Local Scour Around Marine Structures: A Comprehensive Review of Influencing Factors, Prediction Methods, and Future Directions
Abstract
1. Introduction
2. Main Factors Affecting Local Scour
2.1. Hydrodynamic Factor
2.1.1. Flow Velocities, Waves, and Wave–Current Interactions
2.1.2. Turbulence
2.2. Influence of Structural Properties and Spatial Layout on Scour Behavior
2.2.1. Structural Size and Shape
2.2.2. Spatial Arrangement of Structures
2.3. Influence of Seabed Sediment Properties on Local Scour
2.4. Other Factors
2.4.1. Tides
2.4.2. Climate Change
2.5. Scour Protection and Mitigation Measures
3. Local Scour Prediction Methods
3.1. Numerical Modeling of Local Scour
3.1.1. CFD-Based Single-Phase Model
3.1.2. Two-Phase Model
3.1.3. SPH Method
3.2. Local Scour Prediction Equation
Formula/Model | Advantages | Limitations | Applicable Conditions |
---|---|---|---|
HEC-18 Equation [153] | 1. Performs well for laboratory data prediction results; | 1. Prediction of field data often tends to overestimate scour depth; | 1. Suitable for bridge piers of simple geometry; |
2. Data support: fitted based on a large amount of laboratory data. | 2. The accuracy of scour depth prediction for complex piles is low. | 2. Mainly suitable for clear water scour conditions. | |
FDOT Equation [154] | 1. Considers sediment properties; | 1. The prediction accuracy is relatively low and often overestimated; | 1. Suitable for bridge piers of simple geometry; |
2. Applicable to various shapes. | 2. Theoretical limitations in dealing with complex piers. | 2. Mainly suitable for clear water scour conditions. | |
65-1R and 65-2 [145] | 1. Performs well in field data (especially in live-bed conditions); | 1. Underestimation of scour depth for laboratory data (especially for clear water conditions); | 1. Equation 65-2 generally performs better than 65-1R; |
2. Considers the effect of sediment grain size. | 2. Overestimation of scour depth for large diameter abutments and large abutment–sediment ratios. | 2. Equation 65-2 is recommended for medium-diameter piers (5–15 m) or moderate D/D50 ratios. | |
Amini Baghbadorani Equation [147] | 1. Higher accuracy and lower absolute error in scour depth prediction; | 1. Comprehensive data on geometric parameters of bridge piers and water flow conditions are needed; | Complex bridge pier structures with different geometric parameters. |
2. Considers complex pier structures. | 2. Relatively complex calculation. | ||
Hamidifar Equation [150] | 1. Demonstrates high accuracy under multiple statistical indicators; | 1. May slightly overestimate scour depths in practical applications; | 1. Cylindrical piers in clear water; |
2. Low sensitivity to critical flow rates and less affected by errors in critical flow rate estimates. | 2. Needs to be used in conjunction with specific critical flow equations. | 2. Applicable to both lab and field. | |
Sui Equation [20] | 1. Avoids scale effects; | Limited sediment type. | 1. Single piles in a sandy environment; |
2. Considers the Reynolds number effect. | 2. Specific Reynolds number range. | ||
Crowley Equation [149] | 1. Turbulent energy spectrum attenuation is considered with a theoretical basis; | 1. A deeper understanding of turbulent diffusivity is needed; | 1. For relatively well-defined particle and structure sizes; |
2. More explicit consideration of particle size characteristics. | 2. Needs improvement at low b/D50 values. | 2. Supported by turbulent diffusivity data. | |
Tang Equation [151] | 1. Avoids dependence on the concept of equilibrium scour depth; | 1. The applicability to fine-grained sand needs further verification; | 1. Clean water scour conditions; |
2. Provides better flexibility and fault tolerance. | 2. Accuracy at low flow intensities needs to be improved. | 2. Non-uniform sand beds. |
3.3. Machine Learning
Machine Learning Models | Advantages | Limitations | Applicable Conditions |
---|---|---|---|
Neural Networks [156,160]: ANN, ANFIS, etc. | 1. Powerful nonlinear mapping capability; | 1. “Black box” problem; | Scenarios where the dataset is large and the signal-to-noise ratio is high, and where extreme prediction accuracy is sought. |
2. Good prediction performance; | 2. Strong data dependency; | ||
3. Can be enhanced by optimization algorithms or integration methods. | 3. Risk of overfitting. | ||
Support Vector Machines [22,163]: SVM, SVR, etc. | 1. Capable of being applied to different scour types and conditions; | 1. Requires parameter tuning; | Small to medium-sized datasets with high feature dimensions. |
2. Suitable for dealing with complex nonlinear relationships. | 2. Certain “black box” characteristics; | ||
3. Data-dependent. | |||
