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Using Artificial Neural Network to Predict the Restraint in Concrete Culvert at Early Age
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Construction Engineering.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Construction Engineering.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Construction Engineering.ORCID iD: 0000-0002-3459-2855
2015 (English)In: Structural Engineering International, ISSN 1016-8664, E-ISSN 1683-0350, Vol. 25, no 3, p. 258-265Article in journal (Refereed) Published
Abstract [en]

Estimation of restraint is very important for accurate prediction of the risk of concrete cracking at early age. The present study predicts the restraint in 324 walls and 972 roofs for a concrete culvert. A parametric study included the thickness and width of the roofs, thickness and height of the walls, thickness and width of the slab, and length of the structures. Each parameter increased or decreased the restraint in the walls and the roofs. The calculation of the restraint was done elastically by the finite-element method (FE). The results were used by an artificial neural network (ANN) tool, where firstly an influential percentage was investigated as input parameters on the restraint prediction. Equations have been derived by the ANN model to calculate the restraint in the walls and the roofs. It was then used in an Excel sheet to calculate the restraint and compare the result with the result from the finite-element calculations giving high accuracy between the ANN model and the FE calculations

Place, publisher, year, edition, pages
2015. Vol. 25, no 3, p. 258-265
National Category
Infrastructure Engineering
Research subject
Structural Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-13425DOI: 10.2749/101686614X14043795570570ISI: 000370775600003Scopus ID: 2-s2.0-84941962665Local ID: ca62ce25-6bf8-45cf-8228-03e331ee8dfeOAI: oai:DiVA.org:ltu-13425DiVA, id: diva2:986378
Note
Validerad; 2015; Nivå 2; 20150316 (majali)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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Al-Gburi, MajidJonasson, Jan-ErikNilsson, Martin

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