Mr. Xiang Peng Profile

Xiang Peng

at Univ. of British Columbia Okanagan

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Publications (2)

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Proceedings Article | 1 April 2019 Presentation + Paper

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

Uchenna Anyaoha, Xiang Peng, Zheng Liu

Proceedings Volume 10971, 109710I (2019) https://doi.org/10.1117/12.2518279

KEYWORDS: Data modeling, Performance modeling, Statistical modeling, Error analysis, Machine learning, Artificial neural networks, Reconstruction algorithms, Cements, Neural networks

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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.

Proceedings Article | 27 March 2018 Presentation + Paper

Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods

Fang Shi, Xiang Peng, Huan Liu, Yafei Hu, Zheng Liu, Eric Li

Proceedings Volume 10602, 1060207 (2018) https://doi.org/10.1117/12.2300812

KEYWORDS: Sensors, Feature selection, Data modeling, Performance modeling, Machine learning, Data analysis

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Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.

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