随着科技的飞速发展,建筑信息模型(building information modeling,BIM)和测绘三维模型正在建筑行业中扮演着越来越重要的角色。它们各自具有独特的优势,如果能将两者有效地结合,将能极大地推进古建筑数字化保护的发展进程。本文主要探...随着科技的飞速发展,建筑信息模型(building information modeling,BIM)和测绘三维模型正在建筑行业中扮演着越来越重要的角色。它们各自具有独特的优势,如果能将两者有效地结合,将能极大地推进古建筑数字化保护的发展进程。本文主要探讨了基于BIM技术的岭南建筑三维测绘技术研究与应用。首先介绍了BIM模型与测绘三维模型的兼容性问题,并提出了相应的解决方案。其次基于BIM技术的建筑三维测绘方法,阐述了BIM模型与测绘三维模型结合的意义。最后,通过高精度钢结构预拼装和古建筑修缮两个应用实例展示了BIM模型与测绘三维模型结合的应用价值。展开更多
This paper investigates the differences that result from applying different approaches to uncertainty modeling and reports an experimental examining error estimation and propagation in elevation and slope, with the la...This paper investigates the differences that result from applying different approaches to uncertainty modeling and reports an experimental examining error estimation and propagation in elevation and slope, with the latter derived from the former. It is confirmed that significant differences exist between uncertainty descriptors, and propagation of uncertainty to end products is immensely affected by the specification of source uncertainty.展开更多
The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) m...The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) model, the forecasting series of GM(1,1) was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i.e., seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM(1,1) model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM(1,1) model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM(1,1) model. The mean Absolute error and mean square error of seasonal index-GM(1,1) are 30.69% and 54.53% smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.展开更多
文摘随着科技的飞速发展,建筑信息模型(building information modeling,BIM)和测绘三维模型正在建筑行业中扮演着越来越重要的角色。它们各自具有独特的优势,如果能将两者有效地结合,将能极大地推进古建筑数字化保护的发展进程。本文主要探讨了基于BIM技术的岭南建筑三维测绘技术研究与应用。首先介绍了BIM模型与测绘三维模型的兼容性问题,并提出了相应的解决方案。其次基于BIM技术的建筑三维测绘方法,阐述了BIM模型与测绘三维模型结合的意义。最后,通过高精度钢结构预拼装和古建筑修缮两个应用实例展示了BIM模型与测绘三维模型结合的应用价值。
文摘This paper investigates the differences that result from applying different approaches to uncertainty modeling and reports an experimental examining error estimation and propagation in elevation and slope, with the latter derived from the former. It is confirmed that significant differences exist between uncertainty descriptors, and propagation of uncertainty to end products is immensely affected by the specification of source uncertainty.
文摘The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) model, the forecasting series of GM(1,1) was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i.e., seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM(1,1) model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM(1,1) model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM(1,1) model. The mean Absolute error and mean square error of seasonal index-GM(1,1) are 30.69% and 54.53% smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.
