Neural Network Ensemble-based Parameter Sensitivity Analysis: Illustrated on Civil Engineering Systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F15%3APU120349" target="_blank" >RIV/00216305:26110/15:PU120349 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/s00521-015-2132-4" target="_blank" >http://dx.doi.org/10.1007/s00521-015-2132-4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00521-015-2132-4" target="_blank" >10.1007/s00521-015-2132-4</a>
Alternative languages
Result language
angličtina
Original language name
Neural Network Ensemble-based Parameter Sensitivity Analysis: Illustrated on Civil Engineering Systems
Original language description
The use of artificial neural networks for parameter sensitivity analysis in civil engineering systems is an emerging research focus of increased interest. Existing methods are generally based on a single neural network, but are inadequate as a basis for parameter sensitivity analysis because of the instability of a single neural network. To address this deficiency, this study develops a neural network ensemble-based parameter sensitivity analysis paradigm. This paradigm features use of a set of preselected superior neural networks to make decisions about parameter sensitivity by synthesizing sensitivity analysis results of individual neural networks. The proposed paradigm is employed to address two classic civil engineering problems: (1) identification of critical parameters in the fracture failure of notched concrete beams and (2) recognition of the most significant parameters in the lateral deformation of deep foundation pits. The results show that tensile strength and modulus of elasticity are the critical parameters in the fracture failure of the notched concrete beam, and elasticity modulus of soil, Poisson’s ratio and soil cohesion are the most significant influential factors in the lateral deformation of the deep foundation pit. The proposed method provides a common paradigm for analysing the sensitivity of influential parameters, shedding light on the underlying mechanisms of civil engineering systems.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20102 - Construction engineering, Municipal and structural engineering
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
NEURAL COMPUTING & APPLICATIONS
ISSN
0941-0643
e-ISSN
1433-3058
Volume of the periodical
28
Issue of the periodical within the volume
7
Country of publishing house
GB - UNITED KINGDOM
Number of pages
7
Pages from-to
1583-1590
UT code for WoS article
000404928900003
EID of the result in the Scopus database
2-s2.0-84949945018