The Applications of Data Science and Big Data Analytics in Underground Transportation Infrastructure

Details

• Creators:
  Pourhomayoun, Mohammad ; Nagrecha, Kabir ; Mazari, Mehran ; Rodriguez-Nikl, Tonatiuh
• Corporate Creators:
  Colorado School of Mines ; University Transportation Center for Underground Transportation Infrastructure ; California State University, Los Angeles
• Corporate Contributors:
  United States. Department of Transportation. Office of the Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Data analysis
  • Data mining
  • Geological surveying
  • Machine learning
  • Tunneling
  • Underground structures Deep Learning
  • Recurrent Neural Networks
  • Tunnel Boring Machine (TBM)
• Publication/ Report Number:
  Report No. UTC-UTI-008
• DOI:
  https://doi.org/10.5281/zenodo.4713308
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  University Transportation Center for Underground Transportation Infrastructure (UTC)
• Abstract:
  This research project focuses on the applications of data science, machine learning, and big data analytics in the construction, maintenance and performance of the underground transportation infrastructure. The first objective is to develop advanced data mining and novel machine learning based methods for predicting or detecting ground conditions using the data collected before and during the TBM operations. The second objective is to design and develop data-driven predictive models that can predict the TBM state and status in real-time as well as adverse events and anomalies. The project includes 3 main phases: (I) large-scale UTI data collection, exploration, and pre-processing; (II) feature and knowledge extraction, and dimensionality reduction, (III) data analytics, and predictive analytics model using machine learning/deep learning methods and data visualizations.
• Format:
  PDF
• Funding:
  69A355174711
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/59256/dot_59256_DS1.pdf
• File Type:
  [PDF - 2.73 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:d57ea8321ef77d5a6602a32759042a843467c0e9a9206ffba01a8356369b4261b71ed873e4a68aa575dc3064d96d9082beea62e7521200d305ce397f17cb5d59