Pavement Structures Damage Caused by Hurricane Katrina Flooding

Zhang, Zhongjie; Wu, Zhong; Martinez, Mark; Gaspard, Kevin

doi:10.1061/(asce)1090-0241(2008)134:5(633)

Cited by 69 publications

(39 citation statements)

References 2 publications

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“…with median values of major roads higher by 10 cm than those of local roads to illustrate that pavement thickness is a vulnerability factor as indicated by Zhang et al (2008) and acknowledge that major roads undergo a more rigorous 10 design process than local roads. Table B3 shows the parameters of the fragility function when inundation depth was used as an intensity measure.…”

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confidence: 99%

Estimating the risk related to networks: a methodology and an application on a road network

Hackl

Lam

Heitzler

et al. 2018

Preprint

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Abstract. Networks, such as transportation, water, and power, are critical lifelines to society. Managers plan and execute interventions to guarantee the operational state of their networks under various circumstances, including after the occurrence of (natural) hazard events. Creating an intervention program demands knowing the probable consequences (i.e., risk) of the various hazard events that could occur to be able to mitigate their effects. This paper introduces a methodology to support network managers in the quantification of the risk related to their networks. The method emphasizes the integration of the spatial and 5 temporal attributes of the events that need to be modeled to estimate the risk. This work then demonstrates the usefulness of the methodology through its application to design and implement a risk assessment to estimate the potential impact of flood and mudflow events on a road network located in Switzerland. The example includes the modeling of (i) multiple hazard events, (ii) their physical and functional effects throughout the road network, (iii) the functional interrelationships of the affected objects in the network, (iv) the resulting probable consequences in terms of expected costs of restoration, cost of traffic changes, and 10 duration of network disruption, and (v) the restoration of the network.

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confidence: 99%

Estimating the risk related to networks: a methodology and an application on a road network

Hackl

Lam

Heitzler

et al. 2018

Preprint

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“…Another study revealed that the average pavement strength and subgrade modulus loss were 18% and 25% respectively due to flooding. The flooded pavements had higher deflection, and as a result they had lower strength and modulus (Zhang, et al 2008). A flood reduces moduli of pavement layers, and as a consequence a pavement's strength decreases and deflection increases.…”

Section: List Of Tablesmentioning

confidence: 96%

“…Similarly, Zhang, et al (2008) assessed roads after the flood due to the hurricane Katrina in 2005 and then Rita, mainly at New Orleans of USA. In total, 3,220 km roads were flooded for 5 weeks.…”

Section: Impact Of Mr Loss On Pavement Performancementioning

confidence: 99%

“…Although, Helali, et al (2008) and Zhang, et al (2008) estimated pavement damage after a flood, no study so far addressed pavement performances during flooding or suggested a prediction model. Ullidtz (1998) used two existing models, i.e., AASHTO and HDM-III models, to check pavement performances at no flood condition.…”

Section: Impact Of Mr Loss On Pavement Performancementioning

confidence: 99%

“…Some studies assessed pavement responses after flooding (Helali, et al 2008;Zhang, et al 2008;Yuan and Nazarian, 2003). However, they used the deflection data for the analysis, and did not cover pavement performances.…”

Section: Flood-resilient Pavementmentioning

confidence: 99%

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Improvement of a Pavement Management System Incorporating Flooding

Khan¹

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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 LifeCycle 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.

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