Assessment of Ecological Suitability for Highway Under-Bridge Areas: A Methodological Integration of Multi-Criteria Decision-Making and Optimized Backpropagation Neural Networks

Highway under-bridge areas represent a valuable land resource while simultaneously constituting a sensitive ecological zone. Achieving a balance between its redevelopment and ecological preservation constitutes a critical challenge within the field of ecological engineering. Although prior research has addressed urban elevated underbridge space, investigations specifically focusing on highway underpasses remain limited. The absence of standardized criteria for assessing the suitability of these spaces has resulted in uncoordinated and fragmented utilization. In response, this study proposes a comprehensive evaluation framework that integrates multi-criteria decision-making (MCDM) methodologies with optimized backpropagation neural networks, specifically genetic-algorithm-optimized BP (GA-BP) and particle-swarm-optimization-optimized BP (PSO-BP). The model incorporates indicators spanning physical characteristics, environmental factors, safety considerations, and accessibility metrics, and is applied to an empirical dataset comprising 134 highway bridge underpasses in Fuzhou City. The results indicate that (1) both the GA-BP and PSO-BP models enhance convergence speed and classification accuracy, with the GA-BP model demonstrating superior stability and suitability for classifying underpass suitability; (2) the principal determinants of suitability include traffic accessibility, safety parameters, and spatial relationships with adjacent water bodies and agricultural lands; and (3) underpasses characterized as hub-type, single-sided road-adjacent, and cross-connection configurations exhibit greater potential for redevelopment. This investigation represents the first integration of MCDM and optimized neural network techniques in this context, offering a robust tool to support the scientific planning and ecological conservation of underbridge space environments.