Prediction Of Scour Depth Research Articles

Bayesian Updating for Prediction of Scour Depth Using Natural Frequency of Monopiles

Scour is a non-negligible issue of monopiles that profoundly threatens the safety of monopile for offshore wind turbines (OWTs). Accurately predicting the scour depth is essential for the design and operation of OWTs. This study introduces a model aimed at predicting scour depth from the aspect of the natural frequency of monopile. The model is developed using uniform design samples to ensure its applicability across a wider range of OWT monopiles and soil properties. To enhance the model accuracy, a Bayesian framework is employed, incorporating prior information. The three main model coefficients are updated iteratively, allowing the predicted scour depth to converge with the observed values. The Monte Carlo Markov chain (MCMC) simulation is utilized to generate the posterior distribution. The model accuracy is validated through 48 representative samples, and the effectiveness of Bayesian updating in improving the model precision is demonstrated by comparing the results prior to and following Bayesian updating. Additionally, the numerical simulations and monitored data confirm the validity of the proposed prediction model.

Experimental study on local scour for large-diameter mono-column composite bucket foundation for offshore wind turbines

The issue of seabed scour around offshore wind turbine foundations is a significant safety concern. In this study, local scour around a specific type of foundation, known as the large-diameter Mono-Column Composite Bucket Foundation (MCCBF) with six connectors, has been investigated through physical experiments. The study involved various flow conditions, including unidirectional flows, tidal flows, and combinations of regular waves and currents. It has been found that under similar flow intensity and orientation angles, unidirectional flow tends to cause deeper scour depths, but with a more limited extent compared to scour induced by tidal flows. The disparity in maximum scour depth between tidal and unidirectional flows decreases as flow velocity increases. Under regular wave-current interaction, both the scour depth and the scour extent are larger than those induced by unidirectional flow. The presence of lee-wake vortices and streamline contractions near the connectors results in larger scour depths on either side of the foundation, especially when the orientation angle is set to 0°. Different methods for predicting scour depth of the MCCBF were applied and discussed.

Utilizing the ANFIS and teaching-learning-based optimization (TLBO) methods to predict the maximum depth of scouring at culvert outlets

ABSTRACT Excessive scouring at culvert outlets is a widespread issue that often leads to culvert failure. At present, there is an absence of a universally applicable model for accurately predicting the occurrence of local scouring at culvert outlets. In this paper, a comparison is made between the performance of existing empirical equations and the results obtained from two advanced methods: adaptive neuro-fuzzy inference system (ANFIS) and the teaching-learning-based optimization (TLBO). This paper presents the development of the TLBO method, based on the ANFIS approach, for simulating scouring at culvert outlets. The algorithms for supervised training are applied by utilizing data collected from published studies. The results illustrate that the ANFIS–TLBO model successfully predicts scouring depth at culvert outlets with higher accuracy in comparison to existing empirical formulas. Furthermore, the model exhibits a wider range of applicability, accommodating diverse conditions. By integrating the ANFIS approach and TLBO optimization, the model surpasses the limitations of traditional empirical equations, providing engineers with a more reliable tool for predicting scour depth at culvert outlets. Furthermore, the proposed model has the largest accuracy compared to the previously developed empirical models. Ultimately, it can be concluded that the hybrid ANFIS–TLBO is a robust approach for scour depth prediction downstream of culverts.

Arctic tern-optimized weighted feature regression system for predicting bridge scour depth

This paper presents a pioneering artificial intelligence (AI) solution – the Arctic Tern-Optimized Weighted Feature Least Squares Support Vector Regression (ATO-WFLSSVR) system to aid civil engineers in accurately predicting scour depth at bridges. This prediction system amalgamates the strengths of hybrid models by uniting a metaheuristic optimization algorithm with weighted features and least squares support vector regression (WFLSSVR). The metaheuristic algorithm concurrently optimizes all hyperparameters of constituent WFLSSVR models, resulting in a highly effective system. Validation involves a comprehensive assessment using two case studies, which include datasets of scour depths across various complexities and pier foundation types. Comparative analyses against single AI models, conventional ensemble models, hybrid techniques, and empirical methods demonstrate that ATO-WFLSSVR's reliability outperforms others in performance evaluation metrics. Specifically, for the field dataset, ATO-WFLSSVR achieves MAPE and R values of 20.92% and 0.9435, respectively, and for scour depth data at complex pier foundations, it records MAPE and R values of 6.49% and 0.9384, respectively. The automated predictive analytics underscore the robustness, efficiency, and stability of ATO-WFLSSVR compared to existing methods. This study's notable contributions include the development of an innovative optimization algorithm named Arctic Terns Optimizer (ATO), proficiency in solving high-dimensional optimization problems, and the creation of a user-friendly graphical interface system, a promising tool for civil engineers to estimate scour depth at bridges. Further testing and evaluation of ATO-WFLSSVR across diverse datasets encompassing more complex scenarios are recommended. The data and source code for this study are currently accessible at https://www.researchgate.net/profile/Jui-Sheng-Chou/publications.

