Search Results (104)

Local pier scour remains one of the leading causes of bridge failure, calling for predictions that are both accurate and uncertainty-aware. This study develops an interpretable data-driven framework that couples CatBoost (Categorial Gradient Boosting) for deterministic point prediction with NGBoost (Natural Gradient Boosting) for probabilistic prediction. Both models are trained on a laboratory dataset of 552 measurements of local scour at bridge piers using non-dimensional inputs (y/b, V/V_c, b/d₅₀, Fr). Model performance was quantitatively evaluated using standard regression metrics, and interpretability was provided through SHAP (Shapley Additive Explanations) analysis. Monte Carlo–based reliability analysis linked the predicted scour depths to a reliability index β and exceedance probability through a simple multiplicative correction factor. On the held-out test set, CatBoost offers slightly higher point-prediction accuracy, while NGBoost yields well-calibrated prediction intervals with empirical coverages close to the nominal 68% and 95% levels. This framework delivers accurate, interpretable, and uncertainty-aware scour estimates for target-reliability, risk-informed bridge design. Full article

The present study concerns the re-scour of a submarine pipeline after its scour-sagging-burial by modeling a tilting pipeline with varying embedment along the pipeline axis. The effect of the tilting angle on characteristics of three-dimensional scour was investigated, in terms of the scour topographies, the scour depth, as well as the scour propagation along the pipeline. Two previously undetected scour topographies, i.e., sand ripples that extend along the pipeline axis in the downstream direction of the pipeline and scour pits below the pipeline perpendicular to the pipeline axis, were found to significantly affect the development of the scour propagation and the scour depth. The whole scour propagation along the pipeline can be divided into the rapid scouring phase and the slow scouring phase. The transition point between the two phases takes place at the initial embedment-to-diameter ratio of 0.3. With the increase of the incident angle from 0° to 45°, the scour propagation rate increases during the rapid scouring phase but decreases during the slow scouring phase. A predictive model of the scour propagation rate was established based on the erosion characteristics of sediment and the shear stress magnification factor under the pipeline. The newly predictive model of scour propagation rate is found to provide satisfactory results for a tilting submarine pipeline under different flow incident angles. Full article

Local scour around support structures has remained a critical barrier to tidal stream turbine deployment in energetic marine channels since loss of embedment and bearing capacity has undermined stability and delayed commercialization. This review identifies key mechanisms, practical implications, and forward-looking strategies related to local scour. It highlights that rotor operation, small tip clearance, and helical wakes can significantly intensify near-bed shear stress and erosion relative to monopile foundations without turbine rotation. Scour behavior is compared across monopile, tripod, jacket, and gravity-based foundations under steady flow, reversing tides, and combined wave and current conditions, revealing their influence on depth and morphology. The review further assesses coupled interactions among waves, oscillatory currents, turbine-induced flow, and seabed response, including sediment transport, transient pore pressure, and liquefaction risk. Advances in prediction methods spanning laboratory experiments, high-fidelity simulations, semi-empirical models, and data-driven techniques are synthesized, and mitigation strategies are evaluated across passive, active, and eco-integrated approaches. Remaining challenges and specific research needs are outlined, including array-scale effects, monitoring standards, and integration of design frameworks. The review concludes with future directions to support safe, efficient, and sustainable turbine deployment. Full article

Offshore wind turbine support structures are in harsh and unsteady marine environments, and their dynamic characteristics could change gradually after long-term service. To better understand the status and improve remaining life estimation, it is essential to conduct in situ measurement and update the numerical models of these support structures. In this paper, the modal properties of a 5.5 MW offshore wind turbine were first identified by a widely used operational modal analysis technique, frequency-domain decomposition, given the acceleration data obtained from eight sensors located at four different heights on the tower. Then, a finite element model was created in MATLAB R2020a and a set of model parameters including scour depth, foundation stiffness, hydrodynamic added mass and damping coefficients was updated in a Bayesian inference frame. It is found that the posterior distributions of most parameters significantly differ from their prior distributions, except for the hydrodynamic added mass coefficient. The predicted natural frequencies and damping ratios with the updated parameters are close to those values identified with errors less than 2%. But relatively large differences are found when comparing some of the predicted and identified mode shape coefficients. Specifically, it is found that different combinations of the scour depth and foundation stiffness coefficient can reach very similar modal property predictions, meaning that model updating results are not unique. This research demonstrates that the Bayesian inference framework is effective in constructing a more accurate model, even when confronting the inherent challenge of non-unique parameter identifiability, as encountered with scour depth and foundation stiffness. Full article

