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dc.contributor.authorJan, JCen_US
dc.contributor.authorHung, SLen_US
dc.contributor.authorChi, SYen_US
dc.contributor.authorChern, JCen_US
dc.date.accessioned2014-12-08T15:43:00Z-
dc.date.available2014-12-08T15:43:00Z-
dc.date.issued2002-01-01en_US
dc.identifier.issn0887-3801en_US
dc.identifier.urihttp://dx.doi.org/10.1061/(ASCE)0887-3801(2002)16:1(59)en_US
dc.identifier.urihttp://hdl.handle.net/11536/29122-
dc.description.abstractDiaphragm wall deflection is an important field measurement in deep excavation. The monitoring data are applied to evaluate the construction performance to avoid a supporting system failure or damages incurred to adjacent structures. Despite the numerous case histories of construction projects and several forecasting methods, no method accurately forecasts the performance of construction due to the complicated geotechnical and construction factors affecting the behavior of the diaphragm wall. This work predicts the diaphragm wall deflection by using the adaptive limited memory-Broyden-Fletcher-Goldfarb-Shanno supervised neural network. Eighteen case histories of deep excavations with four to seven excavation stages are selected for training and verification. In addition, the knowledge representation adopts measured wall deflections of previous excavation stages as inputs to the network. Doing so substantially reduces the importance of soil parameters, which are often extremely fluctuating and difficult to assess. Simulation results indicate that the artificial neural network can reasonably predict the magnitude, as well as the location, of maximum deflection of the diaphragm wall.en_US
dc.language.isoen_USen_US
dc.subjectneural networksen_US
dc.subjectexcavationen_US
dc.subjectgeotechnical engineeringen_US
dc.subjectsensitivity analysisen_US
dc.subjectalgorithmen_US
dc.subjectdiaphragm wallen_US
dc.titleNeural network forecast model in deep excavationen_US
dc.typeArticleen_US
dc.identifier.doi10.1061/(ASCE)0887-3801(2002)16:1(59)en_US
dc.identifier.journalJOURNAL OF COMPUTING IN CIVIL ENGINEERINGen_US
dc.citation.volume16en_US
dc.citation.issue1en_US
dc.citation.spage59en_US
dc.citation.epage65en_US
dc.contributor.department土木工程學系zh_TW
dc.contributor.departmentDepartment of Civil Engineeringen_US
dc.identifier.wosnumberWOS:000173309800006-
dc.citation.woscount21-
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