Nam Lethanh

Deterioration Forecasting Model with Multistage Weibull Hazard Functions

In this paper, a time-dependent deterioration forecasting model is presented. In the model the deterioration process is described by transition probabilities, which are conditional upon actual in-service duration. The model is formulated by the multistage Weibull hazard model defined by using multiple Weibull hazard functions. The model can be estimated based upon inspection data that are obtained at discrete points in time. The applicability of the model and the estimation methodology presented in this paper are investigated against an empirical data set of highway utilities in the real world.

A Bayesian Estimation Method to Improve Deterioration Prediction for Infrastructure System with Markov Chain Model

In many practices of bridge asset management, life cycle costs are estimated by statistical deterioration prediction models based upon monitoring data collected through inspection activities. In many applications, it is, however, often the case that the validity of statistical deterioration prediction models is flawed by an inadequate stock of inspection dates. In this paper, a systematic methodology is presented to provide estimates of the deterioration process for bridge managers based upon empirical judgments at early stages by experts, and whereby revisions may be made as new data are obtained through later inspections. More concretely, Bayesian estimation methodology is developed to improve the estimation of Markov transition probability of the multi-stage exponential Markov model by Markov chain Monte Carlo method using Gibbs sampling. The paper concludes with an empirical example, using the real world monitoring data, to demonstrate the applicability of the model and its Bayesian estimation method in the case of incomplete monitoring data.

Use of exponential hidden Markov models for modelling pavement deterioration

In this paper, the potential of using an exponential hidden Markov model to model an indicator of pavement condition as a hidden pavement deterioration process, i.e. one that is not directly measurable, is investigated. It is assumed that the evolution of the values of the pavement condition indices can be adequately described using discrete condition states and modelled as a Markov process. It is also assumed that the values of the indices can be measured over time and represented continuously using exponential distributions. The potential advantage of using such a model is illustrated using a real-world example.

A real option approach to determine optimal intervention windows for multi-national rail corridors

In this paper, a real option approach to determine the optimal time to execute interventions on rail infrastructure, when it is not known for certain which intervention is to be executed, is presented (i.e. the optimal intervention window). Such an approach is useful in the management of rail infrastructure that belongs to a multi-national rail corridor where multiple railway organizations, ideally, should coordinate their maintenance interventions, years in advance, to minimize service disruptions. The approach is based on an adaptation of the Black and Scholes differential equation model used to value European call options in financial engineering. It is demonstrated by determining the optimal intervention window for infrastructure in a fictive rail corridor.

A model for the evaluation of intervention strategies for bridges affected by manifest and latent deterioration processes

Markov models are often used in bridge management systems to evaluate intervention strategies (ISs) for bridges affected by manifest deterioration processes (MnDPs). These models do not directly take into consideration the effect of latent deterioration processes (LtDPs) on the object, i.e. the deterioration that might occur due to natural hazards (e.g. earthquakes and floods). In cases where there is a negligible probability of the occurrence of natural hazards, this is justified, otherwise it is not. In this paper, a model is proposed that can be used to evaluate ISs for bridge elements and bridges considering both MnDPs and LtDPs. The model is an extension of the Markov models, and includes condition states (CSs) that can occur due to both MnDPs and LtDPs, as well as the probabilities of transition (p.o.ts) between them. The contributions to the p.o.ts due to MnDPs are initially estimated using well-established methods and adjusted for the contributions to the p.o.ts due to LtDPs, which are estimated using fragility curves and adjusted considering element dependencies, i.e. how the elements of a bridge work together. The use of the model is demonstrated by predicting the future CSs of a bridge affected by both MnDPs and LtDPs.

Determining an Optimal Set of Work Zones on Large Infrastructure Networks in a GIS Framework

AbstractA road network consists of multiple objects that deteriorate over time with different speeds of deterioration. In order to provide an adequate level of service over time, these objects will...

Investigation of a static and a dynamic neighbourhood methodology to develop work programs for multiple close municipal infrastructure networks

Interventions on infrastructure networks in cities cause disruptions to the services provided by those but also to other networks that have to be at least partially shut down for the interventions executed. Due to these effects, there is substantial benefit to be obtained by grouping interventions on networks that are spatially close to one another. This benefit is principally due to reduced costs of intervention and reduced service disruption. In this paper, two intervention grouping methodologies to develop work programs for infrastructure networks are investigated. The first is based on static, the second is based on dynamic grouping. The two methodologies are investigated by developing work programs on multiple infrastructure networks in an urban area and compared against the same methodologies, albeit without coordination. In the example, interventions on the objects of five different infrastructure networks are grouped based on failure probability of the objects and their closeness. It is found that the dynamic grouping methodology results in work programs that result in a better consideration and prioritisation of objects that are in urgent need for an interventi, while accounting for the synergies that can be created due to efficient coordination. The advantages, disadvantages and future research directions are discussed.

