Scour depth prediction at the base of longitudinal walls: a combined experimental, numerical, and field study

2019 ◽  
Vol 20 (2) ◽  
pp. 459-478 ◽  
Author(s):  
A. Khosronejad ◽  
P. Diplas ◽  
D. Angelidis ◽  
Z. Zhang ◽  
N. Heydari ◽  
...  
Keyword(s):  
Field Study ◽  
Scour Depth ◽  
Depth Prediction

Scour Depth Prediction for Asa Dam Bridge, Ilorin, Using Artificial Neural Network

2020 ◽  
Vol 47 ◽  
pp. 53-62
Author(s):  
Samson Olalekan Odeyemi ◽  
Mutiu Adelodun Akinpelu ◽  
Rasheed Abdulwahab ◽  
Kazeem Adeshina Dauda ◽  
Stella Chris-Ukaegbu
Keyword(s):  
Economic Value ◽  
Economic Life ◽  
Scour Depth ◽  
Bridge Scour ◽  
Average Flow Velocity ◽  
Sediment Size ◽  
The North ◽  
Depth Prediction ◽  
Pile Cap ◽  
Artificial Neural

Bridge Scour is the localized loss of the geomaterials around the foundation of a bridge as a result of the movement of water around it. Scour is a great risk to the stability of a bridge’s foundation, thus leading to collapse, loss of lives and setback in a nation’s socio-economic life. Artificial Neural Networks (ANN) are collections of simple, highly connected processing elements that learn according to sets of input parameters and use that to simulate the networks of nerve cells of humans or animal central nervous system. The Asa Dam Bridge, one of the longest bridges in Ilorin, Kwara State, Nigeria, has five (5) spans of 20m each. The bridge connects Ilorin to the Ogbomosho Express way (leading to the western part of the country) and the Eyenkorin-Jebba road (leading to the north). Thus, the bridge has a high economic value. In this research, factors such as flow depth, average flow velocity of the river and median sediment size were investigated to show how they affect the depth of scour around the bridge pile foundation. Data were taken for a period of 48 weeks and ANN was applied to predict and generate a model that shows how these factors relate to the scour depth of the riverbed. The model revealed that the hydraulic parameters and soil grading around the pile cap of Asa River Bridge bears significant influence on the scour depth of its foundation. The model was compared with five (5) other established scour equations.

Multi-lens pier scour monitoring and scour depth prediction

2014 ◽  
Vol 167 (2) ◽  
pp. 88-104 ◽  
Author(s):  
Wen-Yi Chang ◽  
Franco Lin ◽  
Jihn-Sung Lai ◽  
Lung-Cheng Lee ◽  
Whey-Fone Tsai ◽  
...  
Keyword(s):  
Scour Depth ◽  
Pier Scour ◽  
Depth Prediction ◽  
Scour Monitoring

scholarly journals Development of scour around a circular pier and its modelling using genetic algorithm

2020 ◽  
Vol 20 (8) ◽  
pp. 3358-3367
Author(s):  
Manish Pandey ◽  
Mohammad Zakwan ◽  
Mohammad Amir Khan ◽  
Swati Bhave
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Bridge Pier ◽  
Scour Depth ◽  
Clear Water ◽  
Depth Prediction ◽  
Depth Relationship

Abstract This paper deals with generalized scour estimation to investigate maximum scour depth at equilibrium scour condition using experimental data obtained from experiments conducted by the authors along with data of previous researchers. Three hundred experimental data were used to derive the generalized clear water scour relationship around circular a bridge pier by using genetic algorithm (GA) and multiple linear regression (MLR) techniques. The GA-based maximum scour depth relationship showed more precise results than MLR. In addition, the present GA and MLR relationships were compared with some equations developed by earlier researchers. Graphically and statistically, it was observed that the GA and MLR relationships provide better agreement with experimental data as compared to earlier relationships. The present study highlights that the GA approach could be effectively used for estimation of maximum scour depth prediction around the bridge pier.

