Validity of artificial neural modeling to estimate time-dependent deflection of reinforced concrete beams

Autor:	Faiq M. S. Al-Zwainy, Rana I. K. Zaki, Atheer Mahmood Al-saadi, Huda F. Ibraheem
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	time-dependent deflection beams reinforced concrete statistical analysis artificial neural networks variability parametric study Engineering (General). Civil engineering (General) TA1-2040
Zdroj:	Cogent Engineering, Vol 5, Iss 1 (2018)
Druh dokumentu:	article
ISSN:	2331-1916 23311916
DOI:	10.1080/23311916.2018.1477485
Popis:	The architecture and weights of an artificial neural network model that predicts time-dependent deflection have been developed and optimized. To satisfy the serviceability limit states, a concrete structure must be serviceable and perform its intended function throughout its working life. Excessive deflection should not impair the function of the structure or be aesthetically unacceptable. Cracks should not be unsightly or wide enough to lead to durability problems. Design for the serviceability limit states involves making reliable predictions of the instantaneous and time-dependent deflection of reinforced concrete beams. This is complicated by the nonlinear behavior of concrete caused mainly by cracking, tension stiffening, creep, and shrinkage. This paper provides a statistical approach for predicting the time-dependent deflection of reinforced concrete beams at service loads and outlines a validity of the proposed method in comparison with the American Concrete Institute (ACI) method.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/88703c6034a945feb58a77bef2ca4647 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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