statistical inspection data-based analysis was developed. To demonstrate the inspection data-based approach in identifying bridge structural condition development trends in practical scenarios, case studies of concrete deck slabs and steel beam girders were applied. The ANalysis Of VAriance (ANOVA) technique was adopted to identify the major factors considered by a BMS to have a significant influence on the condition index. As a result, three major factors, bridge construction year, inspection year and inspector, were identified when structures were exposed to the same environment. Associated with these major factors, statistical indicators were introduced to characterize the condition development trends of structures, and to detect signs related to the potential of threat to the structural integrity. The indicators were further applied to identify a specific subgroup, e.g. those bridges that were constructed in a certain period and experienced the most deterioration or the highest deterioration rate. The approach developed can be extended to look into more causal factors on how they may alter the structural condition development trends when their information is available within inspection records. The outcomes may offer useful information for maintenance planning. Furthermore, it was found that the inspector also had quite a considerable impact on the condition index. A difference in structural condition assessment among inspectors may occur regardless of the same structural condition scenarios. This difference was quantified and applied to identify inspectors who tended to underestimate deterioration in structural condition.     

Multiobjective optimisation algorithm for sewer network rehabilitation

Understanding of deterioration mechanisms in sewers helps asset managers in developing prediction models for estimating whether or not sewer collapse is likely. Effective utilisation of deterioration prediction models along with the development and use of life cycle maintenance cost analysis contribute to reducing operation and maintenance costs in sewer systems. This article presents a model for life-cycle maintenance planning of deteriorating sewer network as a multi-objective optimisation problem that treats the sewer network condition and service life as well as life-cycle maintenance cost (LCMC) as separate objective functions. The developed model utilises Markov chain model for the prediction of the deterioration of the network. A multi-objective genetic algorithm is used to automatically locate an appropriate maintenance scenario that exhibits an optimised tradeoff among conflicting objectives. Monte Carlo simulation is used to account for LCMC uncertainties. The optimisation algorithm provides an improved opportunity for asset managers to actively select near-optimum maintenance scenario that balances life-cycle maintenance cost, condition and service life of deteriorating sewer network. A case study is used to demonstrate the practical features of developed methodology.     

Segmentation of Pipe Images for Crack Detection in Buried Sewers

Abstract:   Assessing the condition of underground pipelines such as water lines, sewer pipes, and telecommunication conduits in an automated and reliable manner is vital to the safety and maintenance of buried public infrastructure. To fully automate condition assessment, it is necessary to develop robust data analysis and interpretation systems for defects in buried pipes. This article presents the development of an automated data analysis system for detecting defects in sanitary sewer pipelines. We propose a three-step method to identify and extract cracks from contrast enhanced pipe images. This method is based on mathematical morphology and curvature evaluation that detects crack-like patterns in a noisy pipe camera scanned image. As cracks are the most common defects in pipes and are indicative of the residual structural strength of the pipe, they are the focus of this study. This article discusses its implementation on 225 pipe images taken from different cities in North America and shows that the system performs very well under a variety of pipe conditions.     

Expert judgement based maintenance decision support method for structures with a long service-life

The optimal moment at which maintenance activities should be performed on structures with long service-life to guarantee the required quality of service is hard to define, due to uncertainties in their deterioration processes. Most of the developed methods and concepts use historical data to predict the deterioration process with deterministic values as a result. Some researchers recognise that probabilistic deterioration models are required for life-cycle models but in practice, however, historical data are often scarce. Moreover, the available data often only inform about a short period of time, while maintenance strategies, technologies, materials and external circumstances change over time. Therefore, the required probabilistic deterioration models cannot be retrieved and remain unproven in life-cycle modelling so far. Hence, this article introduces an expert judgement based Condition Over Time Assessment method that quantifies the uncertainty regarding the period that is required for structural assets to deteriorate to a given condition. The proposed method utilises Cooke’s classical model, which makes use of knowledge and experience of experts, who are weighed according to their performance in judging uncertainty, to assess this period. A bridge-based experiment shows that the proposed method has the potential to provide a means to effectively plan maintenance.     

Probabilistic based integrated pipeline management system

Accurate prediction of pipeline structural and functional deterioration plays an essential role in the pipeline management process and investment planning at both project and network levels. The investigation described in this paper is primarily concerned with development of systematic concepts of pipeline management system. At present, there are still research needs for improving on the existing models and developing new methodologies of pipeline performance prediction. This paper describes the development of a probabilistic based, integrated pipeline management system, which can assist municipal engineers to make strategic investment decisions when programming pipeline maintenance and rehabilitation projects for the preservation of a pipeline network. The system developed has three major components: (1) development of standard pipe condition rating system, (2) using non-homogenous (i.e., time-related) Markovian prediction models to forecast pipeline deterioration, and (3) utilizing cost-effectiveness based prioritization program to select the optimal multi-year pipeline maintenance and rehabilitation projects.     

