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Maintenance Prediction of Bridges using Entity Embedding Neural Networks
Business process
Subjective assessment by trained inspectors (can vary)Difficult to follow, justify and replicate past decisionsLimited to no access to similar cases of the past
Inspection to Maintenance Advice process of bridge management system
Motivation Architecture
Interpretability
Results
Conclusions
References
A standard three-layers feed-forwardneural network was used for all the classification tasks. We utilise entityembedding layer to learn representationsfrom categorical features. Weighted categorical cross-entropy was also applied tohandle class imbalance problem.
Typical inspection process of civil structures (bridges)
To provide support in subjective assessmentof bridges maintenance planning by developing
predictive models using historical data
Little attension towards improving subjective assessment process of inspection
Predictive tasks and data representation
Action Basedon Advice
Details of Inspected Components
Inferring Damage Details
Assessment of Damage Level
Desk Study forRisk Assessment
Condition State?
Level of Risk?
Analysis of Risk
After Principal Inspection
SubjectiveJudgement
Service LevelAgreements
Qualitative Standards
Quantitaive Standards
Legend
MaintenanceAdvice?
Input Processing Decision
Visual insights
Principal inspection data collected from 2007 to 2017
Bridges asset register Inspection dataDamages detais Risk details
Feature engineering process was guided by expertsNumber of features were reduced from 69 to 23 only!
Input
Embedding
Input
Embedding
Input
Dense
Input
Dense
Dense
Dropout
Dense
Dropout
Output
Concatenate
Categorical FeaturesNumeric Features
Input
Embedding
Input
Embedding
Input
Dense
Input
Dense
Output
Concatenate
Categorical FeaturesNumeric Features
Dense
Dropout
Dense
Dropout
OutputOutput
Condition StateRisk LevelMaintenance Advice
Shared Layers
Dense
Dropout
Dense
Dropout
Dense
Dropout
Dense
Dropout
Condition state classification with SRS
Risk level classification with SRS
Maintenance advice classification with SRS
Explanation of NN-EE (cw) model for all three predictive tasks
Utilise data from in-use business process
Aid decision-makers in maintenance related tasks classiifcation with 80% accuracy
Provide interpretability of the resultsMulti-task learning for future similar tasksGeneric modeling apporachEvolving dataData quality Black-box models
Allah Bukhsh, Z., Stipanovic, I., Saeed, A., & Doree, A. G. Maintenance prediction of bridges using entity embedding neural networks.Under review in Automation in Construction
Caruana, R. Multitask learning. Machine learning 1997, 28, 41–75.Guo, C.; Berkhahn, F. Entity embeddings of categorical variables. arXiv preprint arXiv:1604.06737 2016
Ribeiro, M.T.; Singh, S.; Guestrin, C. Why should i trust you?: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD 2016
Dataset
Zaharah Allah Bukhsh
University of TwenteEnschede, The Netherlands
Irina Stipanovic
University of TwenteEnschede, The Netherlands
Aaqib Saaed
Eindhoven University of Techology, Eindhoven
Andre G. Doree
University of TwenteEnschede, Netherlands
To leverage task-relatedness, we use multi-task learning to learn unified models for solving condition state, risk level, and maintenance advice prediction tasks. The hard-parameter sharing is used in the initial layers of the network, which are shared across all the tasks, whereas the final layers are problem specific.
Architecture of Neural Network with Entity Embeddings
Multi-task neural networks with entity embedding and two shared layers
