Learning to Sieve: Prediction of Grading Curves from Images of Concrete Aggregate
This addresses a domain-specific problem in concrete production by enabling real-time adaptation of mixture designs, though it is incremental as it applies existing deep learning techniques to a new application.
The paper tackles the problem of unknown variations in concrete aggregate size distribution, which affects concrete quality and leads to increased cement usage, by proposing a deep learning method to predict grading curves from images, achieving quantitative evaluation on a novel dataset.
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.