Hossein Hariri Asli

Prediction of pavement roughness is central to network-level planning, yet environmental stressors remain underrepresented in data-driven models. This study develops an input variable selection and ensemble-learning workflow to predict the International Roughness Index (IRI) and support network-level identification of deterioration risk. Segment-level roadway data for Beaumont, Texas (2019–2023; ≈11,900 segments) integrate pavement condition surveys, design and geometry attributes, maintenance and rehabilitation (M&R) records, and environmental layers describing flood inundation depth and velocity, flood susceptibility, topographic convergence, and ground deformation. Edgeworth-approximation Conditional Mutual Information (EA-CMI) and SHapley Additive exPlanations (SHAP) are used to screen and interpret predictors, and Random Forest and Extreme Gradient Boosting (XGBoost) are evaluated against a linear baseline. XGBoost achieves the strongest performance (R² ≈ 0.79 in 2019; R² ≈ 0.73 in 2023), while a SHAP-guided reduced model retains near-parity accuracy with fewer inputs, lowering data demands for practical deployment. Inter-year transfer of the model trained on the 2019 data to the 2023 observations reveals temporal drift, with larger errors concentrated among the most distressed segments. EA-CMI prioritizes structural and distress-related variables while mitigating redundancy before model fitting, and SHAP corroborates the dominant role of distress indicators while highlighting interaction patterns associated with flooding and deformation. Spatial diagnostics indicate clustering in both roughness change and transfer residuals, with deterioration corridors more frequently co-located with high flood exposure and ground movement, whereas spatially coherent improvements are more consistent with recent M&R. Overall, the workflow supports updating and interpretation of roughness predictions for resilience-oriented pavement management in hazard-prone regions.

116A finite volume (FV) method is used to solve the conjugate heat transfer through the heat sinks. In this work the flow and heat transfer development regions inside the tubes are considered. The numerical results are then compared with the available experimental data. The effects of liquid velocity through channels and their effects on heat transfer and pressure drop along microchannels are investigated. Finally, the effects of aspect ratio on heat dissipation and pressure drop in microtubes are predicted.