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

Uchenna Anyaoha; Xiang Peng; Zheng Liu

doi:10.1117/12.2518279

1 April 2019 Concrete performance prediction using boosting smooth transition regression trees (BooST)

Uchenna Anyaoha, Xiang Peng, Zheng Liu

Proceedings Volume 10971, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII; 109710I (2019) https://doi.org/10.1117/12.2518279
Event: SPIE Smart Structures + Nondestructive Evaluation, 2019, Denver, Colorado, United States

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.

Conference Presentation

Citation Download Citation

Uchenna Anyaoha, Xiang Peng, and Zheng Liu "Concrete performance prediction using boosting smooth transition regression trees (BooST)", Proc. SPIE 10971, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII, 109710I (1 April 2019); https://doi.org/10.1117/12.2518279

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available