Deterioration modeling

Schematic deterioration of an asset over time. The increase in performance indicators represent a maintenance action.

Deterioration modeling is the process of modeling and predicting the physical conditions of structures or infrastructure. The condition of infrastructure is represented either using a deterministic index or the probability of failure. Examples of such performance measures are pavement condition index for roads or bridge condition index for bridges. For probabilistic measures, which are the focus of reliability theory, probability of failure or reliability index are used. Deterioration models are instrumental to infrastructure asset management and are the basis for maintenance and rehabilitation decision-making. The condition of all physical infrastructure degrade over time. A deterioration model can help decision-makers to understand how fast the condition drops or violates a certain threshold.

Types of deterioration models

Deterioration models are either deterministic or probabilistic. Deterministic models cannot entertain probabilities. Probabilistic models, however, can predict both the future condition and the probability of being in that certain condition.

Deterministic models

Deterministic models are simple and intelligible, but cannot incorporate probabilities. Deterioration curves solely developed based on age are an example of deterministic deterioration models. Traditionally, most mechanistic and mechanistic-empirical models are developed using deterministic approaches, but more recently researchers and practitioners have become interested in probabilistic models.

Probabilistic models

The bathtub curve hazard function (blue, upper solid line) is a combination of a decreasing hazard of early failure (red dotted line) and an increasing hazard of wear-out failure (yellow dotted line), plus some constant hazard of random failure (green, lower solid line).

Examples of probabilistic deterioration models are the models developed based on reliability theory, Markov chain and machine learning. Unlike deterministic models a probabilistic model can incorporate probability. For instance, it can tell that in five years a road is going to be in a Poor condition with a probability of 75%, and there is a 25% probability that it will stay in a fair condition. Such probabilities are vital to the development of risk assessment models. If a state or class of the performance measure is of interest, Markov models and classification machine learning algorithms can be utilized. However, if decision-makers are interested in numeric value of performance indicators, they need to use regression learning algorithms. A limitation of Markov models is that they cannot consider the history of maintenance, which are among important attribute for predicting the future conditions. Deterioration models developed based on machine learning do not have this limitation. Furthermore, they can include other features such as climatic attributes and traffic as input variables.