Learning to Sieve: Prediction of Grading Curves from Images of Concrete Aggregate

TL;DR

This paper introduces a deep learning approach to predict concrete aggregate grading curves from images, enabling better control of concrete quality and reducing ecological impact by accurately assessing particle size distribution.

Contribution

It presents a novel neural network architecture with multi-scale features and provides a new dataset for aggregate grading curve prediction from images.

Findings

Effective prediction of grading curves from images.

Improved control over concrete mixture design.

Published dataset for future research.

Abstract

A large component of the building material concrete consists of aggregate with varying particle sizes between 0.125 and 32 mm. Its actual size distribution significantly affects the quality characteristics of the final concrete in both, the fresh and hardened states. The usually unknown variations in the size distribution of the aggregate particles, which can be large especially when using recycled aggregate materials, are typically compensated by an increased usage of cement which, however, has severe negative impacts on economical and ecological aspects of the concrete production. In order to allow a precise control of the target properties of the concrete, unknown variations in the size distribution have to be quantified to enable a proper adaptation of the concrete's mixture design in real time. To this end, this paper proposes a deep learning based method for the determination of concrete aggregate grading curves. In this context, we propose a network architecture applying multi-scale feature extraction modules in order to handle the strongly diverse object sizes of the particles. Furthermore, we propose and publish a novel dataset of concrete aggregate used for the quantitative evaluation of our method.