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Genetic programming in civil engineering: advent, applications and future trends

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Abstract

Over the past two decades, machine learning has been gaining significant attention for solving complex engineering problems. Genetic programing (GP) is an advanced framework that can be used for a variety of machine learning tasks. GP searches a program space instead of a data space without a need to pre-defined models. This method generates transparent solutions that can be easily deployed for practical civil engineering applications. GP is establishing itself as a robust intelligent technique to solve complicated civil engineering problems. This paper provides a review of the GP technique and its applications in the civil engineering arena over the last decade. We discuss the features of GP and its variants followed by their potential for solving various civil engineering problems. We finally envision the potential research avenues and emerging trends for the application of GP in civil engineering.

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Acknowledgements

Amir H. Alavi acknowledges the startup fund from the Swanson School of Engineering at the University of Pittsburgh. This study is supported in part by the Fundamental Research Funds for the Central Universities, China (2020-KYY-529112-0002). Pengcheng Jiao acknowledges the Startup Fund of the Hundred Talent Program at the Zhejiang University, China.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA

    Qianyun Zhang, Kaveh Barri & Amir H. Alavi

  2. Institute of Port, Coastal and Offshore Engineering, Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, China

    Pengcheng Jiao

  3. Engineering Research Center of Oceanic Sensing Technology and Equipment, Zhejiang University, Ministry of Education, Zhoushan, 316021, China

    Pengcheng Jiao

  4. Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI, USA

    Hadi Salehi

  5. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

    Amir H. Alavi

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Zhang, Q., Barri, K., Jiao, P. et al. Genetic programming in civil engineering: advent, applications and future trends. Artif Intell Rev 54, 1863–1885 (2021). https://doi.org/10.1007/s10462-020-09894-7

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