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Flood-induced moisture damage of flexible pavements is a serious concern for many road authorities. Reports from several studies confirm the significant structural damage that is caused by flooding on pavements. In general, there is a consensus to design and construct roads that are resistant to flooding. Simultaneously, the concept of resilience has gained popularity – frameworks and specifics are being developed to make civil infrastructure components resilient to natural and manmade disasters. This study provides a framework for quantitatively assessing the resilience of flexible pavements to flooding. The different interlinked steps consist of utilising unsaturated flow through the different layers to estimate drainage, interpretation of the results in terms of stiffness of the relevant layers, estimation of the impact of the change in stiffness on the overall structural condition of the pavement and then translating that change to a resilience index. The paper provides an illustrative example of such an estimation of resilience for a pavement. The results demonstrate the need for providing base course materials with appropriate gradation to ensure adequate hydraulic conductivity, and/or thicker surface layer, to avoid a reduction in service quality and loss of resilience for an extended period of time, in flood-prone areas. Abbreviations: CBR: California bearing ratio; D60: diameter corresponding to 60% passing in results of sieve analysis; FE: finite element; FWD: falling weight deflectometer; HMA: hot-mix asphalt; keff: effective hydraulic conductivity; ksat: saturated hydraulic conductivity; MEPDG: mechanistic empirical pavement design guide; MR: resilient modulus; MRopt: optimum resilient modulus; Q (t); Q: quality; RL: loss of resilience; R: Resilience; S Saturation; SNCratiof: post and pre-flood modified structural numbers (SNCs); SWCC: soil–water characteristic curve; wPI: percentage passing No. 200 sieve × plasticity index. [ABSTRACT FROM AUTHOR]

Field permeability of Hot Mix Asphalt (HMA) needs to be controlled to prevent excessive ingress of water into asphalt pavements, which leads to premature failure. Existing literature shows scatter in the prediction of field permeability values and relationships between factors affecting permeability are complex and are not known precisely. The objective of this paper is to present the analysis and modelling of field permeability of HMA with artificial neural networks (ANN). Permeability data from field testing at five sites, along with materials and mix data are presented. Preliminary statistical analysis to identify correlations and significant factors were conducted. A three-layer ANN was built and trained with part of the data and it was validated and also tested on two separate sets of data. The performance results indicated excellent prediction ability of the neural network. Experiments were conducted to improve the model, and the relative importance of the different factors was evaluated. Gradation and air voids were identified as two of the primary factors affecting field permeability. It is recommended that ANNs be considered to be used on a regular basis for predicting field permeability, particularly because field permeability tests are processes that require a considerable amount of time and resources. [ABSTRACT FROM AUTHOR]