Many congratulations to the six category winners and Thermoelectric Conversion Systems, the overall winner of the 2019 Intelligent Infrastructure Challenge
Overall winner: Thermoelectric Conversion Systems
Category D) Energy and the environment
New approach to temperature regulation of roadside cabinets
Thermoelectric Conversion Systems (TCS) has developed a configurable heat exchanger and control electronics package suitable for regulating the internal temperature of trackside and roadside stand-alone cabinets. Novel electronics and advanced control algorithms maintain the enclosure environment and offer a much higher efficiency that can yield up to 50% energy and 25% cost savings. Cloud-based remote monitoring and telematics ensure a low cost of ownership.
With an expected service life of 20+ years and almost no maintenance requirements our product is the lowest cost option for cabinet cooling. It is scalable and modular meaning increased cooling demand in the future can be easily retro-fitted.
Environmental benefits include increased up time of the equipment being cooled; increased energy efficiency with reduced CO2 and pollutant emissions. The heat pump technology is solid-state so there are no noxious or environmentally hazardous gases involved in either the product or the manufacture of the product. It is also recyclable at end of life.
Category A) Design, construction and ssset management
Winner: map16 Asset Management
AI gully monitoring system that pre-empts highway network flooding events
map16 is a complete end to end IoT gully sensor solution, including hardware and web-based software. Its developers set out to create the first gully sensor solution that is at a price point that makes it scalable for local authorities and other highways bodies.
The resulting gully sensor allows local authorities to monitor live feeds of water and silt levels, combined with weather forecasting data. Not only does it give an overview of the highway drainage network, it also provides pre-warning of possible flooding events driven by met office weather forecasting. Local authorities will have the power to pre-empt floods rather than react to them. Machine learning uses all sensor data and forecasts to understand patterns that before may have been missed in understanding highway network flooding events.
Category B) Operations
Winner: Northern Gas Networks
Automating production of traffic management plans for signing, lighting and guarding
Smart SLG was developed by Northern Gas Networks in partnership with 1Spatial. This desktop prototype allows us to create automated bespoke traffic management plans for signing, lighting and guarding instead of relying on a resource intensive bought-in service. It's a game-changer for the utility sector.
After safety, keeping traffic moving to minimise customer disruption is a top priority during streetworks. Smart SLG creates instant signing requirements, identifies potential customer impacts and can be viewed by local authorities and key stakeholders at the touch of a button. We've proved the concept, and live trials are now under way in Leeds.
Category C) Road user experience
Analysis tool that provides travel behaviour data across different networks and transport modes
TravelAi's SRN IMPACT is an automatic, digital, travel-demand-survey tool that would allow road owners to see the movement of users between the SRN, minor roads, planes, rail, bus and metro networks. Potentially it is capturing the data needed to inform MaaS implementation and service bundling.
Standing for InterModal Performance Analysis of Citizen Travels, SRN IMPACT uses smartphone sensors and TravelAi's deep learning informed mode detection IP to automatically crowdsource rich and accurate quantitative travel-behaviour data. It enables large-scale travel behaviour data acquisition at very low costs.
Anticipated benefits include improved connectivity within the SRN and rest of the road network, improved network capacity utilisation improved and more personalised engagement with customers and greater understanding of the interoperability of different transport modes.
Category E) Safety
Winner and overall runner up: EAVE
Technology that protects the user's hearing while allowing them to remain fully aware of their sound environment
Eave was originally established in 2015 to eliminate the isolation and loneliness caused by noise induced hearing loss. Its technology protects the user's hearing while allowing them to remain fully aware of their sound environment. Eave has subsequently developed a system to help employers prevent noise induced injuries at work. Eave's solution consists of two components; a peak noise monitoring and mapping platform and the Eave intelligent ear defenders.
Highways are one of the most dangerous occupational environments for an operative. Accidents occur when operatives are unable to hear hazards and if they remove their hearing protection they expose themselves to hearing damage.
The Eave Hearing Conservation System headsets allow operatives to hear their environment while controlling noise levels so that workers are never over or under-protected. The system also indicates whether or not hearing protection is being worn correctly.
Category F) Asset Condition Monitoring
Winner: British Geological Survey (in partnership with Socotec)
Remote-condition-monitoring tool for deployment on vulnerable or limited-access earthworks
PRIME (Proactive Infrastructure Monitoring and Evaluation) uses emerging ground imaging technology to allow asset managers to non-invasively "see" inside earthworks. We are seeking to demonstrate and validate PRIME for monitoring geotechnical assets within the road network.
PRIME combines non-invasive ground imaging technology with embedded sensors, data telemetry and web portal access into a highly innovative remote-condition-monitoring tool. It uses geophysical imaging (electrical resistivity tomography - which is sensitive to compositional variations, moisture content and ground movement) to provide near-real-time information on the internal condition of geotechnical assets.
PRIME represents a step-change compared to the current reliance on visual inspections. It provides web-based monitoring of subsurface spatial information, which extends and complements conventional geotechnical point-sensing and sampling. It enables a major shift towards "intelligent assets" where the health and internal condition of safety critical earthwork and drainage structures can be imaged and monitored remotely and in near-real-time.