This study systematically investigates the influence of concrete substrate surface texture characteristics on the adhesion strength of waterborne epoxy coatings. By employing surface treatment techniques such as brush scouring and grinding, the surface roughness, pore structure, and three-dimensional morphology of concrete substrates were quantitatively analyzed using laser scanning and parameter modeling. The correlation between texture parameters (e.g., three-dimensional arithmetic mean roughness S_a and proportion of the hole area) and coating adhesion strength under varying curing temperatures (−18 °C, 20 °C, 40 °C, and 60 °C) was evaluated through pull-off tests and Pearson Correlation Analysis. Results indicate that the absolute proportion of hole area (A_a) (r ≈ −0.93) and S_a (r ≈ −0.81) are key factors affecting adhesion. Surface treatments, including 5 h scouring and 40 min grinding, enhanced pull-off strength by 40–60% by optimizing mechanical interlocking. A nonlinear regression model was established to predict adhesion strength, suggesting an operational S_a range of approximately 0.35–0.40 mm for the current coating system at 20 °C. Temperature significantly modulated the adhesion mechanism; low-temperature curing exacerbated pore defects, while high-temperature conditions intensified thermal stress. Practical guidelines to improve permeability include optimizing the surface roughness through grinding, strictly controlling the absolute proportion of holes, and using preheated or low-viscosity resin in a low-temperature environment. Full article

With the advancement of offshore wind energy toward deep-water floating systems, mooring and anchor foundations represent pivotal components whose load-bearing characteristics critically impact the safety and integrity of floating wind turbines. The dynamic responses of embedded suction anchor foundations for offshore wind mooring systems under cyclic and irregular mooring loads are investigated. A computationally efficient simplified methodology is developed, leveraging dynamic impedance theory and coordinate transformation techniques to derive stiffness and damping matrices at arbitrary padeye locations based on the assumption of a linearly elastic, homogeneous soil medium. The equations of motion are solved using both frequency-domain and time-domain approaches to predict responses under harmonic cyclic loading and irregular nonlinear mooring forces. The model demonstrates excellent self-consistency and achieves high-fidelity agreement with 3D finite element benchmarks at significantly reduced computational costs. Parametric analyses reveal that padeye elevation critically governs dynamic response, enabling rapid optimization of its placement to minimize displacements and enhance safety. Furthermore, scour depth significantly impacts foundation behavior, with severe scour conditions markedly increasing horizontal displacements and rocking rotations, thereby elevating structural failure risks. The proposed framework provides an efficient tool for preliminary design and risk assessment, highlighting scour mitigation as crucial for foundation integrity. Full article

Complex three-dimensional (3D) flows generally occur around structures such as bridge piers and groins installed in river channels during floods, resulting in local scour in movable beds. Most analyses of bed deformation, including local scour around structures in supercritical flow fields, have been conducted using two-dimensional (2D) models. However, the inevitability of 3D flows around structures renders 2D models (assuming hydrostatic pressure distribution) inadequate in reproducing local scour induced by these flows. Therefore, 3D models are necessary for accurate local scour prediction, even in these flow conditions. This study presents the differences in reproducibility between 2D shallow-water hydrodynamic models and 3D hydrodynamic models for the flow and local scour around structures in steep channels under supercritical flow conditions. Both hydrodynamic and mixed-sand bed deformation models, incorporating the fractional area/volume obstacle representation (FAVOR) method, were developed and applied to hydraulic experiments. As a result, the proposed 3D model accurately reproduced the experimental results of local scour. It was also shown that a 2D model may be sufficient for predicting flows and approximate bed deformations when the constriction length formed by the structure is short. By contrast, the application of a 3D model was necessary for predicting bed deformations when the constriction length is long. In addition, the numerical models using the FAVOR method could smoothly analyse flows and bed deformations in channel shapes that do not follow the coordinate system. Full article

Predicting how vegetation–debris interactions reshape alternate sandbars under a steady subcritical flow remains poorly understood in laboratory-to-field scaling. This study quantified how vegetation density and layout interact with debris geometry to control scouring and deposition and developed an empirical tool to predict normalized bed-level changes. Flume experiments investigated how vegetation–debris interactions regulate the hydromorphodynamics of non-migrating alternate sandbars under a steady subcritical flow (Q = 0.003 m³/s; slope = 1/200). Vegetation patches were configured in two spatial layouts—upstream (apex) and river line (edge), at varying densities, with and without debris (I-type: wall-like; U-type: horseshoe-shaped). Results indicated that dense upstream vegetation combined with I-type debris produced the strongest morphodynamic response, generating maximum scour, corresponding to the maximum bed-elevation changes (Δz) normalized by water depth (h) (dimensionless Δz/h) values of −1.55 and 1.05, and sustaining more than 70% of the downstream morphodynamic amplitude. In contrast, U-type debris promoted distributed deposition with a milder scour, while sparse vegetation yielded weaker, more transient responses. Debris geometry-controlled flow partitioning: the I-type enhanced frontal acceleration, whereas the U-type facilitated partial penetration and redistribution. To integrate these findings into predictive frameworks, an empirical regression model was developed to estimate Δz/h from the vegetation density, distribution, and debris geometry, with an additional blockage index to capture synergistic effects. The model achieved 87.5% prediction within ±20% error, providing a practical tool for anticipating scour and deposition intensity across eco-hydraulic configurations. These insights advance intelligent water management by linking morphodynamic responses with predictive modeling, supporting flood-resilient river engineering, adaptive channel stability assessments, and nature-based solutions. Full article

