Details
| Original language | English |
|---|---|
| Journal | International Journal of Distributed Sensor Networks |
| Volume | 15 |
| Issue number | 11 |
| Early online date | 4 Nov 2019 |
| Publication status | Published - Nov 2019 |
Abstract
The terrestrial laser scanning technology is increasingly applied in the deformation monitoring of tunnel structures. However, outliers and data gaps in the terrestrial laser scanning point cloud data have a deteriorating effect on the model reconstruction. A traditional remedy is to delete the outliers in advance of the approximation, which could be time- and labor-consuming for large-scale structures. This research focuses on an outlier-resistant and intelligent method for B-spline approximation with a rank (R)-based estimator, and applies to tunnel measurements. The control points of the B-spline model are estimated specifically by means of the R-estimator based on Wilcoxon scores. A comparative study is carried out on rank-based and ordinary least squares methods, where the Hausdorff distance is adopted to analyze quantitatively for the different settings of control point number of B-spline approximation. It is concluded that the proposed method for tunnel profile modeling is robust against outliers and data gaps, computationally convenient, and it does not need to determine extra tuning constants.
Keywords
- B-spline approximation, health monitoring, rank-based estimator, robust modeling, Terrestrial laser scanning
ASJC Scopus subject areas
- Engineering(all)
- Computer Science(all)
- Computer Networks and Communications
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In: International Journal of Distributed Sensor Networks, Vol. 15, No. 11, 11.2019.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Robust and automatic modeling of tunnel structures based on terrestrial laser scanning measurement
AU - Xu, Xiangyang
AU - Yang, Hao
AU - Kargoll, Boris
N1 - Funding information: The authors would like to acknowledge the support of Natural Science Foundation of Jiangsu Province (No. BK20160558). The authors also wish to acknowledge the support of all the colleagues in Geodetic Institute of Leibniz University Hannover. The authors would like to acknowledge the support of Natural Science Foundation of Jiangsu Province (No. BK20160558). The authors also wish to acknowledge the support of all the colleagues in Geodetic Institute of Leibniz University Hannover. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The publication of this article was funded by the Open Access Fund of the Leibniz Universit?t Hannover. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The publication of this article was funded by the Open Access Fund of the Leibniz Universität Hannover.
PY - 2019/11
Y1 - 2019/11
N2 - The terrestrial laser scanning technology is increasingly applied in the deformation monitoring of tunnel structures. However, outliers and data gaps in the terrestrial laser scanning point cloud data have a deteriorating effect on the model reconstruction. A traditional remedy is to delete the outliers in advance of the approximation, which could be time- and labor-consuming for large-scale structures. This research focuses on an outlier-resistant and intelligent method for B-spline approximation with a rank (R)-based estimator, and applies to tunnel measurements. The control points of the B-spline model are estimated specifically by means of the R-estimator based on Wilcoxon scores. A comparative study is carried out on rank-based and ordinary least squares methods, where the Hausdorff distance is adopted to analyze quantitatively for the different settings of control point number of B-spline approximation. It is concluded that the proposed method for tunnel profile modeling is robust against outliers and data gaps, computationally convenient, and it does not need to determine extra tuning constants.
AB - The terrestrial laser scanning technology is increasingly applied in the deformation monitoring of tunnel structures. However, outliers and data gaps in the terrestrial laser scanning point cloud data have a deteriorating effect on the model reconstruction. A traditional remedy is to delete the outliers in advance of the approximation, which could be time- and labor-consuming for large-scale structures. This research focuses on an outlier-resistant and intelligent method for B-spline approximation with a rank (R)-based estimator, and applies to tunnel measurements. The control points of the B-spline model are estimated specifically by means of the R-estimator based on Wilcoxon scores. A comparative study is carried out on rank-based and ordinary least squares methods, where the Hausdorff distance is adopted to analyze quantitatively for the different settings of control point number of B-spline approximation. It is concluded that the proposed method for tunnel profile modeling is robust against outliers and data gaps, computationally convenient, and it does not need to determine extra tuning constants.
KW - B-spline approximation
KW - health monitoring
KW - rank-based estimator
KW - robust modeling
KW - Terrestrial laser scanning
UR - http://www.scopus.com/inward/record.url?scp=85074645718&partnerID=8YFLogxK
U2 - 10.1177/1550147719884886
DO - 10.1177/1550147719884886
M3 - Article
AN - SCOPUS:85074645718
VL - 15
JO - International Journal of Distributed Sensor Networks
JF - International Journal of Distributed Sensor Networks
SN - 1550-1329
IS - 11
ER -