• The International Concrete Abstracts Portal is an ACI led collaboration with leading technical organizations from within the international concrete industry and offers the most comprehensive collection of published concrete abstracts.

International Concrete Abstracts Portal


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

Author(s): Andrew Fahim

Publication: Web Session



Appears on pages(s):



Date: 4/1/2021

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 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 7000 projects representing geographical regions of over 50 countries and representing several thousand unique concrete mixtures. This, to the authors’ knowledge, is the largest dataset available for training such algorithms. Challenges in maintaining this ever-growing dataset as well as opportunities with the growing capabilities of these algorithms are presented.