Submerged vanes offer a promising solution for reducing scour depth around hydraulic structures such as bridge piers by modifying near-bed flow patterns. However, temporal changes in bed profiles around a cylindrical pier remain insufficiently quantified. This study employs three machine learning models (MLMs), gene expression programming (GEP), support vector regression (SVR), and an artificial neural network (ANN), to simulate the temporal evolution of the bed profile around a cylindrical pier under constant subcritical flow. We use a published laboratory flume dataset (106 observations) obtained for a pier of diameter $D=6\mathrm{cm}$ and uniform sediment with median size $D_{50}=0.43\mathrm{mm}$. Geometric/layout parameters of the submerged vanes (number n, transverse offset z, longitudinal spacing e, and distance from the pier base a) were fixed at their reported optima, and subsequent tests varied installation angles $\alpha$ to minimize scour. Models were trained on $70\%$ of the data and tested on $30\%$ using dimensionless inputs $(t/t_e,{\alpha }_1,{\alpha }_2,{\alpha }_3)$ with t the elapsed time from the start of the run and $t_e$ the equilibrium time at which scour growth becomes negligible and response $s/D$ with s the instantaneous scour depth at time t. The GEP model with a three-gene structure achieved the best accuracy. During training and testing, GEP attained $(\mathrm{RMSE}, \mathrm{MAE}, R^2, {(D_s/D)}_{\mathrm{DDR}\left(\max \right)})=(0.0864,0.0681,0.9237,4.25)$ and $(0.0729,0.0641,0.9143,4.94)$, respectively, where $D_s$ denotes scour depth at equilibrium state, D is the pier diameter, and $\mathrm{DDR}\left(\max \right)\equiv max(D_s/D)$ is the maximum dimensionless depth ratio observed/predicted. Full article

Piles are common support elements for marine and coastal structures. The scour around pile foundations caused by currents is a major threat to the stability and safety of these structures. The empirical equations commonly used for estimating the equilibrium scour depth around pile groups are limited in their predicative capability, especially when the current approaches the pile group at an angle. This study applies a Multi-Layer Perceptron Backpropagation (MLP/BP) neural network to develop a general model for predicting the local maximum equilibrium scour depth around pile groups in steady currents. The input parameters for the model include all relevant non-dimensional hydrodynamic and structural variables taking full account of the effects of the pile group arrangement and its orientation relative to the approaching current. The model’s performance was evaluated by comparing its predictions against those generated by multiple other machine learning methods, as well as against results from widely used empirical formulas. A comprehensive sensitivity analysis is carried out to determine the importance ranking of the input parameters on model accuracy. Full article

Computing the temporal variation in clearwater scour depth around abutments is important for bridge foundation design. To reach the equilibrium scour depth at bridge abutments takes a very long time. However, the corresponding times under prototype conditions can yield values significantly greater than the time to reach the design flood peak. Therefore, estimating the temporal variation in scour depth is necessary. This study evaluates multiple machine learning (ML) models to identify the most accurate method for predicting scour depth (Ds) over time using experimental data. The dataset of 3275 records, including flow depth (Y), abutment length (L), channel width (B), velocity (V), time (t), sediment size (d₅₀), and Ds, was used to train and test Linear Regression (LR), Random Forest Regressor (RFR), Support Vector Regression (SVR), Gradient Boosting (GBR), XGBoost, LightGBM, and KNN models. Results demonstrated the superior performance of AI-based models over conventional regression. The RFR model achieved the highest accuracy (R² = 0.9956, Accuracy = 99.73%), followed by KNN and GBR. In contrast, the conventional LR model performed poorly (R² = 0.4547, Accuracy = 57.39%). This study confirms the significant potential of ML, particularly ensemble methods, to provide highly reliable scour predictions, offering a robust tool for enhancing bridge design and safety. Full article

