Researcher(s)
- Sonia Wabukoya, Electrical Engineering, University of Delaware
Faculty Mentor(s)
- Monique Head, Civil, Construction, and Environmental Engineering, University of Delaware
- Alemu Mosisa Legese, Civil, Construction, and Environmental Engineering, University of Delaware
Abstract
The thermal behavior of concrete significantly influences its structural integrity, durability, and performance under varying environmental conditions. In recent years, the incorporation of microplastics into concrete has prompted investigation into how these additives affect internal temperature regulation and heat retention. This research presents the design and deployment of a low-cost, Raspberry Pi–based data acquisition system intended to monitor the internal temperature of concrete pavers embedded with microplastics. The system is installed outside DuPont Hall at the University of Delaware, where digital DS18B20 temperature sensors are embedded in the pavers at mid-depth and interfaced with a Raspberry Pi microcomputer.
A custom Python script was developed to log time-stamped temperature readings from the sensors at 10-minute intervals. This sampling rate balances data resolution and energy efficiency. Data are saved for later processing and visualization. The Raspberry Pi is powered using a UPS HAT connected to an 8Ah LiFePO₄ battery, enabling uninterrupted field operation. There may be future iterations to support long-term, off-grid deployment.
The project builds upon existing literature on low-cost infrastructure monitoring using microcontroller platforms such as Raspberry Pi, which have been successfully implemented in building health and environmental sensing applications. Comparative analysis will be conducted between internal sensor data and meteorological data from a local weather station, focusing on air temperature and solar radiation. Key performance indicators such as daily average temperature, thermal gradients, and thermal lag will be extracted and analyzed to evaluate the effect of microplastics on heat transfer in concrete.
This study not only demonstrates the viability of Raspberry Pi for scalable, battery-powered monitoring but also contributes to the understanding of smart materials and their behavior under real-world environmental conditions. The approach offers a replicable framework for infrastructure monitoring in civil and materials engineering contexts.