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Machine-learning-based ground sink susceptibility evaluation using underground pipeline data in Korean urban area.

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Summary

This study introduces a method to predict ground sink susceptibility from underground pipelines in urban areas. The random forest classifier demonstrated the best overall performance for creating ground sink susceptibility maps.

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Area of Science:

  • Geosciences
  • Urban Planning
  • Civil Engineering

Background:

  • Extensive research exists on natural ground subsidence, but less on subsidence caused by artificial structures like underground pipelines in urban environments.
  • Understanding and mitigating ground sink susceptibility due to aging urban infrastructure is crucial for public safety and infrastructure management.

Purpose of the Study:

  • To propose and evaluate a method for predicting ground sink susceptibility specifically caused by underground pipelines in urban areas.
  • To identify key factors influencing ground sink occurrence related to underground pipelines and compare the effectiveness of various machine learning models for prediction.

Main Methods:

  • Collected underground pipeline data, drilling data, and 77 ground sink occurrence points across five 1x1 km urban areas.
  • Identified three ground sink conditioning factors (GSCFs): pipe deterioration, diameter, and length, through correlation analysis.
  • Applied four machine learning classifiers: multinomial logistic regression (MLR), decision tree (DT), random forest (RF), and gradient boosting (GB).

Main Results:

  • Pipe deterioration exhibited the highest correlation with ground sink occurrence, followed by pipe length and diameter.
  • The Gradient Boosting (GB) classifier achieved the highest accuracy (0.7432), closely followed by the Random Forest (RF) classifier (0.7407).
  • The RF classifier demonstrated superior reliability, indicated by high Area Under the Receiver Operating Characteristic (AUC-ROC) curve values (0.84, 0.70, 0.87).

Conclusions:

  • The Random Forest (RF) classifier is recommended for creating Ground Sink Susceptibility Maps (GSSMs) due to its overall best performance and reliability.
  • The study highlights the importance of pipe deterioration as a critical factor in predicting urban ground sink events.
  • The developed methodology provides a valuable tool for urban planners and engineers to assess and manage risks associated with underground pipeline infrastructure.