scholarly journals ANFIS-based approach for scour depth prediction at piers in non-uniform sediments

2009 ◽  
Vol 12 (3) ◽  
pp. 303-317 ◽  
Author(s):  
M. Muzzammil ◽  
M. Ayyub
Keyword(s):  
Fuzzy Inference ◽  
Bridge Piers ◽  
Scour Depth ◽  
Fuzzy Inference Systems ◽  
Foundation Design ◽  
Pier Scour ◽  
Depth Prediction ◽  
Alternative Approach ◽  
Inference Systems ◽  
Bridge Pier Scour

An estimation of scour depth is a prerequisite for the efficient foundation design of important hydraulic structures such as bridge piers and abutments. Most of the scour depth prediction formulae available in the literature have been developed based on the analysis of the laboratory/field data using statistical methods such as the regression method (RM). Conventional statistical analysis is generally replaced in many fields of engineering by the alternative approach of artificial neural networks (ANN) and adaptive network-based fuzzy inference systems (ANFIS). These recent techniques have been reported to provide better solutions in cases where the available data is incomplete or ambiguous by nature. An attempt has been made to compare the performance of ANFIS over RM and ANN in modeling the depth of bridge pier scour in non-uniform sediments. It has been found that the ANFIS performed best amongst all these methods.

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

2019 ◽  
Author(s):  
Yousef Hassanzadeh ◽  
Amin Jafari-Bavil-Olyaei ◽  
Mohammad Taghi-Aalami ◽  
Nazila Kardan
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Bridge Pier ◽  
Accurate Estimation ◽  
Scour Depth ◽  
Adaptive Network ◽  
Pier Scour ◽  
Inference System ◽  
Colliding Bodies Optimization ◽  
Bridge Pier Scour

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.

scholarly journals Estimation of scour depth around circular piers: applications of model tree

2014 ◽  
Vol 17 (2) ◽  
pp. 226-238 ◽  
Author(s):  
Amir Etemad-Shahidi ◽  
Lisham Bonakdar ◽  
D.-S. Jeng
Keyword(s):  
Data Mining ◽  
Fuzzy Inference ◽  
Field Measurements ◽  
Bridge Piers ◽  
Scour Depth ◽  
Data Sets ◽  
Data Mining Approach ◽  
Statistical Measures ◽  
Inference Systems ◽  
Empirical Approaches

Scour around bridge piers is one of the main causes of bridge failures and is of great importance for hydraulic engineers and scientists. Prediction of the scour depth around piers is complicated, and accurate results are rarely achieved by the existing models. Recently, data mining approaches such as artificial neural networks and fuzzy inference systems have been applied successfully to predict scour depth around hydraulic structures. In this study, an alternative robust data mining approach was used for the predictions of the scour depth around piers, and the results were compared with those of three empirical approaches. Performances of developed models were tested by experimental data sets collected in laboratory experiments and field measurements, together with existing empirical approaches. Statistical measures indicate that the proposed M5′ model provides a better prediction of scour depth than the empirical approaches.

scholarly journals Prediction of Bridge Pier Scour Depth and Field Scour Depth Monitoring

2018 ◽  
Vol 40 ◽  
pp. 03007
Author(s):  
Fong-Zuo Lee ◽  
Jihn-Sung Lai ◽  
Yuan-Bin Lin ◽  
Kuo-Chun Chang ◽  
Xiaoqin Liu ◽  
...  
Keyword(s):  
Numerical Model ◽  
Real Time ◽  
Earthquake Engineering ◽  
Model Simulation ◽  
Bridge Pier ◽  
Bridge Piers ◽  
Scour Depth ◽  
Pier Scour ◽  
River Bed ◽  
Bridge Pier Scour