Investigation of the use of a Weibull model for the determination of optimal road link intervention strategies

In this paper, a probabilistic model for the determination of optimal intervention strategies (OISs) for a road link composed of multiple objects that are affected by gradual deterioration processes is investigated. The model is composed of a deterioration part and a strategy evaluation part. In the deterioration part, a Weibull hazard function is used to represent the deterioration of the individual objects, where the values of the model parameters are to be estimated using inspection data. A threshold condition state (CS) for each object is defined, at which an intervention must be executed. The results of the deterioration part are used as inputs in the strategy evaluation part, in which OISs for individual objects and for the link as a whole are determined. The determination of the optimal strategies takes into consideration impacts on multiple stakeholders. The model is demonstrated by determining the OISs for a fictive road link composed of one bridge and two road sections. The main strengths of the methodology are that past deterioration is taken into consideration and that it is possible to consider the execution of interventions simultaneously and, therefore, associated reductions in impacts that normally occur when interventions are grouped. The main weakness of the methodology is that the condition of the objects is represented using only two CSs, i.e. fully operational and not fully operational.

Evaluation of intervention strategies for a road link in the Netherlands

Purpose – The purpose of this paper is to investigate the use of the impact hierarchy and the optimization model to determine the optimal intervention strategy for a road link composed of multiple objects. The paper focusses on the results of a case study of intervention project on A20 road link in Rotterdam, the Netherlands. Design/methodology/approach – The study was a case study research. It describes briefly the impact hierarchy and its link to the optimization model, and then focussed on analyzing the results obtained from running the model. In order to understand the influence of various factors affecting the results of optimization, sensitivity analysis was performed. Findings – The proposed hierarchy is suitable to be used to support the determination of optimal intervention strategies (OISs) for public road. From the case study, it was also realized that optimal intervention strategy can be changed due to not only intervention costs incurred by the owner, but also due to the setup of traffic configuration during the execution of interventions since the impacts incurred to users, directly affected public, and indirectly affected public are significantly different from one traffic configuration to the others. The optimal intervention strategy also depends greatly on the factors of deterioration during the operation of the infrastructure objects. Research limitations/implications – In the impact hierarchy, some impact factors are difficult to be quantified, e.g., the long-term economic impacts on the region where having intervention projects. The use of only exponential function for impacts could be oversimplified the actual behavior of the impacts. Other functional form should be investigated to be used within the framework of the optimization model. Practical implications – The proposed hierarchy and the optimization model could be used in practical situation for determination of OISs for multiple objects within a road link. Originality/value – This paper contributes to the body of knowledge of stakeholder analysis in the field of infrastructure asset management. It also gives a guideline and tool for infrastructure administrators to select the OISs for their infrastructure network

Determination of Markov Transition Probabilities to be Used in Bridge Management from Mechanistic-Empirical Models

Many bridge management systems use Markov models to predict the future deterioration of structural elements. This information is subsequently used in the determination of optimal intervention strategies and intervention programs. The input for these Markov models often consists of the condition states of the elements and how they have changed over time. This input is used to estimate the probabilities of transition of an object from each possible condition state to each other possible condition state in one time period. A complication in using Markov models is that there are situations in which there is an inadequate amount of data to estimate the transition probabilities using traditional methods (e.g., due to the lack of recording past information so that it can be easily retrieved, or because it has been collected in an inconsistent or biased manner). In this paper, a methodology to estimate the transition probabilities is presented that uses proportional data obtained by mechanistic-empirical models of the deterioration process. A restricted least-squares optimization model is used to estimate the transition probabilities. The methodology is demonstrated by using it to estimate the transition probabilities for a reinforced concrete (RC) bridge element exposed to chloride-induced corrosion. The proportional data are generated by modeling the corrosion process using mechanistic-empirical models and Monte Carlo simulations. DOI: 10.1061/(ASCE)BE.1943-5592.0001101.