Real-Time Prediction of Mixing Torque Using Deep Learning

Abstract

Mixing torque reflects the interaction between the mixer and fresh mortar, providing insights into material consistency. Traditionally, obtaining torque measurements requires specialised sensors integrated into mixers, which adds cost and limits their practicality for large-scale or on-site use. To address this, this study proposes a deep learning framework that predicts real-time torque values directly from mixing videos. Instead of relying on specialised sensors or equipment, the model extracts spatial and temporal features from consecutive video frames using a time-series architecture. Specifically, a hybrid ResNet–LSTM model is employed: ResNet encodes spatial features from each individual frame, while the LSTM captures temporal dependencies across sequences of frames. This allows the model to learn how visual changes in the mixing process correlate with the evolving torque. A dataset comprising 21 mortar mixtures with varying compositions was collected, including synchronised video footage and torque measurements recorded throughout the mixing period. Workability, flexural and compressive strength tests were performed after mixing. The model achieved R2 scores of 0.992 (training), 0.989 (validation), and 0.936 (testing), indicating that the model achieved high accuracy with strong generalisation ability across unseen data. The inference time is under 60 ms per 5-frame sequence. The proposed method enables fast, non-contact, and reliable torque estimation, offering a practical solution for intelligent monitoring of mixing processes in real-world settings.