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

Muhammad Zeshan Akber (), Wai-Kit Chan, Hiu-Hung Lee and Ghazanfar Ali Anwar ()
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Muhammad Zeshan Akber: Centre for Advances in Reliability and Safety, Hong Kong Science and Technology Parks, Pak Shek Kok, New Territories, Hong Kong, China
Wai-Kit Chan: Centre for Advances in Reliability and Safety, Hong Kong Science and Technology Parks, Pak Shek Kok, New Territories, Hong Kong, China
Hiu-Hung Lee: Centre for Advances in Reliability and Safety, Hong Kong Science and Technology Parks, Pak Shek Kok, New Territories, Hong Kong, China
Ghazanfar Ali Anwar: Centre for Advances in Reliability and Safety, Hong Kong Science and Technology Parks, Pak Shek Kok, New Territories, Hong Kong, China

Mathematics, 2024, vol. 12, issue 19, 1-19

Abstract: 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.

Keywords: tower crane; payload moving; deep neural network; Tree-Structured Parzen Estimator (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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