Condition prediction and estimation of service life in the presence of data censoring and dependent competing risks

Abstract

An accurate estimation of service life is of primary interest in pavement management systems limiting the time frame for maintenance and rehabilitation (M&R) treatments. Common condition prediction models are derived by regression analysis at the road network level based on empirical data from periodic condition surveys. If a particular section has not failed prior to the last survey or the condition has improved (e.g. due to treatment), it is considered as censored. If censoring is neglected the performance functions, service lives and estimated costs may show substantial bias. The authors who acknowledge this problem have used standard statistical (survival analysis) techniques accounting for censoring. However, any road section may fail due to different but dependent competing failure causes (risks), each leading to treatments. This constitutes a special type of censoring that cannot be addressed with traditional survival analysis methods relying on the assumption of independent censoring. As the number of failure causes usually exceeds one (e.g. fatigue, permanent deformation, thermal cracking), this case is quite common. Moreover, the time until a first failure depends on the sign and degree of correlation between present failure types being modelled by the overall survival function. This paper presents a critical review and comparison of common regression, Markov chain and survival analysis models with and without correlated competing risks based on computer-generated data. Using performance history and distress progression models at the section level in combination with survival analysis improves the accuracy of predictions in comparison. Furthermore, the paper proposes a simultaneous modelling of joint and marginal service life distributions based on copula functions as generalised solution accounting for dependence between competing risks. As the focus of this paper is on condition prediction with censored data, the distress-specific planning and optimisation of treatments will be covered in forthcoming papers.

Optimisation of pavement maintenance and rehabilitation activities, timing and work zones for short survey sections and multiple distress types

“…This follows from the fact that, for a series system, the overall survival probability at any time is the product of the survival probabilities for the individual distress types (in the case of uncorrelated distresses). Details about the estimation of service life with censored data and correlated competing risks can be found in Donev and Hoffmann (2017a).…”

“…The power parameter implies slightly progressive deterioration (β 2 = 1.20) for surface defects, degressive deterioration (β 2 = 0.80) for rutting and progressive development (β 2 = 2.90) for alligator cracking. The values are taken from previous research (Donev and Hoffmann 2017a), having no impact on the results presented here (except in the case of soft constraints for alligator cracking). Figure 15 (a,c,e) shows the condition distribution at the network level (1000 sections) for the three distress types without optimisation of the work zone (see Figure 14(a)).…”

Exploring Generational Private Mobility Paradigm Shifts through Duration Modeling Analytics: A Greek Case Study

“…On the other hand, if b is less than 1, then that value of the explanatory variable reduces the probability of the event occurring. It is worth noting that when the effect of only one variable on duration is examined, the Cox proportional hazards model and the Kaplan-Meier method of survival analysis are practically equal [43,45]:…”