Predicting compressive strength of lightweight foamed concrete using extreme learning machine model

Yaseen, Zaher Mundher and Deo, Ravinesh C. and Hilal, Ameer and Abd, Abbas M. and Bueno, Laura Cornejo and Salcedo-Sanz, Sancho and Nehdi, Moncef L. (2017) Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Advances in Engineering Software. ISSN 0965-9978

Abstract

In this research, a machine learning model namely extreme learning machine (ELM) is proposed to predict the compressive strength of foamed concrete. The potential of the ELM model is validated in comparison with multivariate adaptive regression spline (MARS), M5 Tree models and support vector regression (SVR). The Lightweight foamed concrete is produced via creating a cellular structure in a cementitious matrix during the mixing process, and is widely used in heat insulation, sound attenuation, roofing, tunneling and geotechnical applications. Achieving product consistency and accurate predictability of its performance is key to the success of this technology. In the present study, an experimental database encompassing pertinent data retrieved from several previous studies has been created and utilized to train and validate the ELM, MARS, M5 Tree and SVR machine learning models. The input parameters for the predictive models include the cement content, oven dry density, water-to-binder ratio and foamed volume. The predictive accuracy of the four models has been assessed via several statistical score indicators. The results showed that the proposed ELM model achieved an adequate level of prediction accuracy, improving MARS, M5 Tree and SVR models. Hence, the ELM model could be employed as a reliable and accurate data intelligent approach for predicting the compressive strength of foamed concrete, saving laborious trial batches required to attain the desired product quality.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to ArticleFirst version, in accordance with the copyright policy of the publisher. This research was supported by USQ Academic Development and Outside Program ADOSP 2016 awarded to Dr Ravinesh C Deo. Part of this research was also undertaken at the Chinese Academy of Sciences. Laura Cornejo Bueno was a USQ Visiting Research Student under reciprocal supervision of Dr Ravinesh Deo and Prof. Sancho Salcedo-Sanz (2017).
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 25 Sep 2017 03:01
Last Modified: 27 Apr 2018 02:02
Uncontrolled Keywords: Foamed concrete; Compressive strength; Prediction; ELM MARS; M5 Tree; SVR
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
09 Engineering > 0905 Civil Engineering > 090502 Construction Engineering
09 Engineering > 0905 Civil Engineering > 090503 Construction Materials
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Identification Number or DOI: 10.1016/j.advengsoft.2017.09.004
URI: http://eprints.usq.edu.au/id/eprint/33138

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