Local scour around bridge piers is one of the primary causes of structural failure in bridges. Therefore, this study focuses on addressing the estimation of maximum scour depth (d_sm), which is essential for safe and resilient bridge design. Many studies in the last eight decades have included metadata collection and developed around 80 empirical formulas using various scour-affecting parameters of different ranges. To date, a total of 33 formulas have been comparatively analyzed and ranked based on their predictive accuracy. In this study, novel formulas using semi-empirical methods and gene expression programming (GEP) have been developed alongside an artificial neural network (ANN) model to accurately estimate d_sm using 768 observed data points collected from published work, along with eight newly conducted experimental data points in the laboratory. These new formulas/models are systematically compared with 74 empirical literature formulas for their predictive capability. The influential parameters for predicting d_sm are flow intensity, flow shallowness, sediment gradation, sediment coarseness, time, constriction ratio, and Froude number. Performances of the formulas are compared using different statistical metrics such as the coefficient of determination, Nash–Sutcliffe efficiency, mean bias error, and root-mean-squared error. The Gauss–Newton method is employed to solve the nonlinear least-squares problem to develop the semi-empirical formula that outperforms the literature formulas, except the formula from GEP, in terms of statistical performance metrics. However, the feed-forward ANN model outperformed the semi-empirical model during testing and validation phases, respectively, with higher CD (0.790 vs. 0.756), NSE (0.783 vs. 0.750), lower RMSE (0.289 vs. 0.301), and greater prediction accuracy (64.655% vs. 61.935%), providing approximately 15–18% greater accuracy with minimal errors and narrower uncertainty bands. Using user-friendly tools and a strong semi-empirical model, which requires no coding skills, can assist designers and engineers in making accurate predictions in practical bridge design and safety planning. Full article

As the demand for new sustainable solutions for harvesting energy increases, the offshore energy sector focuses on optimising well-established state-of-the-art solutions while striving for new innovative approaches. Hybrid foundation designs have introduced new challenges and raised questions regarding scour and effective countermeasures. To further improve the knowledge regarding scour prediction, this paper presents and analyses the results from an experimental study behaviour of a riprap protection system for a monopile that determines and characterises scour on a flexible arrangement of overlapping sub-areas. The study was complemented by a novel series of tests using a hybrid foundation, where an oscillating surge wave energy converter (OSWEC) type was coupled to the monopile. Despite being submitted to similar hydrodynamic conditions, distinct differences in the scour rate and damage number (S_3D) were observed for both models. Although the OSWEC type contributed to local wave height attenuation (up to a 30% reduction on the leeward side of the hybrid monopile), its oscillatory motion severely aggravated scour, with measured damage rates two to three times higher than those observed in isolated monopiles. These results raise relevant questions about the applicability of existing design formulas for scour protection and underscore the necessity for revised criteria tailored to hybrid offshore foundations. Full article

Overfall flow, characterized by high Froude numbers and intense turbulence, generates boundary shear stress on vertical surfaces, which is considered the direct cause of headcut erosion. This study aims to analyze the hydraulic characteristics of nappe flow over a vertical or near-vertical overfall. Detailed experiments using hot-film anemometry were conducted in an indoor flume to examine the shear stress distribution on vertical surfaces under varying flow rates, overfall heights, and backwater depths. The results show that when the jet dynamic pressure head is less than the backwater depth, the dimensionless relative shear stress and relative depth relationship can be fitted with a beta probability density function. When the dynamic pressure head exceeds the backwater depth, the distribution follows a cubic polynomial form. Dimensional analysis and flow trajectory calculation methods were used to establish shear stress distribution formulas, with determination coefficients of 0.829 and 0.652, and the mean absolute percentage error (MAPE) between the measured and predicted values being 0.106 and 0.081, respectively. The findings provide valuable insights into the effects of complex flow structures on shear stress and offer essential support for the development of scour models for overfall structures. Full article

Scour near various offshore structures has been studied by performing numerical model runs with the modified (Fortran) SEDTUBE model, as a follow-up of an earlier paper on scour near marine offshore structures. A fairly simple 1D numerical model (SEDTUBE model) for the computation of sand transport rates and scour depths near structures on the seabed (berms, bed protections) is proposed, tested and validated. The model domain is a stream tube (varying or constant width) including the loose seabed and the hard layers of (multiple) structures. Hence, the model computes the sediment transport along the bed and over the structure(s). The SEDTUBE model can predict the time evolution of free scour depth around rock berms; bed protections; and pile-type structures, as well as the edge scour further away from the pile, in unidirectional and bidirectional tidal flows (weak and strong currents) in combination with waves over a sandy sediment bed with d₅₀ in the range between 0.2 and 2 mm. Five laboratory and four field cases have been used for validation of the model. The model is much more than a scour model; it can also be used for the prediction of sedimentation in shipping channels. The model is valid for sandy beds and for mud–sand beds with slight cohesive properties. Full article