English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In the modelling study, two models are presented by gene expression programming (GEP) for estimation of compressive strength (fc) of self-compacting concrete (SCC) with fly ash (FA). The main difference between two models is the number of heads determined in the development of models. These established models are proposed to predict the fc values by utilizing amount of cement, water, FA, coarse and fine aggregate, and superplasticiser, and age of specimen as input values for SCC mixtures. In establishment of the proposed models, 516 compressive strength values are utilized; these values were obtained from 34 published scientific experimental literatures on SCC with FA. The training and testing sets employed in the creation of models consist of 368 fc results of SCC mixtures. The models are confirmed with remaining 148 fc results of SCC mixtures, which are not employed in training and testing sets. The estimated fc results attained from established models were compared with fc results of experimental studies, and previously proposed artificial neural network (ANN) model. These comparisons and results of statistical evaluation have strongly revealed that results of established models match well with the experimental results, and they are considered very reliable

Modelling of compressive strength of self-compacting concrete containing fly ash by gene expression programming