@ARTICLE{Du_Cong_Application_2024,
 author={Du, Cong and Wen, Yi and Ren, Lijian},
 volume={vol. 70},
 number={No 3},
 journal={Archives of Civil Engineering},
 pages={445-457},
 howpublished={online},
 year={2024},
 publisher={WARSAW UNIVERSITY OF TECHNOLOGY FACULTY OF CIVIL ENGINEERING and COMMITTEE FOR CIVIL ENGINEERING POLISH ACADEMY OF SCIENCES},
 abstract={In response to the current issue of poor modeling performance of Building Information Modeling for building models, a new Building Information Modeling based on an improved region growth algorithm is proposed. This method improves the region growth algorithm by introducing machine learning technology, and utilizes the improved algorithm to perfect the building model, thereby improving the efficiency of Building Information Modeling. The performance comparison experiment of the improved algorithm shows that its accuracy is 92.3%, respectively, which are lower than the comparison algorithm. Subsequent empirical analysis found that the robustness rating of the renovated building with the new Building Information Modeling was 94.06, significantly higher than the traditional model. The above results indicate that the new Building Information Modeling proposed in the study has high efficiency and accuracy in building reinforcement and renovation. This method can provide a new solution and idea for the field of building reinforcement and renovation.},
 type={Article},
 title={Application of BIM model based on improved region growth algorithm in building reinforcement and renovation},
 URL={http://ochroma.man.poznan.pl/Content/132821/28_2k.pdf},
 doi={10.24425/ace.2024.150994},
 keywords={region growth algorithm, BIM model, building renovation, spherical fixed distance method, K-nearest neighbor method},
}