Koo, C, Jin, R, Li, B et al. (2 more authors) (2017) Case-based reasoning approach to estimating the strength of sustainable concrete. Computers and Concrete, 20 (6). pp. 645-654. ISSN 1598-8198
Abstract
Continuing from previous studies of sustainable concrete containing environmentally friendly materials and existing modeling approach to predicting concrete properties, this study developed an estimation methodology to predicting the strength of sustainable concrete using an advanced case-based reasoning approach. It was conducted in two steps: (i) establishment of a case database and (ii) development of an advanced case-based reasoning model. Through the experimental studies, a total of 144 observations for concrete compressive strength and tensile strength were established to develop the estimation model. As a result, the prediction accuracy of the A-CBR model (i.e., 95.214% for compressive strength and 92.448% for tensile strength) performed superior to other conventional methodologies (e.g., basic case-based reasoning and artificial neural network models). The developed methodology provides an alternative approach in predicting concrete properties and could be further extended to the future research area in durability of sustainable concrete.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2017 Techno Press. This is an author produced version of an article published in Earthquakes and Structures. Uploaded with permission from the publisher. |
Keywords: | sustainable concrete; advanced case-based reasoning; environmentally friendly concrete materials; concrete mixture design; concrete strength prediction; optimization process |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > SWJTU Joint School (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Dec 2017 16:24 |
Last Modified: | 09 Apr 2019 16:05 |
Status: | Published |
Publisher: | Techno Press |
Identification Number: | 10.12989/cac.2017.20.6.645 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:124745 |