Two-layer Adaptive Blockchain-based Supervision model for off-site modular housing production

Xiao Li, Liupengfei Wu, Rui Zhao, Weisheng Lu, Fan Xue

Research output: Journal article publicationJournal articleAcademic researchpeer-review

89 Citations (Scopus)


By manufacturing housing products off-site and assembling on-site, modular construction can significantly improve the housing supply efficiency, particularly for high-density cities. However, off-site modular housing production (OMHP) supervision is currently problematic. The production parties are reluctant to provide detailed private data; Even worse, the submitted operation records can be easily fabricated, tampered with, or hard to trace the responsibility. This study develops an innovative Two-layer Adaptive Blockchain-based Supervision (TABS) model for OMHP. The first layer includes the adaptive private sidechains of participants. The second layer is the main blockchain for communication and ‘trading’ among all participants. Benefitted from the unique adaptive two-layer structure, TABS can avoid tampering with operation records by the main blockchain and drive the participants to publish their operation records promptly without privacy leaks. A system prototype was also developed to evaluate the performance of the TABS model. The results indicated that the TABS model could enhance privacy and reduce storage costs at an acceptable latency level. The findings of this study can pave the avenue for a tamper-proof and privacy-preserving supervision mechanism in the architecture, engineering, and construction industry.

Original languageEnglish
Article number103437
JournalComputers in Industry
Publication statusPublished - Jun 2021


  • Blockchain
  • Modular construction
  • Modular housing production
  • Off-site construction
  • Two-layer adaptive blockchain

ASJC Scopus subject areas

  • General Computer Science
  • General Engineering


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