Prediction Of Scour Depth Around Bridge Piers Using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

Earth\u27s surface is continuously shaped due to the action of geophysical flows. Erosion due to the flow of water in river systems has been identified as a key problem in preserving ecological health but also a threat to our built environment and critical infrastructure, worldwide. As an example, it has been estimated that a major reason for bridge failure is due to scour. Even though the flow past bridge piers has been investigated both experimentally and numerically, and the mechanisms of scouring are relatively understood, there still lacks a tool that can offer fast and reliable predictions. Most of the existing formulas for prediction of bridge pier scour depth are empirical in nature, based on a limited range of data or for piers of specific shape. In this work, the application of a Machine Learning model that has been successfully employed in Water Engineering, namely an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to estimate the scour depth around bridge piers. In particular, various complexity architectures are sequentially built, in order to identify the optimal for scour depth predictions, using appropriate training and validation subsets obtained from the USGS database (and pre-processed to remove incomplete records). The model has five variables, namely the effective pier width (b), the approach velocity (v), the approach depth (y), the mean grain diameter (D50) and the skew to flow. Simulations are conducted with data groups (bed material type, pier type and shape) and different number of input variables, to produce reduced complexity and easily interpretable models. Analysis and comparison of the results indicate that the developed ANFIS model has high accuracy and outstanding generalization ability for prediction of scour parameters. The effective pier width (as opposed to skew to flow) is amongst the most relevant input parameters for the estimation

Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers

An accurate estimation of bridge pier scour has been considered as one of the important parameters in designing of bridges. However, due to the numerous involved parameters and convolution of this phenomenon, many existing approaches cannot predict scour depth with an acceptable accuracy. Obtained results from the empirical relationships show that these relationships have low accuracy in determining the maximum scour depth and they need a high safety factor for many cases, which leads to uneconomic designs of bridges. To cover these disadvantages, three new models are provided to estimate the bridge pier scour using an adaptive network-based fuzzy inference system. The parameters of the system are optimized by using the colliding bodies optimization, enhanced colliding bodies optimization and vibrating particles system methods. To evaluate the efficiency of the proposed methods, their results were compared with those of simple adaptive network-based fuzzy inference system and its improved versions by using the particle swarm optimization and genetic algorithm as well as the empirical equations. Comparison of results showed that the new vibrating particles system based algorithm could find better results than other two ones. In addition, comparison of the results obtained by the proposed methods with those of the empirical relations confirmed the high performance of the new methods

Scour detection with monitoring methods and machine learning algorithms - a critical review

Foundation scour is a widespread reason for the collapse of bridges worldwide. However, assessing bridges is a complex task, which requires a comprehensive understanding of the phenomenon. This literature review first presents recent scour detection techniques and approaches. Direct and indirect monitoring and machine learning algorithm-based studies are investigated in detail in the following sections. The approaches, models, characteristics of data, and other input properties are outlined. The outcomes are given with their advantages and limitations. Finally, assessments are provided at the synthesis of the research.This research was funded by FCT (Portuguese national funding agency for science, research, and technology)/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020 and trough the doctoral Grant 2021.06162.BD. This work has also been partly financed within the European Horizon 2020 Joint Technology Initiative Shift2Rail through contract no. 101012456 (IN2TRACK3)

Group method of data handling to predict scour depth around vertical piles under regular waves

AbstractThis paper presents a new application of the Group Method Of Data Handling (GMDH), to predict pile scour depth exposed to waves. The GMDH network was developed using the Levenberg–Marquardt (LM) method in the training stage for scour prediction. Scour depth due to regular waves was modeled as a function of five dimensionless parameters, including pile Reynolds number, grain Reynolds number, sediment number, Keulegan–Carpenter number, and shields parameter. The testing results of the GMDH-LM were compared with those obtained using the Adaptive Neuro-Fuzzy Inference System (ANFIS), Radial Basis Function-Neural Network (RBF-NN), and empirical equations. In particular, the GMDH-LM provided the most accurate prediction of scour depth compared to other models. Also, the Keulegan–Carpenter number has been determined as the most effective parameter on scour depth through a sensitivity analysis. The GMDH-LM was utilized successfully to investigate the influence of the pile cross section and Keulegan–Carpenter number on scour depth

