A Statistical Modeling Approach to Studying the Effects of Alternative and Waste Materials on Green Concrete Properties

A Statistical Modeling Approach to Studying the Effects of Alternative and Waste Materials on Green Concrete Properties

Bibliographic Details
Main Author: Jin, Ruoyu
Language:English
Published: The Ohio State University / OhioLINK 2013
Subjects:
Civil Engineering
Green concrete
concrete recycling
concrete properties
multivariate regression analysis
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=osu1372854071
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record_format oai_dc
collection NDLTD
language English
sources NDLTD
topic Civil Engineering
Green concrete
concrete recycling
concrete properties
multivariate regression analysis
spellingShingle Civil Engineering
Green concrete
concrete recycling
concrete properties
multivariate regression analysis
Jin, Ruoyu
A Statistical Modeling Approach to Studying the Effects of Alternative and Waste Materials on Green Concrete Properties
author Jin, Ruoyu
author_facet Jin, Ruoyu
author_sort Jin, Ruoyu
title A Statistical Modeling Approach to Studying the Effects of Alternative and Waste Materials on Green Concrete Properties
title_short A Statistical Modeling Approach to Studying the Effects of Alternative and Waste Materials on Green Concrete Properties
title_full A Statistical Modeling Approach to Studying the Effects of Alternative and Waste Materials on Green Concrete Properties
title_fullStr A Statistical Modeling Approach to Studying the Effects of Alternative and Waste Materials on Green Concrete Properties
title_full_unstemmed A Statistical Modeling Approach to Studying the Effects of Alternative and Waste Materials on Green Concrete Properties
title_sort statistical modeling approach to studying the effects of alternative and waste materials on green concrete properties
publisher The Ohio State University / OhioLINK
publishDate 2013
url http://rave.ohiolink.edu/etdc/view?acc_num=osu1372854071
work_keys_str_mv AT jinruoyu astatisticalmodelingapproachtostudyingtheeffectsofalternativeandwastematerialsongreenconcreteproperties
AT jinruoyu statisticalmodelingapproachtostudyingtheeffectsofalternativeandwastematerialsongreenconcreteproperties
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-osu13728540712021-08-03T05:24:22Z A Statistical Modeling Approach to Studying the Effects of Alternative and Waste Materials on Green Concrete Properties Jin, Ruoyu Civil Engineering Green concrete concrete recycling concrete properties multivariate regression analysis The sustainability movement of the construction industry involves the application of environmentally friendly materials and recycling of waste streams. As the most widely consumed construction material worldwide, concrete consumes an astronomical amount of energy and concrete materials (e.g., cement, sand, and aggregates) to produce and becomes waste when it is demolished. In this research, “green” concrete is defined as concrete produced by using alternative or recycled waste materials that can save energy, reduce environmental pollution, reuse waste streams, improve concrete properties, and/or are locally available. These alternative cementitious and aggregate materials are defined as “green” concrete materials. Despite that the various types of “green” concrete materials have been studied previously in academia, there is not sufficient information on how those “green” concrete materials are used in concrete production and how the old concrete is recycled in the U.S. construction industry. This research started with the investigation of the current implementation of “green” concrete and old concrete recycling in the U.S. construction industry using the face-to-face and online questionnaire survey approach. The survey identified the commonly used “green” concrete materials (e.g., fly ash, ground-granulated blast-furnace slag, silica fume, and lightweight aggregate) in the concrete industry and explored the current practice of old concrete recycling. The data collected from questionnaire surveys to industry practitioners links the academia research and industry concern.Based on the feedback collected from the survey, the “green” concrete materials, including the newly emerged Portland limestone cement (PLC), the most commonly used supplementary cementitious material fly ash Class F, and the locally available lightweight aggregate Haydite in Ohio, were selected for the follow-up experimental study. During the experiment, these “green” concrete materials were used to replace conventional materials (i.e., Portland cement and pea gravel) by different percentages. In total 36 batches of concrete with different mixtures were casted. The tested concrete properties include slump, air content, and density of fresh concrete, as well as compressive and tensile strength of hardened concrete at different curing ages. The test results provided the knowledge of how these selected “green” concrete materials affected concrete properties. Through the literature review, this research found that the statistical modeling, as a potential approach to simulating the relationships between concrete properties and variables such as mixture design and curing age, had not been widely applied in the research of “green” concrete. Therefore, this research applied the multivariate regression analysis to predict the “green” concrete properties based on seven independent predicator variables in concrete mixture and curing age. Up to 17 types of regression models were proposed for the simulation, including both linear and non-linear formats. The best-fit model was identified in “green” concrete analysis based on R2, error analysis, analysis of variance, and Durbin-Watson value s. One additional batch of concrete was tested for model validation. The comprehensive list of independent predicator variables enabled the established regression model as a potential tool to predict concrete properties in “green” concrete research and practice. Compared with other methods, this statistical approach is economical and can provide relatively accurate results. 2013-08-30 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1372854071 http://rave.ohiolink.edu/etdc/view?acc_num=osu1372854071 unrestricted This thesis or dissertation is protected by copyright: some rights reserved. It is licensed for use under a Creative Commons license. Specific terms and permissions are available from this document's record in the OhioLINK ETD Center.

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