In practice, it is a major challenge in real-time simulation and prediction of bridge pier scour depth, especially using 3-D numerical model. The simulation time spend too much to use 3-D numerical model simulation and inefficiently to predict bridge pier scour depth in real-time. With heavy rainfall during flood season in Taiwan, abundant sediment with flash flood from upstream watershed is transported to downstream river reaches and transportation time is limited within one day. The flood flow tends to damage bridge structures and affect channel stabilization in fluvial rivers. In addition, the main factors affecting the erosional depth around bridge piers and river bed stabilization are hydrological and hydrographic characteristics in river basin, the scouring and silting of river bed section near the bridge piers, the bridge geometry and protection works of bridge piers. Therefore, based on the observed rainfall data provided by the Central Weather Bureau and the hydrological conditions provided by the Water Resources Agency during flood event as the boundary condition, we develop an effective simulation system for scour depth of bridge piers. The scour depth at the bridge pier is observed by the National Center for Research on Earthquake Engineering for model calibration. In this study, an innovative scour monitoring system using vibration-based Micro-Electro Mechanical Systems (MEMS) sensors was applied. This vibration-based MEMS sensor was packaged inside a stainless sphere with the proper protection of the full-filled resin, which can measure free vibration signals to detect scouring/deposition processes at the bridge pier. It has demonstrated that the measurement system for monitoring bridge scour depth evolution is quite successful in the field.

scholarly journals Gene-expression programming to predict pier scour depth using laboratory data

2011 ◽  
Vol 14 (3) ◽  
pp. 628-645 ◽  
Author(s):  
Mujahid Khan ◽  
H. Md. Azamathulla ◽  
M. Tufail
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Laboratory Data ◽  
Bridge Pier ◽  
Scour Depth ◽  
Bridge Scour ◽  
Regression Equations ◽  
Pier Scour ◽  
Depth Prediction ◽  
Bridge Pier Scour

Prediction of bridge pier scour depth is essential for safe and economical bridge design. Keeping in mind the complex nature of bridge scour phenomenon, there is a need to properly address the methods and techniques used to predict bridge pier scour. Up to the present, extensive research has been carried out for pier scour depth prediction. Different modeling techniques have been applied to achieve better prediction. This paper presents a new soft computing technique called gene-expression programming (GEP) for pier scour depth prediction using laboratory data. A functional relationship has been established using GEP and its performance is compared with other artificial intelligence (AI)-based techniques such as artificial neural networks (ANNs) and conventional regression-based techniques. Laboratory data containing 529 datasets was divided into calibration and validation sets. The performance of GEP was found to be highly satisfactory and encouraging when compared to regression equations but was slightly inferior to ANN. This slightly inferior performance of GEP compared to ANN is offset by its capability to provide compact and explicit mathematical expression for bridge scour. This advantage of GEP over ANN is the main motivation for this work. The resulting GEP models will add to the existing literature of AI-based inductive models for bridge scour modeling.

Evaluation of Selected Pier-Scour Equations Using Field Data

1996 ◽  
Vol 1523 (1) ◽  
pp. 186-195 ◽  
Author(s):  
Mark N. Landers ◽  
David S. Mueller
Keyword(s):  
Field Measurements ◽  
Bridge Piers ◽  
Critical Ratio ◽  
Scour Depth ◽  
Flow Depth ◽  
Clear Water ◽  
Pier Scour ◽  
Logarithmic Space ◽  
State Highway ◽  
Federal Highway

Field measurements of channel scour at bridges are needed to improve the understanding of scour processes and the ability to accurately predict scour depths. An extensive data base of pier-scour measurements has been developed over the last several years in cooperative studies between state highway departments, the Federal Highway Administration, and the U.S. Geological Survey. Selected scour processes and scour design equations are evaluated using 139 measurements of local scour in live-bed and clear-water conditions. Pier-scour measurements were made at 44 bridges around 90 bridge piers in 12 states. The influence of pier width on scour depth is linear in logarithmic space. The maximum observed ratio of pier width to scour depth is 2.1 for piers aligned to the flow. Flow depth and scour depth were found to have a relation that is linear in logarithmic space and that is not bounded by some critical ratio of flow depth to pier width. Comparisons of computed and observed scour depths indicate that none of the selected equations accurately estimate the depth of scour for all of the measured conditions. Some of the equations performed well as conservative design equations; however, they overpredict many observed scour depths by large amounts. Some equations fit the data well for observed scour depths less than about 3 m (9.8 ft), but significantly underpredict larger observed scour depths.

Sign in / Sign up

Export Citation Format

Share Document