Benchmarking the performance and uncertainty of machine learning models in estimating scour depth at sluice outlets

ABSTRACT This study investigates the performance of six machine learning (ML) models – Random Forest (RF), Adaptive Boosting (ADA), CatBoost (CAT), Support Vector Machine (SVM), Lasso Regression (LAS), and Artificial Neural Network (ANN) – against traditional empirical formulas for estimating maximum scour depth after sluice gates. Our findings indicate that ML models generally outperform empirical formulas, with correlation coefficients (CORR) ranging from 0.882 to 0.944 for ML models compared with 0.835–0.847 for empirical methods. Notably, ANN exhibited the highest performance, followed closely by CAT, with a CORR of 0.936. RF, ADA, and SVM performed competitive metrics around 0.928. Variable importance assessments highlighted the dimensionless densimetric Froude number (Fd) as significantly influential, particularly in RF, CAT, and LAS models. Furthermore, SHAP value analysis provided insights into each predictor's impact on model outputs. Uncertainty assessment through Monte Carlo (MC) and Bootstrap (BS) methods, with 1,000 iterations, indicated ML's capability to produce reliable uncertainty maps. ANN leads in performance with higher mean values and lower standard deviations, followed by CAT. MC results trend towards optimistic predictions compared with BS, as reflected in median values and interquartile ranges. This analysis underscores the efficacy of ML models in providing precise and reliable scour depth predictions.

Prediction of local scour depth around bridge piers in clay-sand bed using the ANN method

Prediction of local scour depth around bridge piers in clay-sand bed using the ANN method

Experimental, Analytical, and Numerical Evaluation of Bridge Pier Scouring

Abstract Due to the rising number of incidents of bridge failure, scouring around bridges has received a lot of attention. Accurate scour depth prediction is a challenge for researchers and experts, despite massive global research efforts. Estimation the scour depth is crucial for both a safety and economy. Numerous factors affect the scour depth. In this work, the scour depth of three distinct pier geometries – circular, oblong, and rectangular – is thoroughly investigated, and the effect of water depth and discharge on the scour depth of a given geometry is evaluated. Three pier shapes were the subject of a total of 27 trials, nine of which combined three discharges with varying flume water depth. Experimental observation of scour depth shows good agreement with analytical and numerical scour depth values. A comparative examination of analytical, experimental, and numerical findings indicates that numerical models can effectively estimate scour depth, offering a cost-effective and time-saving methodology. Results showed that circular and oblong pier geometry shows lesser scour depth than rectangular pier geometry for a specific combination of flow depth and discharge. Scour depth decreased with increasing flume water depth for the same discharge. For the constant flume water depth, higher discharge results in greater scour depth.

Predicting maximum scour depth uncertainty downstream of grade control structures: a multi-model approach with Bayesian model averaging

This research proposes a novel multi-model combination approach to overcome the limitations of traditional methods in predicting the scour depth downstream of grade control structures. The study evaluates 26 scenarios using a combination of five individual models and assesses the efficiency of four combination methods. Results indicate that GEP and SVM outperform traditional combination methods. Additionally, the study employs Bayesian Model Average (BMA) to assess the uncertainty originating from individual models. The research concludes by providing predictive equations for GEP and SVM, enabling users to apply these methods for accurate maximum scour depth prediction. In the ideal case, the SVM model had an R 2 = 0.91, with an RMSE of 0.84, whereas the GEP model achieved an R 2 = 0.94, with an RMSE of 0.67. The proposed multi-model approach is more reliable and accurate for predicting maximum scour depth downstream of GCS.

