Abstract
The design of pavement systems relies on sensitivity of their performance to moisture variations; yet traditional sensitivity analysis have limitations in understanding the complex interplays affecting pavement performance, and they can be computationally expensive. This study introduces a novel approach of employing System Dynamics Models (SDM) to assess the factors influencing flexible pavement performance under moisture variations. A data-driven Global Sensitivity Analysis (GSA) can complement traditional methods such as Local Sensitivity Analysis (LSA) and feature ranking, providing a deeper understanding of interconnections. Utilizing Latin Hypercube Sampling, input variable samples are generated to facilitate data-driven GSA. To balance prediction accuracy with computational efficiency, a Random Forest ensemble is developed as a multitarget surrogate model. Key findings underscore the interplay between resilience modulus and pavement layer thickness as well as the significance of subgrade soil particle size for coarse-grained subgrades, while subbase aggregate properties show negligible influence. In fine-grained subgrades, soil plasticity index plays a vital role in peak surface deflection. Additionally, the study uncovers the critical impact of precipitation variables on deflection timing. By predicting these critical timings, informed traffic management measures can be contemplated, enhancing road user safety. This research highlights the importance of tailoring pavement design strategies to specific subgrade characteristics and environmental conditions. Moreover, the study recommends adoption of the proposed sensitivity analysis workflow to delve into the various parameters interactions in infrastructure system design.
Published Version
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