Image-based Deep Learning for the time-dependent prediction of fresh concrete properties

TL;DR

This paper presents a deep learning method using CNNs and stereoscopic images to predict the time-dependent properties of fresh concrete during mixing, enabling real-time quality control and process optimization.

Contribution

It introduces a novel approach combining image sequences and mix design data for dynamic prediction of concrete properties during mixing.

Findings

Depth and optical flow images improve prediction accuracy.

The model predicts slump flow, yield stress, and viscosity effectively.

Temporal information enhances the learning of property evolution.

Abstract

Increasing the degree of digitisation and automation in the concrete production process can play a crucial role in reducing the CO$_2$ emissions that are associated with the production of concrete. In this paper, a method is presented that makes it possible to predict the properties of fresh concrete during the mixing process based on stereoscopic image sequences of the concretes flow behaviour. A Convolutional Neural Network (CNN) is used for the prediction, which receives the images supported by information on the mix design as input. In addition, the network receives temporal information in the form of the time difference between the time at which the images are taken and the time at which the reference values of the concretes are carried out. With this temporal information, the network implicitly learns the time-dependent behaviour of the concretes properties. The network predicts the slump flow diameter, the yield stress and the plastic viscosity. The time-dependent prediction potentially opens up the pathway to determine the temporal development of the fresh concrete properties already during mixing. This provides a huge advantage for the concrete industry. As a result, countermeasures can be taken in a timely manner. It is shown that an approach based on depth and optical flow images, supported by information of the mix design, achieves the best results.