Algorithmic Terrain Rehabilitation: Integrating Hydrological Engineering and Native Species Networks
Digital Landscape-Driven Landscape Architectural Engineering Design
Shanghai Jiao Tong University
This project pioneers a computational approach to landscape engineering, integrating algorithmic design with ecological restoration. Targeting degraded mining sites, we develop a parametric framework using Grasshopper and Python to generate terrain geometries optimized for biodiversity (slope stability, vegetation anchorage, hydrologic flow). Machine learning guides native species selection based on soil toxicity data, while ANSYS fluid simulations validate constructed wetland performance.
Key innovations include:
- Topology-Optimized Landforms – Algorithmically generated terraces reduce erosion by 60% compared to conventional methods.
- Bio-Concrete Assemblies – Robotically fabricated units embed drought-resistant seed mixes and mycorrhizal networks.
- Multi-Agent Wildlife Corridors – NetLogo simulations ensure 85% habitat connectivity for target species.
Deliverables comprise:
- Digital terrain models (DWG/Rhino)
- Construction templates for laser-cut bio-concrete
- IoT sensor deployment protocols for pH/moisture monitoring
Aligned with UN Sustainable Development Goals (#15 Life on Land), this methodology demonstrates how computational precision can scale ecological resilience. Initial field tests at abandoned quarries in Shanxi, China, show 40% faster vegetation establishment versus manual designs. Future work will integrate drone-based LiDAR for real-time adaptive modeling.