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Developing ensemble machine learning for estimating and parametrically assessing the moment capacity of ferrocement members

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Abstract

Currently, ferrocement members are being increasingly used in civil engineering projects. However, the literature still lacks investigations on the potential of using machine learning techniques in predicting the properties and behavior of such members. This study fills the gap in the literature by providing a detailed study that investigates the application of ensemble machine learning approaches in estimating and parametrically assessing the moment capacity of ferrocement members. Additionally, a notable contribution of this study is integrating feature importance and partial dependence analysis. These analytical methods allow for a detailed parametric assessment, aiming to understand the influence of each parameter on the moment capacity. As part of the study, several key input parameters are considered through utilizing a comprehensive database derived from tests on ferrocement. These parameters include the width and depth of specimens, cube compressive strength of mortar, and wire mesh’s tensile strength and volume fraction. The ensemble machine learning approach offers potential advantages in terms of accuracy and reliability over traditional methods. This research underscores the importance of advanced predictive tools in civil engineering and their potential to enhance the understanding and prediction of moment capacity in ferrocement members. The findings from this study can serve as a foundation for future research, aiming to further refine and optimize the prediction models for various construction materials and methods.

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Data availability

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Faculty of Engineering Technology, Department of Civil Engineering, Al-Balqa Applied University, Amman, Jordan

    Jamal Al Adwan & Yazan Alzubi

  2. Faculty of Engineering, Civil Engineering Department, Al-Balqa Applied University, Al Salt, 191117, Jordan

    Jafar Al Thawabteh

Authors
  1. Jamal Al Adwan
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  2. Jafar Al Thawabteh
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  3. Yazan Alzubi
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Contributions

The authors confirm their contribution to the paper as follows: study conception and design: J.AA., J. AT, and Y. A.; data collection: J.AA. and J. AT; analysis and interpretation of results: J.AA., J. AT, and Y. A.; manuscript preparation: J.AA., J. AT, and Y. A. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Yazan Alzubi.

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Al Adwan, J., Al Thawabteh, J. & Alzubi, Y. Developing ensemble machine learning for estimating and parametrically assessing the moment capacity of ferrocement members. Asian J Civ Eng (2024). https://doi.org/10.1007/s42107-024-01012-0

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