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# ML-Class-Project <br>





  
  

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### Project Title:<br>





  
  

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Probabilistic structural health monitoring of composite structures under impact testing <br>





  
  

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### Project Description:<br> 





  
  

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The aerospace industry's safety and cost concerns drive the investigation of damage detection and progression in carbon-fiber-reinforced plastics (CFRP) from low-energy impacts. Leveraging machine learning, specifically Auto-Regressive models, offers a promising avenue for Structural Health Monitoring (SHM). By tailoring these models to the unique characteristics of composite materials and impact-induced damage, the aim is to enhance safety and reduce maintenance costs. This project aims to develop efficient detection techniques. <br>





  
  

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### Folder Structure <br>





  
  

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- data/





  
  

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- code/





  
  

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- notebooks/





  
  

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- literature/





  
  

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  - references/





  
  

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    - Shabbir Ahmed and Fotis Kopsaftopoulos (2022): “Statistical Active-Sensing Structural Health Monitoring via Stochastic Time-Varying Time Series Models.”





  
  

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    - Nardi et al.: "Detection of low-velocity impact-induced delaminations in composite laminates using Auto-Regressive models." [Link](https://linkinghub.elsevier.com/retrieve/pii/S0263822316300253)





  
  

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    - Ahmed and Kopsaftopoulos (2023): "Active Sensing Acousto-Ultrasound SHM via Stochastic Non-stationary Time Series Models."





  
  

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To access the repository, click [here](https://github.com/E-Ameke/ML-Class-Project)