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oai:www.ideals.illinois.edu:2142/83429 2023-07-11 col_2142_5131 col_2142_14770 com_2142_5130 com_2142_3518 com_2142_234

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997. Ghaboussi, J. D. Pecknold Zhang, Mingfu Michael 2015-09-25T21:04:50Z 2015-09-25T21:04:50Z 10000-01-01 1997 1997 A nested modular neural network structure is introduced in this study and applied to model uniaxial concrete behavior under cyclic loading and biaxial concrete behavior under monotonic loading and unloading. The results show that the nested modular neural network structure is more flexible and efficient to model path-dependent material behavior than the fully-connected internal neural network structure. Made available in DSpace on 2015-09-25T21:04:50Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 9717351.pdf: 8689102 bytes, checksum: e3b52852710929245c71a4f53c82d907 (MD5) Previous issue date: 1997 Embargo set by: Seth Robbins for item 84710 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 157 p. http://hdl.handle.net/2142/83429 (MiAaPQ)AAI9717351 eng Artificial Intelligence Neural Network Material Models Determined From Structural Tests text Civil Engineering Civil Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.