{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:34:21Z","timestamp":1791394461216,"version":"4.3.3"},"reference-count":22,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2004,9]]},"abstract":"<jats:p>In earthquake-resistant design of structures, for certain structural configurations and conditions, it is necessary to use accelerograms for dynamic analysis. Accelerograms are also needed to simulate the effects of earthquakes on a building structure in the laboratory. A new method of generating artificial earthquake accelerograms is presented through adroit integration of neural networks and wavelets. A counterpropagation (CPN) neural network model is developed for generating artificial accelerograms from any given design spectrum such as the International Building Code (IBC) design spectrum. Using the IBC design spectrum as network input means an accelerogram may be generated for any geographic location regardless of whether earthquake records exist for that particular location or not. In order to improve the efficiency of the model, the CPN network is modified with the addition of the wavelet transform as a data compression tool to create a new CPN-wavelet network. The proposed CPN-wavelet model is trained using 20 sets of accelerograms and tested with additional five sets of accelerograms available from the U.S. Geological Survey. Given the limited set of training data, the result is quite remarkable.<\/jats:p>","DOI":"10.1142\/s0219691304000524","type":"journal-article","created":{"date-parts":[[2004,8,30]],"date-time":"2004-08-30T11:36:17Z","timestamp":1093865777000},"page":"217-235","source":"Crossref","is-referenced-by-count":28,"title":["A NEURAL NETWORK-WAVELET MODEL FOR GENERATING ARTIFICIAL ACCELEROGRAMS"],"prefix":"10.1142","volume":"02","author":[{"suffix":"Jr.","given":"GENE F.","family":"SIRCA","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"HOJJAT","family":"ADELI","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","volume-title":"Machine Learning \u2014 Neural Networks, Genetic Algorithms, and Fuzzy System","author":"Adeli H.","year":"1995"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2000)126:6(464)"},{"key":"rf3","doi-asserted-by":"crossref","DOI":"10.1201\/9781482267686","volume-title":"Construction Scheduling, Cost Optimization, and Management \u2014 A New Model Based on Neurocomputing and Object Technologies","author":"Adeli H.","year":"2001"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-9445(1995)121:8(1205)"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1201\/9781315214764"},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1111\/0885-9507.00189"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-9473(98)90145-1"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1002\/cpa.3160410705"},{"key":"rf9","first-page":"508","volume":"24","author":"Donoho D. 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