Automatic concrete slump prediction of concrete batching plant by deep learning

Abstract

The workability of fresh concrete is highly important in terms of construction quality and safety. Slump tests are required every 120 m³, yet automated monitoring for each concrete batch remains unavailable in the actual concrete batching plant. To mitigate this issue, we propose an automatic slump prediction method based on the VGG16 neural network by analyzing the video from the final discharge hopper of the batching plant. Additionally, Explainable AI (XAI) is adopted to evaluate and validate our automatic concrete quality inspection approach. Iteratively examining XAI outputs and applying necessary adjustments in data preprocessing helps to achieve better overall performance. The proposed video classification method performed by averaging over the image-level predictions can classify the concrete into four slump classes with an average precision of 85% and an average F1 score of 87%. This demonstrates the possibility of continuous quality evaluation for all concrete produced in the concrete batching plant.

Keywords

• Concrete batching plant
• Deep learning
• Concrete slump
• Quality inspection
• Construction safety