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dc.contributor.advisorAssaleh, Khaled
dc.contributor.advisorAlHamaydeh, Mohammad
dc.contributor.authorChoudhary, Ibrahim Javed
dc.date.accessioned2013-04-22T10:58:12Z
dc.date.available2013-04-22T10:58:12Z
dc.date.issued2013-03
dc.identifier.other35.232-2013.13
dc.identifier.urihttp://hdl.handle.net/11073/5840
dc.descriptionA Master of Science thesis in Mechatronics Engineering by Ibrahim Javed Choudhary entitled, "Artificial Intelligence-Based Modeling of the Nonlinear Behavior of Buckling-Restrained Braces," submitted in March 2013. Thesis advisor is Dr. Khaled Assaleh and co-advisor is Dr. Mohammad AlHamayde. Available are both soft and hard copies of the thesis.en_US
dc.description.abstractThe present research attempts to investigate and compare various artificial intelligence techniques to model the dynamic nonlinear behavior of Buckling Restrained Braces (BRBs). The various intelligent models are developed using normalized time-delayed inputs and outputs to predict normalized brace forces during load reversals. The values of brace forces are denormalized via an auxiliary intelligent (MLP) model. The training and testing of the proposed models are performed using experimental data from BRB specimens tested at the Pacific Earthquake Engineering Research (PEER) Center, University of California, Berkley. Experimental data from one specimen is used in the model development (training) stage. In addition, three sets of data are used to test the model's learning and generalizing abilities. Brace extensions are used as the network input to estimate the resulting brace forces in a longitudinal direction only. The network performance with different parameters is evaluated in order to arrive at an optimized architecture that best models the phenomenon. The nonlinear hysteretic behavior predicted by the majority of the employed models shows excellent agreement with the experimental results for the training sample. The generalization and prediction capability of the several proposed models is further demonstrated by predicting the hysteretic behavior of the testing samples with noticeable and vivid accuracy. The presented models represent a powerful tool for virtually testing BRB specimens. Such a tool supplements the traditionally available experimental tools for BRB performance investigation. A comparison on the basis of RMSE and Coefficient of Determination (R^2) is carried out to quantify and judge the performance of each implemented model. Further, the estimated peak response quantities and the energy dissipation during hysteretic cycles are also evaluated for precise comparison. The developed modeling techniques facilitate the BRB design and performance investigation processes by minimizing the need for, and extent of, experimental testing. Keywords: Buckling-Restrained Brace, Artificial Neural Network, TDNN, NARX, GMM, ANFIS, Polynomial Classifier.en_US
dc.description.sponsorshipCollege of Engineeringen_US
dc.description.sponsorshipMultidisciplinary Programsen_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesMaster of Science in Mechatronics Engineering (MSMTR)en_US
dc.subjectbuckling-restrained bracesen_US
dc.subjectartificial neural networken_US
dc.subjectTDNNen_US
dc.subjectNARXen_US
dc.subjectGMMen_US
dc.subjectANFISen_US
dc.subjectpolynomial classifieren_US
dc.subject.lcshArtificial intelligenceen_US
dc.subject.lcshMathematical modelsen_US
dc.subject.lcshBuckling (Mechanics)en_US
dc.subject.lcshBuilding, Iron and steelen_US
dc.subject.lcshDesign and constructionen_US
dc.subject.lcshSteel framing (Building)en_US
dc.titleArtificial Intelligence-Based Modeling of the Nonlinear Behavior of Buckling-Restrained Bracesen_US
dc.typeThesisen_US


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