TPE-Optimized DNN with Attention Mechanism for Prediction of Tower Crane Payload Moving Conditions

Accurately predicting the payload movement and ensuring efficient control during dynamic tower crane operations are crucial for crane safety, including the ability to predict payload mass within a safe or normal range. This research utilizes deep learning to accurately predict the normal and abnormal payload movement of tower cranes. A scaled-down tower crane prototype with a systematic data acquisition system is built to perform experiments and data collection. The data related to 12 test case scenarios are gathered, and each test case represents a specific combination of hoisting and slewing motion and payload mass to counterweight ratio, defining tower crane operational variations. This comprehensive data is investigated using a novel attention-based deep neural network with Tree-Structured Parzen Estimator optimization (TPE-AttDNN). The proposed TPE-AttDNN achieved a prediction accuracy of 0.95 with a false positive rate of 0.08. These results clearly demonstrate the effectiveness of the proposed model in accurately predicting the tower crane payload moving condition. To ensure a more reliable performance assessment of the proposed AttDNN, we carried out ablation experiments that highlighted the significance of the model’s individual components.