Introducing high-order response surface method for improving scour depth prediction downstream of weirs

Scour depth downstream of weirs is considered one of the most important hydraulic problems, which greatly influences the stability of weirs. Recently, artificial intelligence (AI) methods have become increasingly popular in modeling hydraulic variables, especially scour depth, because they can capture nonlinear relationships between input variables and their associated objectives. Despite their importance, these models have problems with hyperparameter tuning in scour depth modeling due to their structures, so algorithms must be used to tune the hyperparameters. Moreover, these algorithms are usually tuned by using the trial-and-error method to select the hyperparameters such as the number of hidden nodes, transfer function, and learning rate, and in this case, the main problem is overfitting during the training phase. To solve these problems, the high-order response surface method (HORSM), an improved version of the response surface method (RSM), is used as an alternative approach for the first time in this study to predict the scour depth. The HORSM model is based on high-order polynomial functions (from two to six) compared with the artificial neural network model (ANN). The findings indicate that the fifth order of the HORSM polynomial function yields the most precise predictions, with a higher coefficient of determination (R2) of 0.912 and Willmott Index (WI) of 0.972 compared to the values obtained using ANN (R2 = 0.886 and WI = 0.927). Moreover, the accuracy of the predictions is represented by a reduction of the mean square error by up to 44.17 and 29.01% compared to the classical RSM and ANN, respectively. The suggested model established an excellent correlation and accuracy with experimental values.

Advancements in predicting scour depth induced by turbulent wall jets: A comparative analysis of mathematical formulations and machine learning models

This study examines the scour depth induced by turbulent wall jets and proposes novel mathematical formulations to predict the depth of scouring. Through a comprehensive gamma test, key parameters influencing the scour depth are identified, including the apron length, densimetric Froude number, median sediment size, tailwater level, Reynolds number, and Froude number of the jet. Regression analysis is subsequently conducted to establish relationships between the dependent parameter and the aforementioned independent variables. A comparative analysis is then undertaken between the measured scour depths and those predicted by existing equations from previous studies. Furthermore, predictive models leveraging the support vector machine, artificial neural network with particle swarm optimization, M5 tree algorithm, gene expression programming, and adaptive neuro-fuzzy inference system (ANFIS) are developed using the collected data. Statistical metrics are employed to evaluate the performance of each model and the regression equation. The effectiveness of each model in predicting scour depth is demonstrated. Notably, ANFIS yields a coefficient of determination of 0.809 and a root mean square error (RMSE) of 1.585. Multi-nonlinear regression analysis exhibits a coefficient of determination of 0.752 and an RMSE of 0.421, while the M5 tree achieves a coefficient of determination of 0.739 and an RMSE of 1.874, demonstrating superior performance compared to other machine learning techniques and regression equations employed in this study.

Hydrodynamic Modeling and Comprehensive Assessment of Pier Scour Depth and Rate Induced by Wood Debris Accumulation

This study mainly investigates the impact of debris accumulation on scour depth and scour hole characteristics around bridge piers. Through controlled experiments with uniform sand bed material, the influence of various debris shapes (high wedge, low wedge, triangle yield, rectangular, triangle bow, and half-cylinder), upstream debris length, downstream debris extension, and debris thickness on scour depth and scour hole area and volume around the cylindrical pier were analyzed. The findings revealed that the shape and location of debris in the water column upstream of piers are key factors that determine the depth of scour, with high wedge shapes inducing the deepest scour and potentially the largest scour hole, particularly when positioned close to the pier and fully submerged. Scenarios in which triangle bow debris was submerged at full depth upstream of the pier closely resembled situations devoid of debris. Conversely, debris extension downstream of the pier was found to reduce local scour depth while concurrently enlarging the dimensions of the scour hole. The existing scour prediction equations tend to overestimate scour depth in scenarios involving debris, particularly when applying effective and equivalent pier width. This discrepancy arises because these equations were originally developed to predict scour depth around piers in the absence of debris. In response, a refined model for predicting scour induced by debris was proposed, integrating factors such as upstream debris length, downstream extension, obstruction percentage, and debris shape factor. This model demonstrated strong agreement with experimental data within the scope of this study and underwent further validation using additional experimental datasets from other research endeavors. In conclusion, this experimental study advances the comprehension of scour processes around cylindrical bridge piers, providing valuable insights into the role of debris characteristics and positioning.

