Neural Network Ensemble-based Parameter Sensitivity Analysis: Illustrated on Civil Engineering Systems

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Neural Network Ensemble-based Parameter Sensitivity Analysis: Illustrated on Civil Engineering Systems

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Neural Network Ensemble-based Parameter Sensitivity Analysis: Illustrated on Civil Engineering Systems

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20102 - Construction engineering, Municipal and structural engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

NEURAL COMPUTING & APPLICATIONS

ISSN

0941-0643

e-ISSN

1433-3058

Svazek periodika

28

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

7

Strana od-do

1583-1590

Kód UT WoS článku

000404928900003

EID výsledku v databázi Scopus

2-s2.0-84949945018