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Updated: Jul 10, 2025

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole
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Physics-based and machine-learning models for accurate scour depth prediction.

Ajay Jatoliya1, Debayan Bhattacharya1, Bappaditya Manna1

  • 1Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|November 19, 2023
PubMed
Summary
This summary is machine-generated.

This study compares physics-based numerical modeling and machine learning (ML) for estimating offshore structure scour depth. ML models, particularly artificial neural networks, showed high effectiveness, complementing numerical analysis for accurate and timely scour assessment.

Keywords:
machine learningnumerical analysisoffshore foundationsscour depthwind energy

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Area of Science:

  • Geotechnical Engineering
  • Ocean Engineering
  • Computational Fluid Dynamics

Background:

  • Scour phenomena pose significant risks to the stability of offshore structures.
  • Accurate scour depth estimation is crucial for structural integrity and safety.
  • Existing methods may lack efficiency or accuracy in complex marine environments.

Purpose of the Study:

  • To estimate scour depths using both physics-based numerical modeling and machine learning (ML) algorithms.
  • To compare the effectiveness of different ML models and validate numerical results against experimental data.
  • To highlight the combined potential of ML and numerical modeling for efficient and accurate scour assessment.

Main Methods:

  • Machine learning (ML) algorithms, including artificial neural networks and adaptive neuro-fuzzy interface systems, were trained on existing datasets.
  • Physics-based numerical modeling was performed using the REEF3D computational fluid dynamics (CFD) platform.
  • Numerical simulations were conducted for both current-only and coupled wave-current conditions.
  • Model outcomes were validated against statistical measures, reported results, and experimental studies.

Main Results:

  • ML models, specifically artificial neural networks and adaptive neuro-fuzzy interface systems, demonstrated high effectiveness in predicting scour depth.
  • Numerical analysis results showed good agreement with reported experimental values.
  • For current-only conditions, normalized scour depths (S/D) were 0.65 (front) and 0.81 (rear).
  • Under wave-current conditions, the normalized scour depth (S/D) was 0.26.

Conclusions:

  • Both ML and physics-based numerical modeling are valuable tools for assessing scour depth in offshore structures.
  • ML algorithms offer an effective and efficient approach, complementing traditional numerical methods.
  • The study confirms the reliability of numerical simulations and the potential of ML for timely and accurate scour analysis.