Human reliability of the intelligent construction site tower crane driver interface based on DEMATEL-ISM-BN
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Summary
This summary is machine-generated.Accidents on intelligent construction sites are often caused by human error. This study introduces a new method to evaluate human-machine interface reliability and control unsafe behaviors in tower crane drivers.
Area Of Science
- Construction Safety Engineering
- Human Factors Engineering
- Artificial Intelligence in Construction
Background
- Industry 4.0 has advanced intelligent construction sites in China, yet accidents involving digitized tower cranes persist.
- Human unsafe behavior is a primary cause of these tower crane accidents, highlighting the need to assess human factors reliability.
- Existing safety focuses need to shift towards the human-machine interface to understand and mitigate human error patterns.
Purpose Of The Study
- To develop a novel evaluation method for the human-machine interface reliability of intelligent construction site tower cranes.
- To identify and analyze human error behavior patterns among tower crane drivers.
- To establish targeted measures for controlling unsafe behaviors and reducing accidents.
Main Methods
- Text mining techniques (TF-IDF-TruncatedSVD-ComplementNB) were used to identify human error behaviors.
- The SHEL model and Performance Shaping Factors (PSF) system were employed to categorize influencing factors.
- Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) analyzed factor importance and structure.
- A Bayesian network was constructed for a multi-level hierarchical evaluation of the human-machine interface.
Main Results
- A new evaluation method for the human-machine interface was established, integrating DEMATEL, ISM, and Bayesian networks.
- The method effectively quantifies the likelihood of human errors in tower crane operations.
- The evaluation process aligns with real-world operational scenarios of tower crane drivers.
Conclusions
- The developed method provides a robust framework for assessing human factors reliability in intelligent construction.
- Targeted measures can be developed to control unsafe behaviors, thereby enhancing safety for tower crane drivers.
- This research contributes to reducing accidents on intelligent construction sites by addressing human error at the human-machine interface.
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