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An explainable prediction framework for engineering problems: case studies in reinforced concrete members modeling

Amirhessam Tahmassebi (Florida State University, Tallahassee, Florida, USA)
Mehrtash Motamedi (Department of Civil Engineering, The University of British Columbia, Vancouver, Canada)
Amir H. Alavi (Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA) (Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan)
Amir H. Gandomi (Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia)

Engineering Computations

ISSN: 0264-4401

Article publication date: 7 July 2021

Issue publication date: 8 February 2022




Engineering design and operational decisions depend largely on deep understanding of applications that requires assumptions for simplification of the problems in order to find proper solutions. Cutting-edge machine learning algorithms can be used as one of the emerging tools to simplify this process. In this paper, we propose a novel scalable and interpretable machine learning framework to automate this process and fill the current gap.


The essential principles of the proposed pipeline are mainly (1) scalability, (2) interpretibility and (3) robust probabilistic performance across engineering problems. The lack of interpretibility of complex machine learning models prevents their use in various problems including engineering computation assessments. Many consumers of machine learning models would not trust the results if they cannot understand the method. Thus, the SHapley Additive exPlanations (SHAP) approach is employed to interpret the developed machine learning models.


The proposed framework can be applied to a variety of engineering problems including seismic damage assessment of structures. The performance of the proposed framework is investigated using two case studies of failure identification in reinforcement concrete (RC) columns and shear walls. In addition, the reproducibility, reliability and generalizability of the results were validated and the results of the framework were compared to the benchmark studies. The results of the proposed framework outperformed the benchmark results with high statistical significance.


Although, the current study reveals that the geometric input features and reinforcement indices are the most important variables in failure modes detection, better model can be achieved with employing more robust strategies to establish proper database to decrease the errors in some of the failure modes identification.



The authors would like to thank Trace Smith for the careful revision of the final version of the manuscript.

Conflict of Interest: The authors declare that they have no conflict of interest.


Tahmassebi, A., Motamedi, M., Alavi, A.H. and Gandomi, A.H. (2022), "An explainable prediction framework for engineering problems: case studies in reinforced concrete members modeling", Engineering Computations, Vol. 39 No. 2, pp. 609-626.



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