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Artificial Intelligence in Civil Engineering

Author

Listed:
  • Pengzhen Lu
  • Shengyong Chen
  • Yujun Zheng

Abstract

Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering.

Suggested Citation

  • Pengzhen Lu & Shengyong Chen & Yujun Zheng, 2012. "Artificial Intelligence in Civil Engineering," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-22, December.
    • Handle: RePEc:hin:jnlmpe:145974
    DOI: 10.1155/2012/145974
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    Cited by:

    1. James Ogundiran & Ehsan Asadi & Manuel Gameiro da Silva, 2024. "A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings," Sustainability, MDPI, vol. 16(9), pages 1-30, April.
    2. Ke Wang & Ziyi Ying & Shankha Shubhra Goswami & Yongsheng Yin & Yafei Zhao, 2023. "Investigating the Role of Artificial Intelligence Technologies in the Construction Industry Using a Delphi-ANP-TOPSIS Hybrid MCDM Concept under a Fuzzy Environment," Sustainability, MDPI, vol. 15(15), pages 1-42, August.
    3. Mustufa Haider Abidi & Usama Umer & Muneer Khan Mohammed & Mohamed K. Aboudaif & Hisham Alkhalefah, 2020. "Automated Maintenance Data Classification Using Recurrent Neural Network: Enhancement by Spotted Hyena-Based Whale Optimization," Mathematics, MDPI, vol. 8(11), pages 1-33, November.
    4. Akshita Bassi & Aditya Manchanda & Rajwinder Singh & Mahesh Patel, 2023. "A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 209-238, August.
    5. Wael Korani & Malek Mouhoub, 2021. "Review on Nature-Inspired Algorithms," SN Operations Research Forum, Springer, vol. 2(3), pages 1-26, September.

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