Decision making in engineering prediction systems

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Show simple item record Yasarer, Hakan 2013-08-14T13:25:16Z 2013-08-14T13:25:16Z 2013-08-14
dc.description.abstract Access to databases after the digital revolutions has become easier because large databases are progressively available. Knowledge discovery in these databases via intelligent data analysis technology is a relatively young and interdisciplinary field. In engineering applications, there is a demand for turning low-level data-based knowledge into a high-level type knowledge via the use of various data analysis methods. The main reason for this demand is that collecting and analyzing databases can be expensive and time consuming. In cases where experimental or empirical data are already available, prediction models can be used to characterize the desired engineering phenomena and/or eliminate unnecessary future experiments and their associated costs. Phenomena characterization, based on available databases, has been utilized via Artificial Neural Networks (ANNs) for more than two decades. However, there is a need to introduce new paradigms to improve the reliability of the available ANN models and optimize their predictions through a hybrid decision system. In this study, a new set of ANN modeling approaches/paradigms along with a new method to tackle partially missing data (Query method) are introduced for this purpose. The potential use of these methods via a hybrid decision making system is examined by utilizing seven available databases which are obtained from civil engineering applications. Overall, the new proposed approaches have shown notable prediction accuracy improvements on the seven databases in terms of quantified statistical accuracy measures. The proposed new methods are capable in effectively characterizing the general behavior of a specific engineering/scientific phenomenon and can be collectively used to optimize predictions with a reasonable degree of accuracy. The utilization of the proposed hybrid decision making system (HDMS) via an Excel-based environment can easily be utilized by the end user, to any available data-rich database, without the need for any excessive type of training. en_US
dc.description.sponsorship University Transportation Center, Department of Civil Engineering en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Decision making en_US
dc.subject Artificial neural networks en_US
dc.subject Auto-associative networks en_US
dc.subject Feedback ANN network en_US
dc.subject Partially missing datasets en_US
dc.subject New approaches to artificial neural network en_US
dc.title Decision making in engineering prediction systems en_US
dc.type Dissertation en_US Doctor of Philosophy en_US
dc.description.level Doctoral en_US
dc.description.department Department of Civil Engineering en_US
dc.description.advisor Yacoub M. Najjar en_US
dc.subject.umi Civil Engineering (0543) en_US 2013 en_US August en_US

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