Title:
How to Optimize Concrete Deliveries Using Machine Learning and Concrete Truck On-Board Sensors
Author(s):
Siccardi
Publication:
Web Session
Volume:
ws_F23_Siccardi.pdf
Issue:
Appears on pages(s):
Keywords:
DOI:
Date:
10/29/2023
Abstract:
To ensure proper concrete placement on site and targeted properties in the hardened state, ready-mixed concrete producers must be able to control the entire production cycle, from material batching to delivery. Weather conditions, intrinsic variability of raw materials, delivery conditions, or human factor are all parameters having a direct impact on the fluctuation of both fresh and hardened concrete properties, resulting in additional difficulties for producers. In order to further assist concrete producers, on-board sensor systems for concrete truck mixers have been developed over the last decade. By measuring the slump, the air content, the volume or the temperature of the fresh concrete inside the drum, those systems help ensure better concrete quality. It however comes at the cost of generating an ever-increasing amount of diverse data. This presentation will then expose how machine learning methods permitted to predict the concrete slump evolution during transportation from the plant to the construction site for a large number of delivery conditions.