Aggregation of condition survey data in pavement management: shortcomings of a homogeneous sections approach and how to avoid them. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Donev, Valentin; Hoffmann, Markus; Blab, Ronald
• Is Part Of: Structure and infrastructure engineering. Volume 17:Issue 1(2021)
• Page Start: 49
• Page End: 61
• Publication Date: 2021-01-02
• Subjects: Data processing -- infrastructure -- life cycle costs -- optimisation -- pavement management -- predictions -- rehabilitation -- roads & highways -- service life; Structural analysis (Engineering) -- Periodicals; Structural engineering -- Periodicals; Buildings -- Performance -- Periodicals; 620.005
• DOI: 10.1080/15732479.2020.1730409
• Languages: English
• ISSNs: 1573-2479

Abstract

Abstract: A common feature of most pavement management systems is the aggregation of condition data collected from periodic surveys to appropriate long homogeneous sections, aiming to reduce the amount of data to be analysed. It can be shown that regardless of the methods used for sectioning and prediction, homogeneous sections cannot describe the true variation in condition and failure time, leading to either over- or underestimation of service life and hence suboptimal selection of timing and type of maintenance and rehabilitation (M&R) treatments. Furthermore, the generation of homogeneous sections hinders the derivation of project-level performance models from historical condition surveys due to shifting of sections and greatly reduces the number of possible work-zone solutions in the optimisation. This paper illustrates the deviations resulting from different levels of data aggregation based on a case study of 1000 road sections. The results of common sectioning algorithms are then compared to those derived from a LCC-optimisation approach based on accurate condition predictions on short survey sections and work-zones based on economies of scale. The conclusions of this work may be useful to road agencies in avoiding substantial drawbacks of data aggregation and improving project-level M&R treatment recommendations based on the same input condition data.