Interpretable machine learning scheme for predicting bridge pier scour depth

Predicting scour depth around bridge piers is challenging due to the involvement of various uncertain factors and complex processes, making it difficult to obtain accurate results using traditional deterministic models. Recently, nonlinear analysis and prediction using machine learning (ML) techniques that glean statistical structures from input/output data have received substantial attention. With the use of ML, however, interpretation becomes difficult with increased complexity and diversity of the analysis parameters. The study herein proposes a local scour depth prediction model around piers using interpretable ML.A scour depth prediction model was constructed using the state-of-the-art eXtreme Gradient Boositng (XGB) model, and Shapley additive explanations (SHAP) based on the cooperative game theory. The ML model demonstrated excellent prediction accuracy (R2: 0.75, RMSE: 0.23) compared with existing empirical formulas used for assessment of pier scour. Model analysis results demonstrated that for each input variable, the distribution of SHAP values for scour depth is consistent with the general theoretical knowledge about the factors influencing scour depth, suggesting that the predictions of the XGB model are reasonable. Considering accuracy and conservatism, design recommendations using two statistical parameters: mean absolute percentage error (MAPE) and the percentage of predicted scour depth exceeding measured scour depth, referred to as the “level of conservatism” were proposed as a means of providing the flexibility of including a target level of accuracy and conservatism in the computed scour depth.

Clear Water Scour Depth Prediction using Gradient Boosting Machine and Deep Learning

The scouring process in adjacent to spur dikes has the potential for compromising the stability of riverbanks. Hence, it is necessary for river engineering to conduct precise measurement of maximum scour depth in the vicinity of spur dikes. Nevertheless, the determination of the maximum scour depth has proven to be a challenging task, primarily due to the complex nature of the scour phenomena associated with these structures. In this study, two data-driven models, namely the Gradient Boost Machine (GBM) and Deep Learning (DL), were developed to predict the clear water scour depth near to a spur dike. A total of 154 distinct observations have been collected from previous literatures. A total of 103 observations were utilized for training the model, while 53 observation were allocated for validation purposes. Several performance assessment measures were employed to evaluate the performance of the models, including the correlation coefficient (CC), root-coefficient of determination (R2), scattered plot, variation plot, and box plot. GBM outperformed the DL on the basis of above-mentioned assessment measures. Sensitivity analysis suggests that l/d50 is the most influences input parameter. Thus, the conclusion suggested that both the data-driven model can be used in the prediction of the clear water scour depth around spur dikes but GBM have highest accuracy.

New insights into culvert scour depth calculation by soft computing models using multi-model ensemble approach and uncertainty analysis

ABSTRACT The accurate prediction of scour depth in culverts is crucial to ensure public safety and due to the uncertainties in classical empirical/deterministic equations, it remains challenging. This study presents new ensemble multi-models to predict scour depth and quantify model bias and uncertainty. In this study, culvert scour was predicted by gene expression programming (GEP) and least square support vector machine (LSSVM), firstly based on dimensionless parameters. In the multi-model ensemble strategy, four ensemble approaches, namely SM, WM, LSSVM, and BMA were applied. The LSSVM showed overall best skill for the multi-model approach, that reduced the error by 61.3% and increased the coefficient of determination by 52.1%. The results showed the proposed approach can satisfy the requirements of simplicity, applicability, and accuracy at the same time. For application purposes, we provided a simple code to obtain scour depth with great accuracy using LSSVM. BMA and LSSVM were used to quantify the scour culvert uncertainty. The results of the uncertainty analysis showed that the LSSVM predicts scour depth quite well and generates high-quality prediction intervals. The results revealed that a multi-model ensemble strategy improved accuracy, certainty and reliability for culvert scour compared to empirical equations.

Experimental investigation of pier scour depth and its scour hole pattern for different shapes

