Improvement of a Pavement Management System Incorporating Flooding

Abstract

Natural disasters can cause severe damage to infrastructure such as the road network. Currently, a Pavement Management System (PMS) does not incorporate flooding in a Life Cycle Analysis (LCA). A few Road Deterioration (RD) models have addressed flooding, but they have limitations. As a result, there are not any comprehensive RD models that can incorporate flooding in pavement performances. In addition, no optimum life-cycle road maintenance strategy is available. No study investigated the pavement performances because of a loss in Modulus of Resilience (Mr) at granular and subgrade layers during extreme moisture intrusion. The derivation of pre- and post-flood road maintenance strategies and a flood risk assessment should also be incorporated in a PMS.As a case study, this research has considered the January 2011 flood of Queensland, Australia, and has used 34,000 km road database of the Queensland’s main roads authority. The major objectives of this study are to derive: i) network and project level roughness and rutting-based RD models with flooding, ii) pavement performances due to Mr loss at granular and subgrade layers, iii) flood-resilient pavements, iv) optimum road maintenance strategies at without flood, pre- and post-flood scenarios, and v) pavements’ flood risks. The current scope covered the pavements that are affected after a flood, but are not washed away completely and need rehabilitation for structural strengthening.This research has used a probabilistic approach for deriving the RD models, which are valid at both network and project levels. Moreover, the proposed RD models can estimate road deterioration after a flood at different probabilities of flooding.The actual roughness and rutting vs. time data are assessed for the representative road groups or site specific roads to get the transition probability matrices for with and without flooding conditions, which are used in a Monte Carlo simulation. The new RD models show significant pavement deterioration at different probabilities of flooding events. The results are found valid with actual data for about 2 to 3 years after the January 2011 flood. A t-test also supports this match. A pavement’s performance due to Mr losses at granular layers is checked using the two renowned roughness models, which results are found close match with the actual after flood data and RD models.All these results are used estimating pavements flood resilience from three techniques, i.e., i) using the RD modelling results and an indicator of Change in International Roughness Index (∆IRI) in year 1 over the probability of flooding (∆IRI/Pr); ii) with the indicator of ∆IRI in year 1 over Mr loss (∆IRI/MrL); and iii) using the flood-risk consequences. Expectedly, a flood-resilient pavement performed better in the life-cycle.The study has derived optimum pavement maintenance strategies at without flood, pre- and post-flood conditions for the Queensland roads authority as a case-study. About $17.8bn is needed in the next 20 years at normal condition to maintain its flexible and composite roads at 4.0 IRI. The post-flood strategy framework uses the new RD models for predicting after flood deterioration and the Highway Development and Management model (HDM-4) for getting optimum solutions. The unconstrained budget solution requires $49.7bn to keep the network at an excellent condition, while the constrained one provided a reasonable solution with about $26.1bn in life-cycle. The pre-flood maintenance strategy considered an effective approach by upgrading a pavement’s structural strength now with a thin overlay, and then evaluating pavement life-cycle performance if a flood comes in different years for predicting after-flood deterioration using the RD models and selecting cost effective treatments utilising the HDM-4. The total pre-flood strategy cost varied within the range of $37bn to $38bn. Comparing to a post-flood strategy, the pre-flood strategy can maintain the network better and provides positive economic benefits.Finally, the current study aimed to evaluate pavement performances before and after a flood. The roughness vs. time data were used to get a flood and the time gap between two floods was considered as likelihood. Distribution changes of roughness data before and after a flood have been used to calculate flood consequences, and then risk results. The flood consequence and risk results were validated with actual data for two road groups.A road authority can use the RD models for an after flood road deterioration prediction to select appropriate post-flood treatments. Moreover, pavements flood-resilience, life-cycle performances and approaches for the pre- and post-flood strategies may be used for efficiently managing roads with flooding. Any road authority may plan converting flood prone roads into resilience ones for better life-cycle performances. The pavement’s flood resilience has addressed one of the major challenges of climate change and could be used for an innovative pavement design. The research findings are useful for sound strategic planning and sustainable road asset management, as it addresses the impact of flooding events in LCA. All these help in improving a PMS incorporating flooding.