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 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.

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.

scholarly journals Building Java Intelligent Applications Data Mining for Java Type-2 Fuzzy Inference Systems

2015 ◽  
Vol 51 ◽  
pp. 2719-2728 ◽  
Author(s):  
Manuel Castañón-Puga ◽  
Josué Miguel Flores-Parra ◽  
Juan Ramón Castro ◽  
Carelia Gaxiola-Pacheco ◽  
Luis Enrique Palafox-Maestre
Keyword(s):  
Data Mining ◽  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Inference Systems

scholarly journals Combined artificial intelligence modeling for production forecast in a petroleum production field

2019 ◽  
Vol 9 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Marco Antonio Ruiz- Serna ◽  
Guillermo Arturo Alzate- Espinosa ◽  
Andrés Felipe Obando- Montoya ◽  
Hernán Dario Álvarez- Zapata
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Fuzzy Inference ◽  
Fluid Phase ◽  
Gas Production ◽  
Production Model ◽  
Data Imputation ◽  
Production Forecast ◽  
Well Production ◽  
Inference Systems

This paper presents the results about using a methodology that combines two artificial intelligence (AI) models to predict the oil, water and gas production in a Colombian petroleum field. By combining fuzzy logic (FL) and artificial neural networks (ANN) a novelty data mining procedure is implemented, including a data imputation strategy. The FL tool determines the most useful variables or parameters to include into each well production model. ANN and FIS (fuzzy inference systems) predictive models identification is developed after the data mining process. The FIS models are capable to predict specific behaviors, while ANN models are able to forecast an average behavior. The combined use of both tools under few iterative steps, allows to improve forecasting of well behavior until reach a specified accuracy level. The proposed data imputation procedure is the key element to correct false or to complete void positions into operation data used to identify models for a typical oil production field. At the end, two models are obtained for each well product, conforming an interesting tool given the best accurate prediction of fluid phase production.

ITERATIVE STRUCTURE DISCOVERY IN GRAPH-BASED DATA

2005 ◽  
Vol 14 (01n02) ◽  
pp. 101-124 ◽  
Author(s):  
JEFFREY A. COBLE ◽  
RUNU RATHI ◽  
DIANE J. COOK ◽  
LAWRENCE B. HOLDER
Keyword(s):  
Data Mining ◽  
Current Data ◽  
Data Sets ◽  
Minimal Impact ◽  
Dynamic Memory ◽  
Demographic Group ◽  
Data Mining Approach ◽  
Complex Relationships ◽  
Conventional Computer ◽  
Iterative Structure

Much of current data mining research is focused on discovering sets of attributes that discriminate data entities into classes, such as shopping trends for a particular demographic group. In contrast, we are working to develop data mining techniques to discover patterns consisting of complex relationships between entities. Our research is particularly applicable to domains in which the data is event-driven or relationally structured. In this paper we present approaches to address two related challenges; the need to assimilate incremental data updates and the need to mine monolithic datasets. Many realistic problems are continuous in nature and therefore require a data mining approach that can evolve discovered knowledge over time. Similarly, many problems present data sets that are too large to fit into dynamic memory on conventional computer systems. We address incremental data mining by introducing a mechanism for summarizing discoveries from previous data increments so that the globally-best patterns can be computed by mining only the new data increment. To address monolithic datasets we introduce a technique by which these datasets can be partitioned and mined serially with minimal impact on the result quality. We present applications of our work in both the counter-terrorism and bioinformatics domains.

Traffic Flow Prediction with few Data Using Fuzzy Neural Network Method

2014 ◽  
Vol 989-994 ◽  
pp. 2684-2687
Author(s):  
Wei Zhou ◽  
Xiao Xue Wang
Keyword(s):  
Fuzzy Inference ◽  
Large Data ◽  
Flow Speed ◽  
Data Sets ◽  
Learning Approaches ◽  
Traffic Flow Prediction ◽  
Traffic Time Series ◽  
Fuzzy Neural ◽  
Few Data ◽  
Inference Systems

Many machine learning approaches in the field of Artificial Intelligence (AI) have been developed. Most of them rely on using large data sets to build up knowledge. However, the traffic system usually has only few data. In this article, the so-called adaptive neural fuzzy inference systems (ANFIS) is employed to predict the traffic time-series with few data, including flow, speed and occupancy

scholarly journals a\A technique on novel based marching ants colonies clusters for operational big data sets

2018 ◽  
Vol 7 (2.12) ◽  
pp. 184
Author(s):  
Konda Sreenu ◽  
Dr Boddu Raja Srinivasa Reddy
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Everyday Life ◽  
Large Time ◽  
Storage System ◽  
Data Sets ◽  
World Data ◽  
Data Mining Approach ◽  
Output Information ◽  
Aggregate Functions

Computer plays a key role in everywhere world. Data is growing along with the usage of computers. In everyday life we use computer for various purpose and store bulk of information. One or other way we want to retrieve data from the storage system. Retrieving bulk of data information is not a simple thing or it is magic show. Every user wants data in different forms like reports or output information. For doing all this exercises we require one process. Process is nothing but marching ants colonies. Data related databases and tables are collected, trivial data is selected from huge tables and databases, apply aggregate functions on data and output information or reports related to data. Paper focus on how efficiently we can use software for some extent on solving business related problems. Paper may not solve century year’s data but we can achieve something. When century years data it is better to go for data mining approach because it accumulates large time to solve such big problems 

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