Polylactide composites containing bamboo fiber treated with various coupling agents were prepared using melt compounding. 3aminopropyltriethoxysilane, vinyltrimethoxysilane, maleic anhydride, and acrylic acid were used as the coupling agents to improve the mechanical properties of polylactide and bamboo fiber composites. In addition, to investigate the effect of fiber diameter on mechanical properties, either split bamboo fiber or fibrillar bamboo fiber was used as reinforcement. The composites containing split bamboo fiber treated with 3aminopropyltriethoxysilane and acrylic acid exhibited the highest tensile strength of all composites prepared. In addition, the composites containing fibrillar bamboo fiber showed a marked improvement in tensile strength. To investigate the biodegradability of the composites, an enzymatic degradation test was performed using Proteinase K, a commonly used enzyme for polylactide degradation. All the polylactide composites containing bamboo fiber, regardless of which coupling agent was applied, showed a similar trend in enzymatic degradation to that of neat polylactide.

Infrastructure usability becomes limited during a heavy snowfall event. In order to prevent such limitations, damage calculations and a decision-making process are needed. Snow-removal routing is a type of relevant disaster-prevention service. While three-dimensional (3D) models support these measures, they contain complex information regarding compatibility. This study generates a city-level semantic information model for roads using CityGML, an open standard data schema, and calculates the optimal snow removal route using this model. To this end, constraint conditions are analyzed from the viewpoint of a snow-removal vehicle, and a road network for an optimal route is applied to a 3D road information model. Furthermore, this study proposes a new algorithm that reduces the number of nodes used in the optimal route calculation, and a genetic algorithm is used to find the solution of the formulated objective function. This new algorithm reduces the number of nodes to less than two-thirds that of the original numbers when determining the optimal travel route for snow-removal vehicles in the target area.