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Title: The Use of Machine Learning Algorithms and IoT Sensor Data for Concrete Performance Testing and Analysis

Author(s): Andrew Fahim, Tahmid Mehdi, Ali Taheri, Pouria Ghods, Aali Alizadeh, and Sarah De Carufel

Publication: Symposium Paper

Volume: 350


Appears on pages(s): 142-152

Keywords: artificial intelligence, concrete performance, IoT, machine learning, mixture optimization

DOI: 10.14359/51734320

Date: 11/1/2021

With IoT sensors gaining widespread adoption in recent years for monitoring in-situ concrete properties, the volume of data generated using these sensors is growing at a significant rate. These sensors are typically used for several purposes among which temperature, humidity and strength monitoring (using the maturity method) are currently the most common. This data is typically collected at centralized cloud-based databases where they can be accessed by sensor end-users as well as algorithm developers. This work presents on how data from these IoT sensors has been used by the authors to train machine learning algorithms to perform several tasks including but not limited to: detecting anomalies, detecting events in the service life of the sensor (e.g. concrete pouring,) suggesting mixture alterations to optimize performance and predicting future performance. These capabilities are currently being used by concrete practitioners on daily basis. This is done using data collected from tens of thousands of sensors, used in over 7500 projects representing geographical regions of over 45 countries and representing several thousand unique concrete mixtures. This, to the authors’ knowledge, is the largest dataset available for training such algorithms. Two use cases are presented for how this data is utilized to train machine learning algorithms to assist practitioners in day-to-day activities such as mixture optimization.