Tree-Based Models [22,161,168,170]: DT, M5Tree, M5MT, GTB, REPTree, etc. | 1. Intuitive and interpretable; | 1. Possible overfitting; | Scenarios where physical interpretation or decision analysis of prediction results is required. |
2. Able to handle nonlinear relationships; | 2. Performance is affected by the range of data; | ||
3. Does not require extensive data preprocessing; | 3. Limited interpretability. | ||
4. Good predictive performance. | |||
Boosting/Ensemble Methods [22,168,171]: RF, Boosting Model (AdaBoost, XGBoost, CatBoost, LightGBM), BRT, SGB, etc. | 1. Superior predictive performance; | 1. Parameter optimization requirements; | Various scour prediction tasks with extremely high requirements for prediction accuracy. |
2. Capable of handling complex nonlinear relationships and variable interactions; | 2. Potential risk of overfitting; | ||
3. Wide range of applications. | 3. Performance may vary with specific conditions. | ||
Genetic Algorithm Models [156,161,163,169]: GEP, GP, EPR, MGGP, etc. | 1. Generates explicit prediction formulas; | 1. Complex and computationally expensive parameter optimization; | Exploratory research aims to discover new, concise physical laws or empirical formulas from data. |
2. Dealing with complex nonlinear problems; | 2. Highly dependent on data. | ||
3. Superior prediction performance; | |||
4. Applicable to a wide range of scour conditions and structure types. | |||
Enhanced Models Based on Optimization Algorithms [21,160,172]: ANN-PSO; ANFIS-GA; NF-GMDH-PSO; PSO-XGBoost; RFO-XGBoost; RPSO-XGBoost; RS-REPTree, etc. | 1. Significantly improved prediction accuracy; | 1. Higher demand for data; | When the performance of existing single models fails to meet requirements for specific issues, seek breakthroughs in performance. |
2. Excels in dealing with complex nonlinear relationships; | 2. Requires parameter tuning; | ||
3. Broader scope of application. | 3. Risk of overfitting. |
3.4. Probabilistic Prediction Methods
4. Conclusions and Future Work
- Research on scour mechanisms in complex real marine environments
- 2.
- Development and validation of high-efficiency numerical models for engineering applications
- 3.
- Intelligent prediction methods integrating data-driven and physical mechanisms
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Modeling Approach | Advantages | Limitations | Applicable Conditions |
---|---|---|---|
CFD-based single-phase model | 1. Relatively low computational costs; | 1. Inability to capture microscopic mechanisms; | 1. Macroscopic prediction and engineering design; |
2. Simulates macroscopic scour phenomena; | 2. Reliance on empirical formulas and parameters. | 2. Parametric studies and alternative comparison. | |
3. Mature technology and flexible framework. | |||
The Eulerian–Lagrangian two-phase model | 1. Models microscopic mechanisms; | 1. Extremely high computational cost; | 1. Mechanism investigation; |
2. Captures particle kinematics; | 2. Scenarios requiring detailed interactions; | ||
3. Considers true fluid–particle coupling; | 2. Limited number of particles. | 3. Low to medium sediment transport conditions. | |
4. Includes inter-particle contact. | |||
The Eulerian–Eulerian two-phase model | 1. Capable of modeling the interaction of two phases, fluid and sediment, as a continuous medium; | 1. Ignores the discrete nature of particles; | Suitable for high-concentration sediment transport conditions. |
2. Computational efficiency is superior to DEM for scenarios with high sediment concentrations. | 2. Relies on constitutive relationships; | ||
3. High computational costs. | |||
SPH method | 1. Excellent at handling large deformations; | 1. Higher computational costs; | Flows with complex interfaces. |
2. Avoids mesh-related issues. | 2. More complex realization of boundary conditions. |
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Duan, B.; Wang, D.; Qin, C.; Duan, L. Local Scour Around Marine Structures: A Comprehensive Review of Influencing Factors, Prediction Methods, and Future Directions. Buildings 2025, 15, 2125. https://doi.org/10.3390/buildings15122125
Duan B, Wang D, Qin C, Duan L. Local Scour Around Marine Structures: A Comprehensive Review of Influencing Factors, Prediction Methods, and Future Directions. Buildings. 2025; 15(12):2125. https://doi.org/10.3390/buildings15122125
Chicago/Turabian StyleDuan, Bingchuan, Duoyin Wang, Chenxi Qin, and Lunliang Duan. 2025. "Local Scour Around Marine Structures: A Comprehensive Review of Influencing Factors, Prediction Methods, and Future Directions" Buildings 15, no. 12: 2125. https://doi.org/10.3390/buildings15122125
APA StyleDuan, B., Wang, D., Qin, C., & Duan, L. (2025). Local Scour Around Marine Structures: A Comprehensive Review of Influencing Factors, Prediction Methods, and Future Directions. Buildings, 15(12), 2125. https://doi.org/10.3390/buildings15122125