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With the increasing of tunnel mileage, one of the most vital problems of broad-scale structures is the intelligent identification and location of structural defects, which require massive data storage capacity and high data processing efficiency. The full-field point cloud data collected by terrestrial laser scanning (TLS) can be extracted to construct three-dimensional measured models more accurately, therefore, the fusion of TLS and the finite element method (FEM) is particularly beneficial to the deformation analysis of tunnel structures. This paper combines numerical simulation and full-field measurement to interactively verify the reliability of deformation analysis based on FEM simulation and point cloud processing. The innovation of this paper is that a three-dimensional FEM is validated through the measured point cloud data of the tunnel structure. The result of this study indicated that FEM simulation and the structural health monitoring analysis are consistent. [ABSTRACT FROM AUTHOR]

The application of three-dimensional laser scanning technology in the field of tunnel deformation monitoring has changed the traditional measurement method. It provides an automated and intelligent solution for monitoring the geometric deformation of tunnel sections due to its high efficiency and independence from environmental influences. In this paper, based on B-spline fitting and iterative nearest point (ICP) alignment, the calculation of the difference between the radial distance and the design radius of a tunnel is transformed into a curve transformation that iterates over the nearest-neighbor points and calculates the difference in the distance between the corresponding points. The innovation of this paper is that the high-precision tunnel deformation monitoring method integrating B-spline fitting and ICP alignment can automatically compensate for the missing point clouds, is not affected by the point clouds of the tunnel inner and outer liner appendages, is more sensitive in the local deformation feedback and can be applied to a variety of tunnel shapes. The results indicate that our method maximally improves the accuracy of the horizontal convergence calculation by 28.6 mm and the accuracy of the vault settlement by 27.8 mm in comparison with the least squares circle fitting algorithm. [ABSTRACT FROM AUTHOR]

With the rapid development of urban rail transit and the aging of transportation infrastructure, the demand for shield tunnel disaster detection is about to break out. Most of the traditional monitoring methods require considerable manpower and time costs, which cannot satisfy the increasing requirements of tunnel operation and maintenance. Free-form model construction and deviation mechanism analysis are investigated in this paper to monitor the geometric deformation information of shield tunnels quickly and accurately. A method for identifying geometric features of tunnel sections based on the free-form B-spline approximation is proposed. The innovation of this paper lies in the intelligent recognition of common interference targets by the residual classification method. Furthermore, various Root Mean Squared Error (RMSE) distributions are investigated, which successfully realizes and verifies the clustering analysis of certain point cloud features. [ABSTRACT FROM AUTHOR]

Extracting complete cross-sectional geometric features from the large amount of point cloud data acquired by laser scanners plays an important part in the detection of deformations in tunnel inspection projects. Tunnel cross-sections have symmetrical geometric features, and information is traditionally collected manually. The traditional manual extraction of point clouds is inefficient and limited by the subjectivity of the operators when addressing the problems. This paper proposes a new algorithm for the automatic identification of tunnel lining section curves, the rapid separation of common interference targets, and the optimization of curve geometry features. The innovation of this approach lies in the combination of B-spline and Euclidean clustering methods and the comprehensive evaluation of the denoising results in terms of precision, recall, F-score, and rand index (RI). In this way, the automatically extracted health point cloud data are refitted to optimize the tunnel profile model. [ABSTRACT FROM AUTHOR]

Freeform parameterizations to reproduce structure deformation are increasingly important topics in laser-scanner-based deformation analyses. High-accuracy assurance of free-form surface approximation is extremely critical for reliable deformation analysis. One main challenge in this field is the model selection. Improper model complexity could result in under-fitting the real object shape or overfitting data noises, and thus a failure of deformation analysis. A multi-sensor system could integrate advantages of different sensors and improve the quality of mission completed. This paper combines terrestrial laser scanning (TLS) and laser tracker (LT) technologies, to enhance high-accuracy surface modeling in deformation analysis. A surface-based B-spline approximation and a multi-sensor system are investigated, the latter of which focuses mainly on the combination of TLS and LT technologies. The innovation of this paper is that the surface-based B-spline approximation is validated and optimized with LT corner cube reflectors. Hypothesis testing is adopted to select the best parameter setting by judging most consistency of TLS and LT in various epochs. In the B-spline surface modeling, both instrumental and numerical uncertainties are considered. We use the instrumental uncertainty model based on intensity value, as well as numerical uncertainty based on adjustment theories. A sampling strategy is proposed to avoid data gaps and obtain even distributed data points. [ABSTRACT FROM AUTHOR]

The hazards of cracks, which could badly decrease reliability and safety of structures, are receiving increasing attention with the popularity of tunnel constructions. Traditional crack inspection relies on visual examination, which is time-, cost- and labor-intensive. Therefore, how to identify and measure cracks intelligently is significantly essential. The paper focuses on the Canny method to extract cracks of tunnel structures by the intensity value of reflectivity. We propose and investigate a novel method which combines dilation and the Canny algorithm to identify and extract the cracks automatically and intelligently based on the point cloud data of terrestrial laser scanning measurement. In order for measurement of cracks, the projection of summed edge pixels is adopted, where a synthesis is carried out on the detection results with all sampling parameters. Based on the synthesized image, vertical crack presents two sharp peaks where the space of the peaks indicates the average width of the crack, as well as its position. The advantage of the method is that it does not require determination of Canny detector parameters. The deviation between manual measurement and Canny detection is 2.92%. [ABSTRACT FROM AUTHOR]

