Scour Depth at Bridges: Method Including Soil Properties. I: Maximum Scour Depth Prediction

2015 ◽  
Vol 141 (2) ◽  
pp. 04014104 ◽  
Author(s):  
Jean-Louis Briaud
Keyword(s):  
Soil Properties ◽  
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.

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

scholarly journals A GIS-Enabled Approach for Assessing Damage Potential of Levee Systems Based on Underlying Geology and River Morphology

2013 ◽  
Vol 2013 ◽  
pp. 1-20 ◽  
Author(s):  
Mustafa Saadi ◽  
Adda Athanasopoulos-Zekkos
Keyword(s):  
Soil Properties ◽  
Ordinary Kriging ◽  
Spatial Information ◽  
Soil Liquefaction ◽  
Damage Potential ◽  
Depth Prediction ◽  
Damage Indices ◽  
Levee System ◽  
System Risk ◽  
Kriging Approach

Flood protection levee systems are complex, interconnected systems, where failure at one location means failure of the entire system. Levees are formed through various geologic processes and human activities over time and information regarding soil properties is collected only at limited point locations and varies significantly both laterally and with depth. Prediction of levee performance in locations where no soil data is available becomes a limitation for system risk assessment studies. Geographic Information Systems (GIS) are particularly suitable for the complex and efficient management of spatial information, georeferencing capabilities, and geostatistical analysis. A GIS enabled approach for assessing damage potential of levees systems is presented. Spatial variability of soil properties is correlated with regional variables such as distance from nearest river segment, river meandering sinuosity index, and surface geology. A geostatistical ordinary kriging approach is used for developing these correlations. Soil strength parameters of identified levee stratigraphy layers were statistically analyzed using a geostatistical ordinary kriging approach and correlated with preselected regional variables. A levee system in Northern California is used as a pilot study for the proposed approach. Excessive underseepage and loss of freeboard due to soil liquefaction are evaluated as the two damage indices for earthen levees.

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 A PREDICTIVE MODEL FOR SCOUR DEPTH OF COASTAL BUILDING FAILURES DUE TO TSUNAMIS

2017 ◽  
pp. 24
Author(s):  
Mara Jauane Nicholas ◽  
Ravindra Jayaratne ◽  
Takayuki Suzuki ◽  
Tomoya Shibayama
Keyword(s):  
Soil Properties ◽  
Predictive Model ◽  
Great East Japan Earthquake ◽  
Scour Depth ◽  
State University ◽  
Colorado State University ◽  
Tsunami Disaster ◽  
Industrial Sectors ◽  
Proposed Model ◽  
S Model

The 2011 Great East Japan Earthquake and Tsunami was one of the strongest earthquakes which generated a major tsunami in modern history. The tsunami disaster had an estimated cost of 16.9 trillion yen (US$ 217.3 billion) and affected the Coastal buildings, services, infrastructure and industrial sectors. Approximately 61% of damaged cost was from the building sector. A practical predictive scour depth model at seaward face was developed to highlight the scour failure of Coastal buildings in Miyagi, Fukushima and Iwate prefectures affected by the 2011 Great East Japan Earthquake and Tsunami. The predictive model for representative scour depth was developed in terms of various hydraulic, geometrical and soil properties affecting Coastal buildings. An analysis was undertaken to investigate the effectiveness of the authors’ predictive scour model against the existing models. The results of the authors’ proposed model suggested that the tsunami velocity played a significant role on tsunami-induced scour, other scour models such as Tonkin et al.’s model (2003) is reliant on the accuracy of sub models and hydrodynamic forces while the Colorado State University model as modified by Nadal et al. (2010) is reliant on the geometric parameter of the structure.

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