The frequency of extreme weather events has increased worldwide, leading to more intense natural disasters, which pose significant threats to human life and property safety. The main form of disaster occurrence is multi-hazard coupling and multi-hazard chaining. This paper constructs a road natural disaster risk assessment model using a fuzzy comprehensive evaluation method and neural network to quantitatively analyze road disasters with multiple hazards, and provides valuable insights for the predication of road natural disaster risk. Here, ten factors, including temperature, relative humidity, precipitation, elevation, slope, slope orientation, vegetation cover, geologic lithology, historical impact factors, and road density, were selected as input variables, and risk grade was selected as the output value (the evaluation results). The remaining hidden layers use the fully connected neural network. This model was first trained using historical data (from 2011 to 2021) obtained from road networks and natural disasters in Guangxi, China. Then, taking Lingchuan County as an example, the model was used to predict the risk of natural disasters on its roads, and, finally, the prediction accuracy of the model was determined by comparing the results with actual disaster situations. This study can provide theoretical support and technical operations for the development of subsequent early warning systems.

To address the engineering challenges associated with Guilin red clay, such as its potentially low strength and unfavorable mechanical behavior, this study investigated the effectiveness of lignin and lime as modifiers. Consolidation undrained triaxial tests and scanning electron microscopy (SEM) were employed to evaluate the strength characteristics and microstructural changes in modified clay specimens with varying dosages. The results demonstrate distinct strengthening mechanisms: Lignin exhibits an optimal dosage (6%), significantly increasing cohesion and internal friction angle through physical reinforcement (“soil fiber” formation), but higher dosages (8%) lead to particle separation and strength reduction. In contrast, lime provides continuous and substantial strength enhancement with increasing dosage (up to 8%), primarily through chemical reactions producing cementitious compounds (e.g., C-S-H, C-A-H) that densify the structure. Consequently, lime-modified clay shows significantly higher cohesion and internal friction angle compared to lignin-modified clay at equivalent or higher dosages, with corresponding stress–strain curves shifting from enhanced (strain-hardening) to softening behavior. These findings provide practical insights into red clay improvement in geotechnical engineering applications.