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Prediction of parameters of building structures using regression equations with independent action of variables and constancy of operating factors

Author(s):


Medium: journal article
Language(s): English
Published in: IOP Conference Series: Materials Science and Engineering, , n. 1, v. 1001
Page(s): 012076
DOI: 10.1088/1757-899x/1001/1/012076
Abstract:

In this article, the authors present a method for predicting the parameters of building structures and calculating the residual life using regression equations. The most commonly used regression equations were used: linear, polynomial, power, exponential, exponential, logarithmic, semilogarithmic, hyperbolic and logistic. The authors proposed to use one-factor regression equations, in which the variable is time, and the dependent value is the parameter of the building structure, the changes of which the researcher needs to determine. These equations are named by the author as the basic regression equations. Also, the authors in this article in the future, the basic equations are presented as multifactorial. This is achieved by replacing the coefficients of the main equation with regression equations, which are obtained after a series of tests are performed for various values of the selected input parameters (environmental conditions, material of construction, etc.), regression equations are obtained for each such state, as a result of which there are a number of values for the parameters of the basic regression equations. As a result, a repeated regression analysis is carried out and a regression equation is constructed for the coefficients of the main regression equation, which depends on the value of the specified parameters. Such equations are called secondary by the authors. Examples are given for conditional linear regression, where the author has demonstrated how the coefficients of the main regression equation are replaced and what final form the equation goes to after replacing the coefficients with secondary equations.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1088/1757-899x/1001/1/012076.
  • About this
    data sheet
  • Reference-ID
    10674926
  • Published on:
    02/06/2022
  • Last updated on:
    02/06/2022
 
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