Developing a new road deterioration model incorporating flooding

Abstract

Currently, there is no road deterioration (RD) model that can address pavement performance with flooding.Therefore, the aim of this work was to develop an RD model that considers flooding and pre-and post-flood strategies. The case study is road group(s) in Queensland, Australia, based on the 34 000 km road database of its main roads authority, as Queensland was severely affected by floods in 2011. This paper provides a brief literature review and proposes an approach to develop an RD model based on a non-homogeneous Markov transition probability matrix. The new RD models are developed for a number of road groups based on roughness and rutting data collected since 2000. Detailed results on the derived roughness-based and rutting-based RD models with flooding for two selected road groups are highlighted. The new RD models show significant pavement deterioration at different probabilities of flooding events, especially at higher probabilities. It is believed that these results will help road authorities in managing flood-damaged roads with appropriate treatments and optimum budget.

Estimating Pavement’s Flood Resilience

“…Recently, Khan et al (2014aKhan et al ( , 2017c and Khan (2017) developed project and network levels roughness and rutting-based road deterioration (RD) models at different probabilities of flooding. Additionally, Khan (2017) and Khan et al (2017a) determined pavement responses during flooding using the Mr loss values in granular and subgrade layers.…”

“…Using the roughness prediction model of AASHTO (2008) (based on AASHTO's pavement design guide of 2008) and the Highway Development and Management Model (HDM-4) (Odoki and Kerali 2000), they observed poor pavement performance after a flood when Mr was reduced. The impact of pavement performance due to different probabilities of flooding was shown in Khan et al (2014a). Both these studies (Khan et al 2014a;Khan 2017) provided IRI versus time and rutting versus time because of a flood.…”

“…The impact of pavement performance due to different probabilities of flooding was shown in Khan et al (2014a). Both these studies (Khan et al 2014a;Khan 2017) provided IRI versus time and rutting versus time because of a flood. An after-flood effect on pavement roughness was estimated while assessing flood risk for the road network (Khan 2017;Khan et al 2017b), which gives ΔIRI due to a flood.…”

Development of pavement performance prediction model for bituminous hot mix asphalt on interurban road networks

“…Bu sebeple literatürde, üstyapıların performansının zamanla değişimini deterministik ve stokastik yöntemlerle modelleyen birçok çalışmaya rastlanmaktadır. Bahsedilen bu çalışmalarında, doğrusal veya doğrusal olmayan regresyon yöntemleri [7]- [10], otoregresyon ve zaman serisi yöntemleri [11], [12], MARS yöntemi [13], yapay zeka modelleme tekniklerinden özellikle yapay sinir ağları (YSA) yöntemi [14] ve stokastik yöntemlerden markov zinciri [15]- [17] ve bayesian teorisi [14], [18] yöntemlerinin başlıca tercih edilen yöntemler olduğu dikkat çekmektedir [19]. Araştırmacılar genel olarak, üstyapının mevcut performansını tasvir eden PCI, uluslararası düzgünsüzlük indeksi (IRI), üstyapı servis indeksi (PSI) vb.…”

Editorial

“…Khan et al (2014) present models that take account of flooding in analysis of road deterioration, predict pavement performance for different flooding probabilities and allow the selection of optimum treatment for post-flood rehabilitation for a specific road group.…”