Decentralized artificial intelligence in construction using blockchain

Chengliang Zheng, Xingyu Tao, Liang Dong, Umer Zukaib, Jingyuan Tang, Haohua Zhou, Jack C.P. Cheng, Xiaohui Cui, Zhidong Shen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Alleviating cybersecurity risks associated with centralized AI training and implementation is a burgeoning challenge in the construction industry. This paper addresses two primary questions: (1) What is the knowledge of AI security vulnerability in construction, and (2) How can AI be decentralized using blockchain? To this end, this paper proposes a blockchain-AI integrated framework (BAII), enabling AI to be trained, verified, and applied on a decentralized blockchain. The framework has been successfully validated in an excavator pose recognition scenario, demonstrating acceptable latency and high performance with 95 % accuracy, 94 % precision, and 96 % recall. This research is pivotal for construction managers and IT security professionals, enhancing the reliability and safety of AI applications in construction. The decentralized AI (DAI) approach can also inspire further research into motivating constructors to contribute to AI modeling and training through incentive mechanisms in the blockchain.

Original languageEnglish
Article number105669
JournalAutomation in Construction
Volume166
DOIs
Publication statusPublished - Oct 2024

Keywords

  • Artificial intelligence (AI)
  • Blockchain
  • Decentralized machine learning
  • Excavator pose recognition
  • Smart contract

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction

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