Probabilistic transition of condition: render facades

The service life of cement-rendered facades is closely related to the environmental conditions to which they are exposed. The probability distribution is determined for the degradation condition of render facades considering different environmental exposures. A sample of 100 render facades was subjected to meticulous fieldwork to determine their condition. The analysis focuses on the environmental factors that most influence the overall degradation of the facades, evaluated through the condition level. Probabilistic models based on Markov chains are developed to predict the evolution of facade deterioration according to exposure to outdoor environmental conditions. The proposed model provides data on the synergy between the degradation agents and the degradation condition of render facades, the average time of permanence in each degradation level, and indications of the effect of degradation on the durability of render that may be applied in the implementation and fine-tuning of maintenance procedures. A better understanding of the durability of render facades allows a more rational management of their maintenance, contributing to a reduction of their life cycle costs. The proposed stochastic model provides information that can be applied in the context of insurance policies, allowing an evaluation of the risk of failure of coatings.     

Petri net modelling of bridge asset management using maintenance-related state conditions

Bridge structures are an important part of the UK transportation network. They are also experiencing increasing rates of deterioration due to the increasing traffic volume and load intensity. Available bridge models have many restrictions due to the assumptions of the analytical method used and the means by which the model states are defined to represent the condition of the structure. These models also lack the complexity to allow detailed maintenance and renewal options to be explored. This paper presents a bridge model developed based on the Petri net (PN) approach. The method allows for detailed modelling of the individual components in the structure whilst maintaining the size of the analytical problem to a manageable size and resulting in an efficient analysis. The bridge model is formed from sub-models of each of the bridge components and takes into consideration the component deterioration process, the interaction and dependency between different component deterioration processes, along with the inspection and maintenance processes. The model states are defined based on actual degraded component conditions which they experience. It is therefore easy to relate these to the appropriate maintenance options. This gives a considerable advantage over those models based on the condition scores or ratings. The state residence times between changes in state resulting from deterioration and maintenance are governed by appropriate Weibull distributions. Thus, avoiding the restriction of constant failure rates used in Markov approaches which are rarely appropriate to model deteriorating asset conditions. The application of the model is demonstrated on a typical bridge structure where the PN model is solved using Monte Carlo simulation, the model results are also presented and discussed.     

Prediction models for sewer infrastructure utilizing rule-based simulation

Management of infrastructure projects is becoming increasingly challenging due to inherent uncertainties. The most effeective way to deal with uncertainty is to collect supplementary information and knowledge. When expensive or infeasible, quantification of uncertainty may be performed using analytical or simulation techniques. The City of Edmonton, Canada has approximately 4600 km of sewer pipes in the combined, sanitary, and storm sewer local systems with uncertainty issues related to deterioration. The City has taken a proactive approach with respect to sewer rehabilitation, as it is more cost-effeective to repair a defective pipe prior to failure rather than after a collapse. This article demonstrates an approach for predicting the condition of a sewer pipe and the related cost of rehabilitation, given the limited data. Three models are described in this article that are developed to assist the City of Edmonton to effeectively plan maintenance expenditure. Each model uses a combination of rule-based simulation and probability analysis to assist in the planning of future expenditures for sewer maintenance, thereby producing an invaluable planning tool.     

A condition assessment method for roadside assets

Infrastructure condition assessments provide key information for monitoring the quality of infrastructure, planning and budgeting of maintenance and rehabilitation activities and establishing performance goals. Although the literature is rich in condition assessment methods for roadway pavement and bridges (and to some extent for traffic signs), it is lacking such methods for roadside assets. This paper describes the development and validation of a condition assessment method for 12 roadside asset types and maintenance activities that are related to roadway safety, drainage, cleanliness and vegetation. These assets and maintenance activities are located in the area between the outside edges of the outside shoulders and the right-of-way boundaries. On divided highways, the median is also included. The developed condition assessment method consists of a set of performance standards, a visual inspection procedure to assess compliance with these standards and a statistical analysis procedure to determine the roadside level of service. To test and validate the developed method, it was applied to five highway corridors in Texas, USA, representing different climatic conditions, topography, traffic volume and population density (urban vs. rural). These field trials provided insights into the developed method, including practicality, reproducibility and probability distribution function which best represents the sample unit score.     

Probabilistic Neural Network for Reliability Assessment of Oil and Gas Pipelines

A fuzzy artificial neural network (ANN)–based approach is proposed for reliability assessment of oil and gas pipelines. The proposed ANN model is trained with field observation data collected using magnetic flux leakage (MFL) tools to characterize the actual condition of aging pipelines vulnerable to metal loss corrosion. The objective of this paper is to develop a simulation-based probabilistic neural network model to estimate the probability of failure of aging pipelines vulnerable to corrosion. The approach is to transform a simulation-based probabilistic analysis framework to estimate the pipeline reliability into an adaptable connectionist representation, using supervised training to initialize the weights so that the adaptable neural network predicts the probability of failure for oil and gas pipelines. This ANN model uses eight pipe parameters as input variables. The output variable is the probability of failure. The proposed method is generic, and it can be applied to several decision problems related with the maintenance of aging engineering systems.     

