Modeling the Influence of Environmental Factors on Concrete Evaporation Rate

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Vasileios Papadimitropoulos Panagiotis Tsikas Athanasios Chassiakos
Abstract	Newly poured concrete opposing hot and windy conditions is considerably susceptible to plastic shrinkage cracking. Crack-free concrete structures are essential in ensuring high level of durability and functionality as cracks allow harmful instances or water to penetrate in the concrete resulting in structural damages, e.g. reinforcement corrosion or pressure application on the crack sides due to water freezing effect. Among other factors influencing plastic shrinkage, an important one is the concrete surface humidity evaporation rate. The evaporation rate is currently calculated in practice by using a quite complex Nomograph, a process rather tedious, time consuming and prone to inaccuracies. In response to such limitations, three analytical models for estimating the evaporation rate are developed and evaluated in this paper on the basis of the ACI 305R-10 Nomograph for “Hot Weather Concreting”. In this direction, several methods and techniques are employed including curve fitting via Genetic Algorithm optimization and Artificial Neural Networks techniques. The models are developed and tested upon datasets from two different countries and compared to the results of a previous similar study. The outcomes of this study indicate that such models can effectively re-develop the Nomograph output and estimate the concrete evaporation rate with high accuracy compared to typical curve-fitting statistical models or models from the literature. Among the proposed methods, the optimization via Genetic Algorithms, individually applied at each estimation process step, provides the best fitting result.
Related Links	http://www.jsoftcivil.com/article_117865_8c13d050bea76adfd1246e40f92add8e.pdf
DOI	10.22115/scce.2020.246071.1254
Journal	Journal of Soft Computing in Civil Engineering
Issue Number	4
Volume Number	4
e-ISSN	25882872
Language	English
Publisher	Pouyan Press
Publisher Date	2020-01-01
Publisher Place	Iran
Access Restriction	Open
Subject Keyword	Technology Concrete Evaporation Rate Plastic Shrinkage Hot Weather Concreting Artificial Neural Networks Genetic Algorithms Curve-fitting
Content Type	Text
Resource Type	Article
Subject	Architecture Artificial Intelligence Civil and Structural Engineering Computer Science Applications Building and Construction Engineering