Idea Evaluation & Selection of Problem Space

 

Summary of Activities

The team has focused on addressing the issue of urban flooding, particularly the challenges posed by impermeable surfaces in cities due to urbanization and climate change. The approach integrates permeable road surfaces, 4D printing, digital twins, and AI-driven systems to manage stormwater effectively. Key learnings include the interconnected nature of runoff, infiltration, and urban design, as well as the critical need for adaptive solutions that incorporate green and gray infrastructure.

Learnings and Feedback

  • Urban streets account for 80% of unbuilt areas in cities and are a significant contributor to runoff issues.
  • Solutions must balance reducing runoff, improving water quality, and enhancing urban livability.
  • Feedback has emphasized the need for cost-effective, scalable solutions that engage diverse stakeholders such as city planners, environmental authorities, and residents.

Decisions Made

The team decided to focus on transforming roads into multifunctional infrastructures capable of managing stormwater through permeable surfaces and integrated technologies.

Future Trends Impacting Problem Space

The problem space will evolve with trends like climate change-induced extreme weather events, increasing urbanization, and the adoption of smart city technologies. Urban areas are expected to prioritize sustainable and adaptive infrastructure, as reflected in the conceptual design for 2050 roads.

Project Direction

 

Problem Space

Urban flooding due to impermeable surfaces and climate change. Research supports that runoff contributes to pollution, damages infrastructure, and exacerbates the urban heat island effect.

Preliminary Chosen Direction

The project focuses on permeable road designs enhanced by deep technologies, including:

4D Printing: Adaptive materials that respond to environmental stimuli.

While 3D printing creates static, three-dimensional objects layer by layer, 4D printing produces dynamic structures that can change their shape, properties, or functionality over time in response to environmental stimuli such as:

  • Temperature
  • Humidity
  • Light
  • Pressure
  • Magnetic or Electric Fields

This adaptability is made possible by using smart materials, such as shape-memory polymers (SMPs), hydrogels, or responsive composites, which have the ability to transform or self-assemble when exposed to specific conditions.

Example Use Case A 4D-printed pavement surface embedded with moisture- responsive polymers could detect excess rainwater, temporarily swell to create drainage pathways, and shrink back once the surface dries out.

Digital Twins: Real-time monitoring and predictive modeling.

True-to-reality digital twins are revolutionizing industries from autonomous vehicles to smart cities by enabling virtual testing and optimization of complex real-world systems. This technology enables better planning, monitoring and optimization of stormwater management systems.

Example Use Cases

  • Real-Time Monitoring and Data Integration: Sensors placed in drains, pipelines, and surface areas monitor water levels, flow rates, and pollutants. Data feeds into the digital twin, providing real-time visibility of the stormwater system.
  • Scenario Planning, Predictive Modeling for Flood Prevention: A Digital Twins helps to simulate different designs of stormwater systems before physical implementation. In addition, scenarios can test the effectiveness of green infrastructure (e.g., permeable pavements, bioswales) and traditional drainage systems. Digital twins use AI and machine learning algorithms to simulate weather scenarios, storm events, and urban growth impacts. They predict how water will flow, where flooding might occur, and how infrastructure will respond. Proactive measures can be implemented, such as adjusting drainage gates or deploying temporary water storage systems.

AI and IoT: Smart traffic and parking management to optimize urban space usage.

Smart Parking refers to the use of technology and data-driven systems to optimize the management, availability, and utilization of parking spaces in urban areas. It reduces congestion, minimizes search time for parking, and improves overall traffic flow. A city using AI-powered parking systems reduces the need for surface parking, allowing previously sealed areas to be converted into green spaces or permeable zones. Roadway Predictability involves using advanced data analytics, AI, and IoT technologies to forecast and optimize traffic flow, reduce congestion, and ensure efficient use of road networks. Adaptive traffic signals reduce congestion in a busy district, preventing the need for building new roads. Green infrastructure can replace planned expansions.

This prototype is a simplified representation of how we envision permeable road surfaces serving as effective systems for water infiltration.

 

Next Steps

  • Develop prototypes of permeable road surfaces.
  • Validate the effectiveness of digital twins and predictive models.
  • Address gaps in pollutant filtration, particularly microplastics and heavy metals.