CEE Seminar: Artificial Neural Networks-Based Modeling
Professor & Chair
Civil Engineering Department
University of Mississippi
Abstract: An artificial neural network (ANN) is a mathematical or computational model that attempts to emulate the structure and/or functional aspects of biological neural networks. The interest in neural networks re-emerged only after the discovery of the error back-propagation scheme. Nowadays, artificial neural networks can mostly be characterized as ‘computational models’ with particular properties such as the ability to predict, adapt, learn, generalize, cluster or organize data. In this presentation, basic aspects of ANNs will be explained via simple numerical examples. Notable applications where ANNs have been used will be highlighted.
Bio: Jacob Najjar is a professor and chair of the Department of Civil Engineering at the University of Mississippi. Prior to joining UM, he was a faculty member for 19 years in the civil engineering department at Kansas State University. He also worked as an assistant professor in the Department of Civil, Mechanical and Environmental Engineering at George Washington University. Najjar received his bachelor's degree in civil engineering from Yarmouk University (Jordan) and his M.S. and Ph.D. degrees from the University of Oklahoma. His research focused mainly on the use of applied information technology via Artificial Neural Networks (ANNs) to model various complex systems. He has been funded by different agencies at the national, regional, state and local levels. His research efforts have yielded over 100 peer-refereed articles and book chapters, 55 conference proceedings and 45 invited technical presentations. His published work has been cited more than 820 times with an H-Index of 15. Najjar has received more than 10 teaching-related awards/recognitions from student organizations, CE departments, Kansas State University and the American Society of Engineering Education.