Modelling the effect of various factors on the condition of pavement marking

Poorly maintained pavement marking might certainly contribute to road accidents. The cost of road accidents is estimated to be 10–25 billion Canadian dollars annually. Accordingly, it is necessary for municipalities to develop a strategic cost-effective plan in order to renew and restripe pavement markings. Therefore, the objective of the present research is to model the effect of various factors on pavement marking conditions. Data on Alkyd paint pavement marking material are collected from the city of Ottawa, Ontario, Canada. Since the collected data from municipalities in Canada always include input variables and fail to provide output variable(s) (e.g. condition), an unsupervised neural network (UNN) model is first developed to generate the condition of pavement marking (output). Then, regression and neurofuzzy models are developed based upon the results of UNN model. The developed models are validated in which they show satisfactory results. A sensitivity analysis is performed to show the effect of changing the input variables on the developed models’ output(s).     

Characterising the effect of external factors on deterioration rates of bridge components using multivariate proportional hazards regression

Within asset management of infrastructure systems, increases in maintenance needs subject to budgetary constraints have motivated the development of tools to forecast deterioration to optimise maintenance intervention. Current bridge deterioration modelling approaches, including the evolving duration-based methods, routinely rely on a priori categorisation of bridges based on design, functional, and geographic factors to account for their effects on deterioration rates. However, such preclassification is often based on engineering judgement and may not reflect the true influence of these explanatory factors. In the current study, a proportional hazards regression-based methodology was developed to identify the most critical factors affecting deterioration using the entire unsegmented bridge database. The framework designed to perform this duration-based regression on large bridge databases is presented in this paper and results from implementation on a state inventory of over 17,000 bridges are discussed. The results provide insight into the extent that explanatory factors influence deterioration rates of different bridge components. A novel aspect of the developed framework is its ability to analyse the time-dependent effects of explanatory factors on deterioration rates over the lifecycle of the structural components. This analysis can be used to develop multivariate deterioration models and inform decision-making and prioritisation strategies.     

Integrated condition rating and forecasting method for bridge decks using Visual Inspection and Ground Penetrating Radar

The growing problem of bridge deterioration globally has imposed prominent challenges on transportation agencies, mainly in terms of ensuring safety and serviceability of the bridge infrastructure. The large number of bridges built during the 20th century has aged and produced a complex decision-making problem that departments of transportation need to deal with. Bridge management, as a particular domain of infrastructure asset management, has focused on developing methods for condition rating and deterioration modeling. The current research reviews bridge inspection practices and identifies the main defects and deterioration signs of concrete bridge decks that are typically captured by Visual Inspection (VI) and Non-Destructive Evaluation (NDE) techniques. The research introduces the Quality Function Deployment (QFD) theory and Weibull Distribution Function (WDF) as an integrated novel method to the area of bridge condition assessment and deterioration modeling. The proposed QFD condition assessment model is developed based on integrating VI and Ground Penetrating Radar (GPR) evaluation results to provide consistent condition ratings and performance predictions. The QFD model is demonstrated with a real case study and compared to other condition assessment models. Moreover, the QFD method is validated with data extracted from twenty bridge inspection reports completed by bridge inspectors and assessed by bridge experts. The developed deterioration curves using the reliability function for the Weibull distribution show absolute matching in these results through predicting the structure future performance and defining its useful service life. Accordingly, these models can enhance bridge Maintenance, Repair and Replacement (MRR) decisions since they produce reliable condition ratings and predictions that can link to proper rehabilitation action, and eventually assist in the decision making and planning for the selected MRR action. All these processes are integrated within one framework.     

4种城镇燃气管网风险评估模型的比较

对目前我国研发的3种风险评估模型的结构、管道失效因素及其占系统总评分的权重与英国 Muhlbauer 的模型进行了比较,比较了各模型中第三方损害、腐蚀、设计、误操作等一级因素下的二级、三级因素的选取及权重。分析了这些评估模型在我国城镇燃气管网风险评估实践中的适用性及可操作性。     

Inspection and maintenance of oil & gas pipelines: a review of policies

Oil and gas (O&G) pipelines are expensive assets that cross through both the ecologically sensitive and densely populated urban areas. The pipeline failure may have potentially significant consequences for both the natural and human environments. In order to maintain the integrity of O&G pipelines, inspection and maintenance processes should be governed by efficient policies. The objective of this paper is to conduct a state-of-the-art review of maintenance policies of O&G pipelines to investigate their advantages, limitations, and associated implementation issues. Maintenance policies can be categorised into corrective, preventive, predictive and proactive. Corrective maintenance policies (1940s) were based on the ‘repair when broke’ philosophy. Economic considerations shifted practice towards preventive maintenance (1970s to 1990s); later with improved inspection techniques and environmental regulations, predictive and proactive or risk-based maintenance (RBM) policies were developed. This review explicates different methodologies for RBM and related issues, e.g. uncertainties and variability, conservative assumptions, etc. Uncertainties associated with investigation and prediction of defects have been more frequently reported in the literature so far. Moreover, existing studies primarily focused on reducing the likelihood and cost of failure, whereas consideration of environmental factors in overall risk has been a relatively less addressed issue.