Local scour, a complex phenomenon in river flows around piers with movable beds, can damage bridge piers during high floods. Predicting scour depth accurately is vital for safety and economic reasons, especially for large bridges. This study using hydraulic flume laboratory experiments compared diamond, square, and elliptical pier models of different sizes under steady clear-water conditions considering different flow rates and discharge levels to identify the most efficient shape with less local scour. Local scour, a complex phenomenon in three-dimensional flow around piers in rivers with movable beds, can lead to detrimental effects on bridge piers due to high flood velocities. Accurate prediction of scour depth is crucial for economic and safety reasons, especially for large bridges with complex piers. Hydraulic engineers are keen on forecasting the equilibrium scour depth. To achieve this, laboratory testing compared diamond, square, and elliptical pier models under steady clear-water conditions to identify the most efficient pier shape with less local scour. This research provides valuable insights for optimizing pier design to enhance bridge stability and resilience against scour-induced risks. A variety of configurations, including different sizes and shapes of piers were experimented with in the flume using diamond, square, and elliptical shapes. The test results showed that the local scour depth around elliptical piers was around 29.16% less, and around diamond piers, it was approximately 16.05% less compared to the scour depth observed around square piers with the same dimensions. The researchers also observed distinct patterns of scouring around different pier shapes. Specifically, the square-shaped piers displayed the highest level of scouring depth, that is, 48 mm, followed by the diamond-shaped pier which experienced a scouring depth of 48 mm while the elliptical-shaped piers experienced the least amount of scouring depth, that is, 34 mm. The test results also demonstrated that pier size significantly influences scouring, with an increase in pier size from 3 × 3 cm2 to 5 × 5 cm2 leading to a rise in scour depth by 26.04%. Moreover, this study findings also elucidated that an increase in flow results in an increase of in scouring depth i.e., elevating the discharge from 0.0026 cumecs to 0.0029 cumecs led to a 28.13% increase in scouring depth for the identical pier size. These findings provide valuable insights into the hydraulic behavior of various pier shapes and can aid in the optimization of bridge design and hydraulic engineering practices. The investigations further revealed that local scouring is sensitive not only to pier dimensions but also to other critical parameters, including flow rate, time of exposure, and the size of a pier.

Scouring around bridge pier: A comprehensive analysis of scour depth predictive equations for clear-water and live-bed scouring conditions

Abstract The failure of bridges, attributed to bridge pier scouring, poses a significant challenge in ensuring safe and cost-effective design. Numerous laboratory and field experiments have been conducted to comprehend the mechanisms and predict the maximum equilibrium scour depth around bridge piers. Over the last eight decades, various empirical methods have been developed, with different authors incorporating diverse influencing parameters that significantly impact the estimation of equilibrium scour depth around bridge piers. This paper aims to consolidate: (1) available experimental and field data sets on different types of bridge pier scouring, (2) the influence of flow and roughness parameters on both clear water scouring (CWS) and live bed scouring (LBS), and (3) existing empirical equations suitable for computing equilibrium scour depth around a bridge pier under CWS and LBS conditions. The presented research encompasses over 80 experimental/field data sets and more than 60 scour-predicting equations developed for CWS and LBS conditions in the past eight decades. Based on the performance of different empirical models in predicting scour depth ratio, suitable models are recommended for CWS and LBS conditions.

Influence of Cumulative Effective Stream Power on Scour Depth Prediction Around Bridge Piers in Cohesive Bed Sediments

Streambed scour in cohesive sediment is complex because erosion processes depend on the physical, geochemical, and biological properties of the sediment. The scouring processes can also be characterized as a slow fatigue phenomenon. Therefore, repetitive hydraulic loadings from multiple stormflow events are likely necessary for equilibrium scour depths to develop in cohesive sediment compared with non-cohesive sediment. Cumulative effective stream power, which is a surrogate measure of effective stream power duration, showed a significant relation with scour development and propagation in cohesive sediments around bridge piers, where results from this study identified a statistically significant correlation between cumulative effective stream power and the observed scour depths around different bridge piers ( R2 = 0.56, p < 0.001). However, some localized and site-specific variations were observed. It was also observed that scour depth development in cohesive soil appeared to be dependent on effective shear duration, rather than the number of flow events above erosion threshold values. In addition, the relationship between an erodibility index ( K) and critical stream power showed a significant statistical correlation ( R2 = 0.61, p = 0.017). Results from this study deviated from the Annandale empirical relationship for sediments when K < 0.1. This finding supports that site-specific critical stream power should be measured using an empirical relationship for cohesive bed sediments to predict scour depths.

Efficient data-driven machine learning models for scour depth predictions at sloping sea defences

