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Concrete performance prediction using boosting smooth transition regression trees (BooST)
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Paper Abstract

The compressive strength of concrete structure is always influenced by the composition of varied materials, casting process, and curing period, etc. Among these variables, an optimal mix of different materials will achieve better structural compressive strength. Thus, understanding the non-linearity of concrete and its variables is paramount for improving and predicting the performance of concrete structures. Due to the expensive and time-consuming laboratory analysis, the use of post-processing and data analysis provides an excellent opportunity to explore and predict optimal models for concrete compressive strength performance. However, given the inadequacy of traditional regression models and other analytic techniques in modeling non-linear regression problems, there is still a need to achieve a better predictive model with minimal errors as well as the capability to estimate partial effects of characteristics on response variables. In this study, a predictive analysis was carried out to investigate the performance of concrete compressive strength at 28 days with a new machine learning model called boosting smooth transition regression trees (BooST). It is observed from the experimental results that the BooST model provides a better prediction accuracy in comparison with the state-of-the-art techniques used for concrete compressive strength prediction. Thus, there is a great potential to apply the BooST model for predicting the compressive strength of concrete in practice.

Paper Details

Date Published: 1 April 2019
PDF: 9 pages
Proc. SPIE 10971, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII, 109710I (1 April 2019); doi: 10.1117/12.2518279
Show Author Affiliations
Uchenna Anyaoha, The Univ. of British Columbia Okanagan (Canada)
Xiang Peng, The Univ. of British Columbia Okanagan (Canada)
Zheng Liu, The Univ. of British Columbia Okanagan (Canada)


Published in SPIE Proceedings Vol. 10971:
Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII
Andrew L. Gyekenyesi, Editor(s)

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