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 ANFIS-based approach to scour depth prediction at abutments in armored beds

2010 ◽  
Vol 13 (4) ◽  
pp. 699-713 ◽  
Author(s):  
Mohammad Muzzammil ◽  
Javed Alam
Keyword(s):  
Fuzzy Inference ◽  
Accurate Estimation ◽  
Scour Depth ◽  
Bridge Abutments ◽  
Anfis Model ◽  
Inference System ◽  
Depth Prediction ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann ◽  
The Comparative Study

An accurate estimation of the maximum possible scour depth at bridge abutments is of paramount importance in decision-making for the safe abutment foundation depth and also for the degree of scour countermeasures to be implemented against excessive scouring. Most of the scour depth prediction formulae available in the literature have been developed based on the analysis of laboratory and field data using statistical methods such as the regression method (RM). The alternative approaches, such as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), are generally preferred to provide better solutions in cases where the available data is incomplete or ambiguous in nature. In the present study, an attempt has, therefore, been made to develop the ANFIS model for the prediction of scour depth at the bridge abutments embedded in an armored bed and make the comparative study for the performance of ANFIS over RM and ANN in modeling the scour depth. It has been found that the ANFIS model performed best amongst all of these methods. The causative variables in raw form result in a more accurate prediction of the scour depth than that of their grouped form.

scholarly journals ANFIS approach to the scour depth prediction at a bridge abutment

2010 ◽  
Vol 12 (4) ◽  
pp. 474-485 ◽  
Author(s):  
Mohammad Muzzammil
Keyword(s):  
Fuzzy Inference ◽  
Accurate Estimation ◽  
Scour Depth ◽  
Bridge Abutments ◽  
Anfis Model ◽  
Inference System ◽  
Model Studies ◽  
Depth Prediction ◽  
Bridge Abutment ◽  
Conventional Regression

An accurate estimation of the maximum possible scour depth at bridge abutments is of paramount importance in decision-making for the safe abutment foundation depth and also for the degree of scour counter-measure to be implemented against excessive scouring. Despite analysis of innumerable prototype and hydraulic model studies in the past, the scour depth prediction at the bridge abutments has remained inconclusive. This paper presents an alternative to the conventional regression model (RM) in the form of an adaptive network-based fuzzy inference system (ANFIS) modelling. The performance of ANFIS over RM and artificial neural networks (ANNs) is assessed here. It was found that the ANFIS model performed best among of these methods. The causative variables in raw form result in a more accurate prediction of the scour depth than that of their grouped form.

scholarly journals Application of Improved GMDH Models to Predict Local Scour Depth at Complex Bridge Piers

2020 ◽  
Vol 6 (1) ◽  
pp. 69-84 ◽  
Author(s):  
Habibeh Ghodsi ◽  
Mohammad Javad Khanjani
Keyword(s):  
Local Scour ◽  
Bridge Pier ◽  
Bridge Piers ◽  
Group Method ◽  
Scour Depth ◽  
Empirical Equations ◽  
Depth Prediction ◽  
Pile Cap ◽  
Data Points ◽  
Statistical Criteria

Scour depth prediction is a vital issue in bridge pier design. Recently, good progress has been made in the development of artificial intelligence (AI) to predict scour depth around hydraulic structures base such as bridge piers. In this study, two hybrid intelligence models based on combination of group method of data handling (GMDH) with harmony search algorithm (HS) and shuffled complex evolution (SCE) have been developed to predict local scour depth around complex bridge piers using 82 laboratory data measured by authors and  615 data points from published literature. The results were compared to conventional GMDH models with two kinds of transfer functions called GMDH1 and GMDH2. Based upon the pile cap location, data points were divided into three categories. The performance of all utilized models was evaluated by the statistical criteria of R, RMSE, MAPE, BIAS, and SI. Performances of developed models were evaluated by experimental data points collected in laboratory experiments, together with commonly empirical equations. The results showed that GMDH2SCE was the superior model in terms of all statistical criteria in training when the pile cap was above the initial bed level and completely buried pile cap. For a partially-buried pile cap, GMDH1SCE offered the best performance. Among empirical equations, HEC-18 produced relatively good performances for different types of complex piers. This study recommends hybrid GMDH models, as powerful tools in complex bridge pier scour depth prediction.

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