Prediction of Maximum Scour Depth Using Scaled Down Bridge Model in a Laboratory

Recently, United States faced catastrophic flooding in West Virginia, Texas, Louisiana, Oklahoma, and Arkansas, and the flooding resulted in several bridge failures. Among them, Bridge scour is one of the main causes of many bridge failures, and leads to financial losses as well as loss of life. Since 1960, a lot of scour research has been completed and several estimation methods were already in the hand of hydraulic engineers. However, scouring is still a challenging topic. Currently, the issues of scour are once again rising because the occurrence of extreme weather events are expected to increase in frequency. Furthermore, current practice of scour estimation shows over-prediction and sometimes, under-prediction. One possible reason is adding separate estimates of contraction and local scour when in fact these processes occur simultaneously. Another possible reason is that current scour equations are based on experiments using free-surface flow in idealized-rectangular flumes even though extreme flood event can cause bridge overtopping flow in combination with submerged orifice flow and the resulting scouring depth is site-specific. In this study, experiments were carried out by professor Hong at the hydraulics laboratory in the School of Civil and Environmental Engineering at the Georgia Institute of Technology in a compound shape channel using 1:60 bridge model of the Towaliga River Bridge at Macon, Georgia including river bathymetry in different flow conditions (free, submerged orifice and overtopping flow). Finding a solution regarding maximum scour depth calculation in clear water scour condition using required analysis of experimental results is contributed by myself. Based on the findings from laboratory experiments coupled with widely used empirical scour estimation methods, such as Colorado State University (CSU) pier scour equation, Melville-Sheppard equation and Ambient pier scour method, a comprehensive way of predicting maximum scour depth is suggested which overcomes problem regarding separate estimation of different scour depths. During the analysis, the effect of flow contraction on local scour was evaluated, and the result confirmed the necessity of single scour depth prediction method rather than separate estimation of different scour depths. In addition, an area-average contraction scour depth prediction method using ambient bed elevation around the local scour was also suggested and analyzed by measured flow contraction ratio. Also, the effect of vertical flow contraction and the effect of existence of a pier bent (located close to the abutment) on the maximum scour depth was investigated. The results show that in pressure flow, a combination of lateral and vertical contraction boosted the maximum scour depth. Results from the existence of the pier bent show that the location of maximum scour depth is unbiased on the presence of the pier bent but the amount of maximum scour depth is relatively higher due to the discharge redistribution when the pier bent is absence rather than its presence

Application of GEP, M5-TREE, ANFIS, and MARS for Predicting Scour Depth in Live Bed Conditions around Bridge Piers

This paper presents the use of data-driven models, namely Gene expression programming (GEP), M5 model tree (M5-TREE), Multivariate adaptive regression spline (MARS), and Adaptive neuro-fuzzy inference system (ANFIS) to predict bridge pier scour depth. Only 213 data sets of the live bed conditions from laboratory tests and field data measurements were considered for the present analysis. The gamma test has been performed to determine the ideal input combinations for model development. Five main non-dimensional parameters: Sediment Coarseness ratios, Froude number, flow intensity, gradation coefficient of the bed material, and shape factor, were found to be the vital input parameters for scour depth model development. The results of these 4 data-driven models were compared with the results of nine conventional empirical equations using the performance criterion correlation coefficient (R), root mean squared error (RMSE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (E), and index of agreement (Id) and graphical analysis. Based on values of the performance indices, ANFIS model was selected with R=0.986, RMSE=0.062, MAPE=6.767, E=0.975 and Id=0.987. The results also show the outperformance of ANFIS model over the other selected data driven models and conventional empirical equations. This model can also be applied to the modelling of bridge pier scour in clear water conditions and can provide insight into the efficacy of modelling approaches in hydraulic properties

Estimation of the Local Scour from a Cylindrical Bridge Pier Using a Compilation Wavelet Model and Artificial Neural Network

In the present study, an artificial neural network and its combination with wavelet theory are used as the computational tool to predict the depth of local scouring from the bridge pier. The five variables measured are the pier diameter of the bridge, the critical and the average velocities, the average diameter of the bed aggregates, and the flow depth. In this study, the neural wavelet method is used as a preprocessor. The data was passed through the wavelet filter and then passed to the artificial neural network. Among the various wavelet functions used for preprocessing, the dmey function results in the highest correlation coefficient and the lowest RMSE and is more efficient than other functions. In the wavelet-neural network compilation method, the neural network activator function was replaced by different wavelet functions. The results show that the neural network method with the Polywog4 wavelet activator function with a correlation coefficient of 87% is an improvement of 8.75% compared to the normal neural network model. By performing data filtering by wavelet and using the resulting coefficients in the neural network, the resulting correlation coefficient is 82%, only a 2.5% improvement compared to the normal neural network. By analyzing the results obtained from neural network methods, the wavelet-neural network predicted errors compared to experimental observations were 8.26, 1.56, and 1.24%, respectively. According to the evaluation criteria, combination of the best effective hydraulic parameters, the combination of wavelet function and neural network, and the number of neural network neurons achieved the best results

Scour depth prediction around bridge abutment protected by spur dike using soft computing tools and regression methods

Scour depth around bridge abutment is a crucial parameter to design the protective spur dike. Costly and time consuming experiments make it difficult to evaluate the scour depth in the problems involving scour phenomena. However, soft computing and regression methods may be applied based on the experimental results. In this paper, a set of experiments is performed and a database including 127 records is collected to evaluate the relation between scour depth and five independent variables including abutment length, flow discharge, flow depth, spur dike length and Spur dike distance from abutment to upstream. This paper presents a new application of the multi-layer perceptron neural network (MLP), group method of data handling (GMDH), non-linear regression (NLR) and multiple linear regression (MLR) to predict the scour depth. A sensitivity analysis is also performed to evaluate the influence of each variable on the scour depth. Results indicate that the first three methods are efficient and accurate enough to be applied in practical applications with determination coefficient (R2) above 90%, while, the MLR has shown a poor performance in this paper. It is observed that MLP and GMDH outperform other methods based on the test data. However, explicit equation derived by NLR has a major advantage to be applied in the field applications without skilled operators and computer packages