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Road agencies employ different criteria and methods to compare treatment alternatives and develop maintenance and rehabilitation (M&R) programmes for their networks. The maximisation of benefits has been integrated in several leading pavement management systems and used for network-level programming since the 1980s. The approach defines benefit as the area between the performance curves with and without M&R actions based on an aggregated index, representing the overall pavement condition. Further simplifications like the screening of solutions using an efficiency frontier and the incremental benefit–cost technique have made it possible to apply the method with the available computing power at the time. Based on a case study of 1000 road sections, this paper analyses the effect of benefit maximisation on annual budget, network condition, treatment selection, trigger values and remaining life. M&R programmes minimising costs and maximising benefits are compared at the project and network level for various budget scenarios. The results show that the maximisation of benefits based on an aggregated condition index leads to substantially higher agency costs and favours the selection of expensive treatments with earlier timing, irrespective of actual failure causes. The conclusions of this work may be useful to road agencies in developing more efficient budget-allocation polices. [ABSTRACT FROM AUTHOR]

Existing pavement management systems (PMSs) aggregate short survey sections into long homogeneous sections (typically 1–5 km), aiming to reduce the amount of data and formulate a tractable optimisation problem. However, assuming that sections in a similar condition will undergo a similar deterioration of this condition is incorrect, resulting in imprecise predictions and violations of thresholds. Thus, any maintenance and rehabilitation (M&R) optimisation approach based on long road sections and a single condition indicator (aggregated or not) cannot provide applicable results at the project level. This paper provides a holistic model framework and consistent methodology for a new PMS, abolishing the need for composite condition indices, data aggregation and homogeneous sections in the M&R optimisation process. The proposed bottom-up optimisation is based on short survey sections (25–50m) and solves for optimal treatment type, timing, work-zone length and layout, minimising agency, road user and environmental life cycle costs. Instead of using just one condition indicator, the method models the effects of each treatment on multiple distress types and their combinations. Moreover, the short survey sections are grouped into larger work zones based on purely economic criteria (i.e. economies-of-scale costs). Treatment cost, temporary traffic control cost, and work-zone user cost models are derived as a function of the project size and calibrated for actual prices in Austria. Genetic algorithms are employed as a part of the solution algorithm, being capable of handling an unlimited number of road sections. Furthermore, the approach can be extended to account for budget restrictions, risk and cross-asset allocation of funds for any aging repairable infrastructure system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Periodic condition assessments of pavements together with condition predictions are the basis for investment decisions in every pavement management system (PMS). Typical approaches include surveys of distress types every 3–6 years with analysis rating and calculation of condition indices for road safety and/or structural health. Furthermore, advanced PMS prediction models allow a comparison of maintenance alternatives and an optimisation of investment strategies. This paper presents an overview of current survey and rating approaches in Germany, Switzerland and Austria, together with an impact analysis of different methods, utilised deterministic performance functions and condition threshold (trigger) values for all major distress types. The core of this paper is a comparison of common deterministic condition prediction models with discrete stochastic approaches and prediction models based on advanced regression techniques mainly from scientific literature and an innovative stochastic continuous time and continuous state space process (HOFFMANN – Process). All prediction models are applied to real-world data from condition surveys in Austria and the long-term pavement performance Database (USA) at single-section and network level. The paper provides evidence why deterministic prediction approaches are leading to substantial bias in condition distribution and remaining service life as they do not account for the stochastic nature of pavements. Classic Markov-chain approaches do not account for censoring of survey data and neglect changes in transition probabilities with increasing age. Applying common bivariate and multiple regression techniques may also lead to certain bias due to collinearity effects and specification bias. The paper provides mathematical evidence on ways to avoid these shortcomings based on the presented innovative stochastic process leading to a higher reliability in condition assessment, rating and accuracy of condition predictions. The aspects of censoring, distress-specific assignment and optimisation of treatments with this new HOFFMANN-process will be covered in forthcoming papers. [ABSTRACT FROM PUBLISHER]