Deterioration modeling

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).
Schematic deterioration of an asset over time. The increase in performance indicators represent a maintenance action.
A road deteriorates over time, and its surface roughness increases. The road is located in Texas.

Deterioration modeling is the process of modeling and predicting the physical conditions of equipment, structures, infrastructure or any other physical assets. 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.[1][2] Deterioration models are instrumental to infrastructure asset management and are the basis for maintenance and rehabilitation decision-making.[3][4] 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.[5]

Traditionally, most municipalities have been using deterioration curves for deterioration modeling.[5] Recently, more complex methods based on simulation, Markov models and machine learning models have been introduced. A well-known model to show the probability of failure of an asset throughout its life is called bathtub curve. This curve is made of three main stages: infant failure, constant failure, and wear out failure. In infrastructure asset management the dominant mode of deterioration is because of aging, traffic, and climatic attribute. Therefore, the wear out failure is of most concern.[6][7]

  1. ^ Melchers, R. E. (2002), “Structural Reliability Analysis and Prediction,” 2nd Ed., John Wiley, Chichester, UK.
  2. ^ Piryonesi, Sayed Madeh; Tavakolan, Mehdi (9 January 2017). "A mathematical programming model for solving cost-safety optimization (CSO) problems in the maintenance of structures". KSCE Journal of Civil Engineering. 21 (6): 2226–2234. doi:10.1007/s12205-017-0531-z.
  3. ^ Piryonesi, S. M.; El-Diraby, T. E. (2020) [Published online: December 21, 2019]. "Data Analytics in Asset Management: Cost-Effective Prediction of the Pavement Condition Index". Journal of Infrastructure Systems. 26 (1). doi:10.1061/(ASCE)IS.1943-555X.0000512.
  4. ^ "The IAM (Institute of Asset Management): Asset Management - an Anatomy".
  5. ^ a b El-Diraby, T. E., Kinawy, S., & Piryonesi, S. M. (2017). A Comprehensive Review of Approaches Used by Ontario Municipalities to Develop Road Asset Management Plans (No. 17-00281)
  6. ^ Ens, A. (2012). Development of a flexible framework for deterioration modelling in infrastructure asset management.
  7. ^ "Piryonesi, S. M. (2019). The Application of Data Analytics to Asset Management: Deterioration and Climate Change Adaptation in Ontario Roads (Doctoral dissertation)".