| description abstract | In order to improve the prediction ability of the construction progress of a prefabricated hollow floor, a building information modeling (BIM) reliability prediction method for the construction progress of prefabricated hollow floor based on heterogeneous data fusion is proposed. Considering various uncertain factors in the actual construction process, the artificial neural network is used to simulate and calculate the construction project progress, and the mathematical model for the construction progress prediction of the prefabricated hollow floor is established. The time dimension is added to the three-dimensional (3D) model for construction simulation, and the key nodes in the construction process are found through the construction animation and model information; thus, the construction schedule is designed and adjusted. The heterogeneous data fusion method is used to realize the BIM information fusion and feature clustering analysis of the construction progress of the prefabricated hollow floor, extract the characteristic quantity of the association rules of the construction progress, realize the convergence control and self-adaptation learning training in the construction progress prediction process through the fuzzy neural network learning algorithm, and use the BIM4D platform to realize the visual simulation of the construction progress prediction and plan management. On this basis, the schedule shall be prepared. The experimental results show that this method has strong generalization learning ability and good visual prediction accuracy for the construction progress BIM prediction of prefabricated hollow floor, which effectively ensures the efficiency of project implementation and the quality of the project and also carries out corresponding cost control. The BIM reliability prediction method of a prefabricated hollow floor construction schedule based on heterogeneous data fusion proposed in this study provides a practical tool for construction practitioners. By integrating BIM and data fusion technology, the method effectively integrates heterogeneous data from multiple channels, including weather, material supply, personnel allocation, etc., so as to achieve accurate prediction of the construction progress of prefabricated hollow floor. In practical applications, the method can track the construction progress in real time, reveal the potential risks and delay factors in time and provide targeted optimization suggestions based on the forecast results. This not only helps the project manager to better control the construction schedule and improve the on-time delivery rate of the project but also optimizes the allocation of resources and reduces the construction cost. In addition, the method is highly portable and can be applied to different types of construction projects, providing strong support for the digital transformation and intelligent upgrading of the construction industry. Therefore, the prediction method proposed in this study has important practical application value for construction industry practitioners who wish to improve the efficiency and accuracy of project management. | |
