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Understanding and predicting quay crane breakdowns using explainable AI
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DOI：10.1016/j.martra.2026.100152.png)
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Abstract
En 中文
• Applies explainable AI to predict quay crane breakdown occurrences. • Combines operations, monitoring, and weather data to model breakdown risks. • Uses SHAP-based explanations to reveal key drivers of crane breakdowns. • Employs nested cross-validation for reliable evaluation across classifiers. • Provides actionable insights to strengthen resilience and efficiency in port operations.
Keywords:
Quay cranes
Container terminal operations
Breakdown prediction
Predictive maintenance
Machine learning
Explainable artificial intelligence (XAI)
Port performance
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