Infrastructure deterioration models are an integral part of asset management. Deterioration models are used to predict future asset condition and to estimate funding requirements. The purpose of this research is to develop a framework to create infrastructure deterioration models. An overview of the various types of deterioration models is included, presenting the advantages and disadvantages of each type. Existing deterioration model frameworks are also considered. A deterioration modelling framework is then proposed. The selection of the model type, calibration and validation is presented. The framework is then applied to two case studies. The first case study involves a comparison of three pavement deterioration models, created for the City of Oshawa for use in their asset management system. The second case study involves modelling sewer deterioration. This model has been developed to explore the relationship between age, material and deterioration in trunk sewers.

Infrastructure deterioration models are an integral part of asset management. Deterioration models are used to predict future asset condition and to estimate funding requirements. The purpose of this research is to develop a framework to create infrastructure deterioration models. An overview of the various types of deterioration models is included, presenting the advantages and disadvantages of each type. Existing deterioration model frameworks are also considered. A deterioration modelling framework is then proposed. The selection of the model type, calibration and validation is presented. The framework is then applied to two case studies. The first case study involves a comparison of three pavement deterioration models, created for the City of Oshawa for use in their asset management system. The second case study involves modelling sewer deterioration. This model has been developed to explore the relationship between age, material and deterioration in trunk sewers.

Across a large part of the UK highways network the carriageway and pavement foundations are drained by Highway Filter Drains (HFDs). A HFD is a linear trench constructed either at the pavement edge or central reserve, fitted with a porous carrier pipe at the base and backfilled with an initially highly porous aggregate material. This arrangement enables the swift removal of surface runoff and subsurface water from the pavement system minimising road user hazards and eliminating risk of structural damage to the pavement sub-base. The highly porous backfill filters throughout its operational life fines washed from the pavement wearing course or adjacent land. HFDs have been found to be prone to collecting near the basal sections (pipe) or surface layers contaminants or detritus that causes the filter media to gradually block. The process has been defined as HFD clogging and it has been found to lead to reduced drainage capacity and potentially severe drop of serviceability. O&M contractual agreements for DBFO projects usually propose in-service and handback requirements for all assets included in the concession portfolio. Different performance thresholds are thus prescribed for pavements, structures, ancillary assets or street lighting. Similar definitions can be retrieved for drainage assets in such agreements, and these include HFDs. Performance metrics are defined though in a generic language and residual life (a key indicator for major assets that usually drives long-term maintenance planning) is prescribed without indicative means to evaluate such a parameter. Most of pavement maintenance is carried out nowadays using proactive management thinking and engineered assessment of benefits and costs of alternative strategies (what-if scenarios). Such a proactive regime is founded upon data driven processes and asset specific ageing / renewal understanding. Within the spectrum of road management, maintenance Life Cycle Costs are usually generated and updated on an annual basis using inventory and condition data linked to a Decision Support Tool (DST). This enables the assessment and optimisation of investment requirements and projection of deterioration and of treatment impacts aligned to continuous monitoring of asset performance. Following this paradigm shift in infrastructure management, a similar structured methodology to optimise HFD maintenance planning is desired and is introduced in this thesis. The work presented enables the identification of proactive maintenance drivers and potential routes in applying a systemised HFD appraisal and monitoring system. An evaluation of Asset Management prerequisites is thus discussed linked to an overview of strategic requirements to establish such a proactive approach. The thesis identifies condition assessment protocols and focuses on developing the means to evaluate deteriorated characteristics of in service drains using destructive and non-destructive techniques. A probabilistic HFD ageing / renewal model is also proposed using Markov chains. This builds upon existing deterioration understanding and links back to current treatment options and impacts. A filter drain decision support toolkit is lastly developed to support maintenance planning and strategy generation.

Cette thèse explore l’utilisation des réseaux Bayésiens (RB) afin de répondre à des problématiques de dégradation en grandes dimensions concernant des infrastructures du génie civil. Alors que les approches traditionnelles basées l’évolution physique déterministe de détérioration sont déficientes pour des problèmes à grande échelle, les gestionnaires d’ouvrages ont développé une connaissance de modèles nécessitant la gestion de l’incertain. L’utilisation de la dépendance probabiliste se révèle être une approche adéquate dans ce contexte tandis que la possibilité de modéliser l’incertain est une composante attrayante. Le concept de dépendance au sein des RB s’exprime principalement de deux façons. D’une part, les probabilités conditionnelles classiques s’appuyant le théorème de Bayes et d’autre part, une classe de RB faisant l’usage de copules et corrélation de rang comme mesures de dépendance. Nous présentons à la fois des contributions théoriques et pratiques dans le cadre de ces deux classes de RB ; les RB dynamiques discrets et les RB non paramétriques, respectivement. Des problématiques concernant la paramétrisation de chacune des classes sont également abordées. Dans un contexte théorique, nous montrons que les RBNP permet de caractériser n’importe quel processus de Markov. / This thesis explores high-dimensional deterioration-related problems using Bayesian networks (BN). Asset managers become more and more familiar on how to reason with uncertainty as traditional physics-based models fail to fully encompass the dynamics of large-scale degradation issues. Probabilistic dependence is able to achieve this while the ability to incorporate randomness is enticing.In fact, dependence in BN is mainly expressed in two ways. On the one hand, classic conditional probabilities that lean on thewell-known Bayes rule and, on the other hand, a more recent classof BN featuring copulae and rank correlation as dependence metrics. Both theoretical and practical contributions are presented for the two classes of BN referred to as discrete dynamic andnon-parametric BN, respectively. Issues related to the parametrization for each class of BN are addressed. For the discrete dynamic class, we extend the current framework by incorporating an additional dimension. We observed that this dimension allows to have more control on the deterioration mechanism through the main endogenous governing variables impacting it. For the non-parametric class, we demonstrate its remarkable capacity to handle a high-dimension crack growth issue for a steel bridge. We further show that this type of BN can characterize any Markov process.