Human error risk prioritization in crane operations based on CPT and ICWGT

Li, Aihua

doi:10.1371/journal.pone.0297120

Cited by 3 publications

(4 citation statements)

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“…To address this issue, Li and Zhao [12] proposed a model for crane safety assessment that integrates CPT and entropy. Li [4] proposed a risk prioritization model for human errors in crane operations based on CPT and an improved combination weighting model based on game theory. Thus, in the present study, CPT was utilized to consider the psychological behavior of decision makers in crane hazard assessment.…”

Section: Literature Review On Crane Safety Evaluationmentioning

confidence: 99%

“…Research has highlighted the role of linguistic information in the assessment of crane operational risks [4]. Various techniques for uncertain linguistic representations have been applied to safety assessments of cranes.…”

Section: Introductionmentioning

confidence: 99%

“…Fortunately, cumulative prospect theory (CPT) provides a better understanding of the psychological behaviors involved in decision making when dealing with gains and losses. It has been applied to crane risk assessments [4] [12]. However, the integration of prospect theory and Z-numbers into the study of crane operation hazard evaluation is limited.…”

Section: Introductionmentioning

confidence: 99%

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Crane Operation Hazard Evaluation Based on Z-Number and CPT

2024

IEEE Access

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Crane operation hazard evaluation plays an important role in theo prevention of accidents and casualties. Therefore, formulating effective measures to ensure the safety of cranes is vital. However, most studies on crane safety evaluation overlook the risk attitudes of decision makers. To overcome the limitations of the current research, this study proposes a novel risk prioritization method based on the Znumber and cumulative prospect theory (CPT) for crane operation hazard evaluation. First, considering the uncertainty and unreliability of decision makers' assessments, the Z-number is utilized to capture the decision information with regard to crane operation hazards. Second, to consider decision makers' psychological behavior in crane operation hazard evaluation, CPT is used in the Z-environment to rank the identified hazards. Third, grade discrimination rules and cumulative prospect values are integrated to classify the danger level of a hazard. Finally, a case study involving a crane operation hazard evaluation based on the Z-number and CPT is employed to verify the practicability of the proposed model. Sensitivity analyses and comparative studies are conducted to validate the proposed method.

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Section: Literature Review On Crane Safety Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Crane Operation Hazard Evaluation Based on Z-Number and CPT

2024

IEEE Access

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“…Das et al [ 40 ] identified and ranked the potential failures of crane operations using the Z number in conjunction with the fuzzy VIKOR. To reflect the risk attitudes of experts and integrate subjective and objective weights, Li [ 41 ] proposed an improved FMEA method for human errors in crane operations by using cumulative prospect theory and improved combination weighting model of game theory. Although numerous studies have been conducted on FMEA using various approaches that integrate decision support techniques, there is a scarcity of research on the assessment of crane risks through the integration of MCDM with FMEA.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment of crane operation hazards using modified FMEA approach with Z-number and set pair analysis

2024

Heliyon

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Deep Learning-Based Boolean, Time Series, Error Detection, and Predictive Analysis in Container Crane Operations

Awasthi,

Krpalkova,

Walsh

2024

Algorithms

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Deep learning is crucial in marine logistics and container crane error detection, diagnosis, and prediction. A novel deep learning technique using Long Short-Term Memory (LSTM) detected and anticipated errors in a system with imbalanced data. The LSTM model was trained on real operational error data from container cranes. The custom algorithm employs the Synthetic Minority Oversampling TEchnique (SMOTE) to balance the imbalanced data for operational data errors (i.e., too few minority class samples). Python was used to program. Pearson, Spearman, and Kendall correlation matrices and covariance matrices are presented. The model’s training and validation loss is shown, and the remaining data are predicted. The test set (30% of actual data) and forecasted data had RMSEs of 0.065. A heatmap of a confusion matrix was created using Matplotlib and Seaborn. Additionally, the error outputs for the time series for the next n seconds were projected, with the n seconds input by the user. Accuracy was 0.996, precision was 1.00, recall was 0.500, and f1 score was 0.667, according to the evaluation criteria that were produced. Experiments demonstrated that the technique is capable of identifying critical elements. Thus, future attempts will improve the model’s structure to forecast industrial big data errors. However, the advantage is that it can handle imbalanced data, which is usually what most industries have. With additional data, the model can be further improved.

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Human error risk prioritization in crane operations based on CPT and ICWGT

Cited by 3 publications

References 40 publications

Crane Operation Hazard Evaluation Based on Z-Number and CPT

Crane Operation Hazard Evaluation Based on Z-Number and CPT

Risk assessment of crane operation hazards using modified FMEA approach with Z-number and set pair analysis

Deep Learning-Based Boolean, Time Series, Error Detection, and Predictive Analysis in Container Crane Operations

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