Seawalls are critical defence infrastructures in coastal zones that protect hinterland areas from storm surges, wave overtopping and soil erosion hazards. Scouring at the toe of sea defences, caused by wave-induced accretion and erosion of bed material imposes a significant threat to the structural integrity of coastal infrastructures. Accurate prediction of scour depths is essential for appropriate and efficient design and maintenance of coastal structures, which serve to mitigate risks of structural failure through toe scouring. However, limited guidance and predictive tools are available for estimating toe scouring at sloping structures. In recent years, Artificial Intelligence and Machine Learning (ML) algorithms have gained interest, and although they underpin robust predictive models for many coastal engineering applications, such models have yet to be applied to scour prediction. Here we develop and present ML-based models for predicting toe scour depths at sloping seawall. Four ML algorithms, namely, Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Artificial Neural Networks (ANNs), and Support Vector Machine Regression (SVMR) are utilised. Comprehensive physical modelling measurement data is utilised to develop and validate the predictive models. A Novel framework for feature selection, feature importance, and hyperparameter tuning algorithms are adopted for pre- and post-processing steps of ML-based models. In-depth statistical analyses are proposed to evaluate the predictive performance of the proposed models. The results indicate a minimum of 80% prediction accuracy across all the algorithms tested in this study and overall, the SVMR produced the most accurate predictions with a Coefficient of Determination (r2) of 0.74 and a Mean Absolute Error (MAE) value of 0.17. The SVMR algorithm also offered most computationally efficient performance among the algorithms tested. The methodological framework proposed in this study can be applied to scouring datasets for rapid assessment of scour at coastal defence structures, facilitating model-informed decision-making.

Local scour depth at piles group exposed to regular waves: On the assessment of expressions based on classification concepts and evolutionary algorithms

An accurate estimation of local scour depth around piles group is inevitably essential to provide stability of marine structures. Over the past decades, many investigations have been made to understand the scouring process at piles group exposed to waves for field and experimental scales. This study aims to predict the wave-induced local scour depth by various robust Data Driven Models (DDMs) and Machine Learning Models (MLMs) developed by classification and evolutionary concepts: Model Tree (MT), Evolutionary Polynomial Regression (EPR), Multivariate Adaptive Regression Spline (MARS), and Gene-Expression Programming (GEP). From relevant literature, 125 data series were employed to provide empirical relationships for scour depth predictions. The raw variables were related to bed sediment, pile configuration, pile geometry, approaching flow, and wave characteristics. Non-dimensional parameters have been obtained through the Buckingham theorem in order to control local scour depth. In this way, the ratio of spacing between piles to pile diameter (G/D), sediment number (Ns), pile arrangement number (ratio of the number of piles parallel to the flow; m; to the number of piles normal to the flow; n), Shields parameter (θ), and Keulegan-Carpenter (KC) were considered. From the training and testing stages of Artificial Intelligence Models (AIMs), it was found that regression equation given by MARS model provided better performance (e.g., Correlation Coefficient [R] = 0.9297, Root Mean Square Error [RMSE] = 0.3489, and Scatter Index [SI] = 0.2765) than other AI models’ efficiency. Additionally, the performance of AI models was assessed for various ranges of dimensionless parameters (i.e., G/D, Ns, and θ) versus KC variation. MARS model had the best performance for G/D = 0–1 and KC < 10 (R = 0.9917 and RMSE = 0.2198) and 0.4≤θ < 0.5 and 15 < KC < 25 whereas EPR model had promising efficiency in the its highest level for Ns = 1–3 and 10 < KC ≤ 15 (R = 0.9941 and RMSE = 0.0771) than other AI models. For the sensitivity analysis, the mutation rate and number of chromosomes are examined for the GEP model, the K-fold number and number of parameters for the MARS model, and the number of algebraic terms and number of chromosomes for the EPR model. Furthermore, ranking analysis of AI models indicated that MARS model had the best performance (Ranking Performance Index [RPI] = 0.7348) and followed by GEP (0.3412), M5MT (0.2999), and EPR (0.2848).

Prediction of local scour depth around bridge piers: modelling based on machine learning approaches

Scouring is a threat to the foundations of bridges, and it frequently results in bridge collapse. Recently, machine-learning approaches have become quite popular in solving hydraulic and hydrologic problems. The present study adopted adaptive neuro-fuzzy inference systems (ANFIS) and gene expression programming (GEP) to model bridge pier scour depth. A temporal scour depth relationship has been proposed based on ANFIS and GEP approaches. In this paper, 500 data sets have been used to model the temporal scour depth using ANFIS and GEP models, in which 80% (400) data sets for training and 20% (100) datasets for testing purposes are used to develop the model. The performance of the GEP model is validated by comparing the results of the developed ANFIS model and other existing empirical equations. It is found that the GEP-based scour depth prediction model is effective in training and validation. The present GEP model accurately predicts the scour depth with a mean absolute percentage error (MAPE) value of less than 12% and an R2 value greater than 0.85. Thus, the GEP model can be utilized to predict scour depth around the bridge pier for unsteady flow conditions.