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Active COOPERATIVE AGREEMENT National Science Foundation (US)

SBIR Phase II: Prototyping Internet of Things (IoT) Sensing Platform for Infrastructure Monitoring

$10M USD

Funder National Science Foundation (US)
Recipient Organization Wave Logix, Inc.
Country United States
Start Date Sep 01, 2024
End Date Aug 31, 2026
Duration 729 days
Number of Grantees 1
Roles Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2404015
Grant Description

The broader impact/commercial potential of this SBIR Phase II project is to deploy a novel concrete strength sensing technology to provide smart solutions to construction industry. This groundbreaking technology will transform construction industry by enabling faster, data-driven decisions through real-time data of concrete strength monitoring. Short-term, this technology will allow accelerated project timelines and eliminate costly quality control errors.

Long term, this technology will leverage the power of big data to enable data-driven decision making and optimization of concrete mix design which will drastically reduce carbon footprint, eliminate wastes, and lead to more durable concrete infrastructures. By leveraging AI and big data analysis of the vast amount of structural health data collected, the project paves the way for the development of AI-powered solutions for predictive maintenance and improved construction practices.

The proposed project will focus on developing the market ready Internet-of-Things (IoT) concrete sensing system that addresses the challenge of using antiquated testing methods in construction industry, often leading to schedule delays and costs overrun. Built on the success of Phase I project, this program will develop a complete solution for scaling up productions.

A systematic hardware production and quality control procedure will be established, key parameters for a reproducible production line will be determined, and instrumentation errors will be minimized. Concurrently, a scalable cloud backend will be developed, capable of serving tens of thousands of dataloggers while ensuring data security and low latency.

The machine learning algorithm will be further refined to provide fast and accurate strength inferences. A full-scale production and stress testing of the sensor system in real-life conditions will also be conducted to evaluate the robustness and user experience.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

All Grantees

Wave Logix, Inc.

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