@article{Zhao2026, 
author = {Xuefeng Zhao and Danglong Luo and Yunfei Peng and Yanfei Luo and Taiyuan Kang and Zhenhua Liu and Sen Lu and Shanshan Yang},
title = {MR-based dynamic optimization and simulation method for component-level construction of prefabricated buildings},
year = {2026},
journal = {Journal of Intelligent Construction},
volume = {4},
number = {1},
pages = {9180109},
keywords = {mixed reality, on-site application, prefabricated buildings, construction plan optimization},
url = {https://www.sciopen.com/article/10.26599/JIC.2026.9180109},
doi = {10.26599/JIC.2026.9180109},
abstract = {To address the efficiency bottlenecks in multi-component collaborative construction of prefabricated buildings and the inadequacy of traditional manual scheduling in responding to dynamic on-site conditions, this study proposes an integrated optimization framework combining genetic algorithms (GAs) and mixed reality (MR). By developing a genetic algorithm-based optimization model, the automated component-level scheduling under multi-constraint conditions is achieved. The system incorporates a dynamic “perception–decision–optimization” loop to enhance schedule robustness against on-site disruptions. Innovatively integrating mixed reality technology, we establish a cyber-physical simulation system enabling full-scale component visualization through three-dimensional (3D) spatial mapping and real-time registration. Validated through a Beijing prefabricated building project, the framework demonstrates 27.28% efficiency improvement over conventional methods by achieving millimeter-level spatial alignment between digital plans and physical environments. Key innovations include: (1) a dual-driven optimization mechanism combining algorithmic scheduling with MR-enhanced verification, (2) an in-situ visualization of construction sequences through holographic component projection, and (3) a closed-loop process reconfiguration capability responsive to field variations. The empirical results confirm the system’s effectiveness in bridging digital-physical gaps in component-level construction management, providing a scalable solution for intelligent prefabrication implementation. This methodology advances construction automation by synergizing computational optimization with spatial computing, offering practical insights for industry 4.0 transformation in modular building projects.}
}