This study aims to explore factors affecting passenger car and truck driver injury severity in passenger car-truck crashes. Police-reported crash data from 2007 to 2017 in Canada are collected. Two-vehicle crashes involving one truck and one passenger car are extracted for modeling. Different injury severities are not equally represented. To address the data imbalance issue, this study applies four different data imbalance treatment approaches, including over-sampling, under-sampling, a hybrid method, and a cost-sensitive learning method. To test the performances of different classifiers, five classification models are used, including multinomial logistic regression, Naive Bayes, Classification and Regression Tree, support vector machine, and eXtreme Gradient Boosting (XGBoost). In both the passenger car driver and truck driver injury severity analysis, XGBoost combined with cost-sensitive learning generates the best results in terms of G-mean, area under the curve, and overall accuracy. Additionally, the Shapley Additive Explanations (SHAP) approach is adopted to interpret the result of the best-performing model. Most of the explanatory variables have similar effects on passenger car and truck driver fatality risks. Nevertheless, six variables exhibit opposite effects, including the age of the passenger car driver, crash hour, the passenger car age, road surface condition, weather condition and the truck age. Results of this study could provide some valuable insights for improving truck traffic safety. For instance, properly installing traffic control devices could be an effective way to reduce fatality risks in passenger car-truck crashes. Besides, passenger car drivers should be extremely cautious when driving between midnight to 6 am on truck corridors.

For culverts installed in soft soil, the rigid composite pile foundation is used for soil treatment, and the stresses around the culverts are complicated. Centrifuge experiments and numerical simulations are conducted to investigate the vertical earth pressures and soil settlement over a slab culvert under high backfill in soft soil, and the foundations are treated with rigid composite piles. The influences of lateral pile length and pile spacing on the earth pressures at the top of the culvert are analysed, and they have significant effects on the vertical earth pressure coefficients. It also indicates a strong correlation between the backfill height and the vertical earth pressure coefficients. For a high-backfill culvert, there is a uniform settlement surface where the settlement of the interior and exterior soil prisms is at the same level. According to the experiments, the height of the uniform settlement surface and the vertical earth pressures decrease with increasing the lateral pile length and pile spacing. Numerical simulations were conducted using finite element software Midas GTS to evaluate the performance of the experiments. Further, recommendations for selecting an appropriate pile composite foundation for slab culvert in soft soil are provided.

This study quantifies vertical earth pressure on the roofs of box culverts under high fills in valley terrain using centrifuge model tests with expanded polystyrene (EPS) geofoam for load mitigation. We compare buried-type culverts with valley-terrain high-fill culverts and isolate the effects of the EPS installation height and panel thickness on the roof pressure and the associated concentration factor. The analysis of fill settlement elucidates the terrain-dependent load reduction mechanism and the efficacy of EPS panels. The results show that the roof pressure increases with EPS installation height but decreases and then plateaus once the panel thickness exceeds 75 cm; the load reduction benefit weakens when the installation height exceeds 2 m. Optimal performance is achieved with panels installed at 2 m and with a 75 cm thickness, which lowers applied loads while maintaining structural stability. These findings clarify soil–structure interactions in complex topography and provide practical guidance for deploying EPS in high-fill valley projects.