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Strength development in concrete with wood ash blended cement and use of soft computing models to predict strength parameters

In this study, Wood Ash (WA) prepared from the uncontrolled burning of the saw dust is evaluated for its suitability as partial cement replacement in conventional concrete. The saw dust has been acquired from a wood polishing unit. The physical, chemical and mineralogical characteristics of WA is pr...

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Detalles Bibliográficos
Autores principales: Chowdhury, S., Maniar, A., Suganya, O.M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4642169/
https://www.ncbi.nlm.nih.gov/pubmed/26644928
http://dx.doi.org/10.1016/j.jare.2014.08.006
Descripción
Sumario:In this study, Wood Ash (WA) prepared from the uncontrolled burning of the saw dust is evaluated for its suitability as partial cement replacement in conventional concrete. The saw dust has been acquired from a wood polishing unit. The physical, chemical and mineralogical characteristics of WA is presented and analyzed. The strength parameters (compressive strength, split tensile strength and flexural strength) of concrete with blended WA cement are evaluated and studied. Two different water-to-binder ratio (0.4 and 0.45) and five different replacement percentages of WA (5%, 10%, 15%, 18% and 20%) including control specimens for both water-to-cement ratio is considered. Results of compressive strength, split tensile strength and flexural strength showed that the strength properties of concrete mixture decreased marginally with increase in wood ash contents, but strength increased with later age. The XRD test results and chemical analysis of WA showed that it contains amorphous silica and thus can be used as cement replacing material. Through the analysis of results obtained in this study, it was concluded that WA could be blended with cement without adversely affecting the strength properties of concrete. Also using a new statistical theory of the Support Vector Machine (SVM), strength parameters were predicted by developing a suitable model and as a result, the application of soft computing in structural engineering has been successfully presented in this research paper.