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Hierarchical order of influence of mix variables affecting compressive strength of sustainable concrete containing fly ash, copper slag, silica fume, and fibres.

ABSTRACT: Experiments have been conducted to study the effect of addition of fly ash, copper slag, and steel and polypropylene fibres on compressive strength of concrete and to determine the hierarchical order of influence of the mix variables in affecting the strength using cluster analysis experimentally. While fly ash and copper slag are used for partial replacement of cement and fine aggregate, respectively, defined quantities of steel and polypropylene fibres were added to the mixes. It is found from the experimental study that, in general, irrespective of the presence or absence of fibres, (i) for a given copper slag-fine aggregate ratio, increase in fly ash-cement ratio the concrete strength decreases and with the increase in copper slag-sand ratio also the rate of strength decrease and (ii) for a given fly ash-cement ratio, increase in copper slag-fine aggregate ratio increases the strength of the concrete. From the cluster analysis, it is found that the quantities of coarse and fine aggregate present have high influence in affecting the strength. It is also observed that the quantities of fly ash and copper slag used as substitutes have equal "influence" in affecting the strength. Marginal effect of addition of fibres in the compression strength of concrete is also revealed by the cluster analysis.

SUBMITTER: Natarajan S

PROVIDER: S-EPMC3951050 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Project description:The emission of greenhouse gases and natural-resource depletion caused by the production of ordinary Portland cement (OPC) have a detrimental effect on the environment. Thus, an alternative means is required to produce eco-friendly concrete such as geopolymer concrete (GPC). However, GPC has a complex cementitious matrix and an ambiguous mix design. Aside from that, the composition and proportions of materials utilized may have an impact on the compressive strength. Similarly, the use of robust and efficient machine-learning (ML) approaches is now required to forecast the strength of such a composite cementitious matrix. As a result, this study anticipated the compressive strength of GPC with waste resources using ensemble and non-ensemble ML algorithms. This was accomplished through the use of Anaconda (Python). To build a strong ensemble learner by integrating weak learners, adaptive boosting, random forest (RF), and ensemble learner bagging were employed. Furthermore, ensemble learners were utilized on non-ensemble or weak learners, such as decision trees (DT) and support vector machines (SVM) via regression. The data encompassed 156 statistical samples in which nine variables, namely superplasticizer (kg/m3), fly ash (kg/m3), ground granulated blast-furnace slag (GGBS), temperature (°C), coarse and fine aggregate (kg/m3), sodium silicate (Na2SiO3), and sodium hydroxide (NaOH), were chosen to anticipate the results. Exploring it in depth, twenty sub-models with ensemble boosting and bagging approaches were trained, and tuning was performed to achieve the highest possible coefficient of determination (R2). Moreover, cross K-Fold validation analysis and statistical checks were performed via indicators for the evaluation of the models. The result revealed that ensemble approaches yielded robust performance compared to non-ensemble algorithms. Generally, an ensemble learner with the RF and bagging approach on a DT yielded robust performance by achieving a better R2 as 0.93, and with the lowest statistical errors. The communal model in artificial-intelligence analysis, on average, improved the accuracy of the model.

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Hierarchical order of influence of mix variables affecting compressive strength of sustainable concrete containing fly ash, copper slag, silica fume, and fibres.

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