A 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 eventually require interventions. As road managers are trying, in general, to maximize the benefit obtained from the road network, it is in their interest to determine intervention programs, which consist of the grouping of interventions in work zones. The determination of optimal intervention programs is relatively complicated when considering single objects, but it becomes even more so when considering multiple objects embedded within a network. The objects to be included in the work zones at each time interval depend on many factors, such as the interventions to be executed on the objects, the maximum allowable length of the work zones, the traffic configurations to be used in the work zones and the available financial resources. Although some initial research in this area has been conducted, none has determined the optimal set of work zones on large infrastructure networks in a geographical information system (GIS) framework, which is necessary in the world of modern infrastructure management. In the work presented in this paper, a GIS-based program was developed to determine optimal intervention programs for large infrastructure networks using a linear optimization model, which can be linked directly to a GIS. The model includes constraints on the amount of available resources, on the length of the work zone, and on the distance between two work zones. A constraint-constructing algorithm is used in order to set up the latter two constraints. The program is illustrated by determining the optimal set of work zones for an example road network similar to the one in the canton of Wallis, Switzerland, including more than 2,000 bridges, tunnels, and road sections.

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.

The deterioration of a pavement surface can be described in terms of the presence and severity of distinct distresses, like potholes, cracking, and rutting. Each deterioration process is ordinarily described by a set of pavement indicators (e.g., number of potholes, percentage of cracks, international roughness index) that are measured during monitoring and inspection activities. Manifestly, there exist statistical correlations among the deterioration processes. For instance, cracks appearing on a road section may contribute to an increase in pothole occurrence, and vice versa. In order to mathematically formulate the statistical interdependency among deterioration processes, a Poisson hidden Markov model is proposed in this paper. The model describes the complex process of pavement deterioration, which includes the frequent occurrence of local damage (e.g., potholes) as well as the degradation of other pavement indicators (e.g., cracks, roughness). To model the concurrent frequency of local damage, a stochastic Poisson process is used. At the same time, a Markov chain model is used to depict the degradation of other pavement indicators. A numerical estimation approach using Bayesian statistics with a Markov chain Monte Carlo simulation is developed to derive the values of the model’s parameters based on historical information. The applicability of the model was demonstrated through an empirical example using data from a Japanese highway road link..