Advanced robotic systems will encounter a rapid breakthrough opportunity and become increasingly important, especially with the aid of the accelerated development of artificial intelligence technology. Nowadays, advanced robotic systems are widely used in various fields. However, the development of artificial intelligence-based robot systems for structural health monitoring of tunnels needs to be further investigated, especially for data modeling and intelligent processing for noises. This research focuses on integrated B-spline approximation with a nonparametric rank method and reveals its advantages of high efficiency and noise resistance for the automatic health monitoring of tunnel structures. Furthermore, the root-mean-square error and time consumption of the rank-based and Huber's M-estimator methods are compared based on various profiles. The results imply that the rank-based method to model point cloud data has a comparative advantage in the monitoring of tunnel, as well as the large-area structures, which requires high degrees of efficiency and robustness. [ABSTRACT FROM AUTHOR]

Abstract Terrestrial laser scanning is extensively adopted in the area of high-precision monitoring and three-dimensional measurement. Architectural structures today are increasingly complex and health monitoring plays an important role in guaranteeing their safety. Therefore, how reliability deformation monitoring can be improved is one of the key problems in the field of engineering. This paper combines the three-dimensional laser scanning technology and finite element method (FEM) to investigate the deformation mechanism of arched structures. Within this paper, we simulated arched structures using the FEM, which is consistent with the result of terrestrial laser scanner (TLS) measurement. We aimed at constructing an intelligent and efficient FEM model which can be extensively applied in the monitoring of many constructs, such as bridges and ancient architecture. The focus in this research lies mainly on deformation analysis, which is based on FEM model simulation with the calibration of TLS measurement. [ABSTRACT FROM AUTHOR]

Abstract Automatically modeling and intelligently monitoring composite tunnel structures is of great significance considering the development of composite and diverse tunnel construction materials. Therefore, how to efficiently analyze the deformation of all kinds of tunnel structures with its expanding application is becoming increasingly important. In this paper, we analyze the over- and under-fitting problems under different point cloud profiles to generate an automatic and robust B-spline model which is a suitable method for high accuracy approximation capable of optimizing the composite tunnel model by adjusting the parameters. The innovation of this research is that we combine the maximum likelihood function and the degree of freedom to obtain the optimal parameters model of composite tunnels, which are validated by the various profiles. [ABSTRACT FROM AUTHOR]

Abstract Terrestrial laser scanning (TLS) technology plays an important role in the field of deformation monitoring nowadays, and a lot of the architectures are arched composite structures, which are required to be further understood for their mechanical and physical properties. Only a theoretical numerical analysis is sometimes difficult and defective to achieve, due to varying loads and other factors influencing the stability and reliability of such structures. Therefore, measurements can improve the theoretical analysis in several ways. One of the most important issues is the deformation analysis within load experiments. This paper focuses on the presentation of new possibilities for surface-based measurement techniques and their applicability in the deformation analysis of arched structures. The goal of this paper is to investigate the difference of load-displacement relationships between arched and beam structures though a static load experiment based on high accuracy TLS measurement techniques and the surface analysis method. In this paper, we try to reveal the influencing mechanism of ‘prestressing-like’ force and interpret why arched structures can withstand a surprisingly greater load than beam structures under the same conditions. [ABSTRACT FROM AUTHOR]

With the development of city constructions, tunnels are becoming important structures for underground transportation. Tunnels constitute layered composite structures with concrete, reinforcement, waterproof layers, etc. Deformation monitoring of this kind of wide-ranging composite structure is significant to assure their safety considering the development of their complexity. Terrestrial laser scanning (TLS) is one of the most accurate and fast measurement technologies for deformation analysis. It has been applied widely in survey fields with the advantages of non-contact and panoramic acquisition of information. In this situation, TLS instruments are being developed rapidly, which necessitates high requirements regarding software aspects, especially concerning high-accuracy model construction. Therefore, developing a reliable method for 3D modeling with complex and massive point clouds is urgent. In this paper, we propose an adaptive expectation maximization (EM) method based on the scaled t-distribution for B-spline estimation, where automation is achieved for the best approximation with the maximum probability density. The innovation of this paper lies in offering a robust, automatic and time-efficient solution to model practical tunnel structures with a complex point cloud. [ABSTRACT FROM AUTHOR]

How to obtain a three-dimensional (3D) model efficiently and extract the feature information of larger-scale composite structures, such as tunnels, accurately is a significant issue in the field of health monitoring. Therefore, an effective method based on TLS measurement is proposed and developed using surface-based non-destructive technology. In this paper, terrestrial laser scanning (TLS) technology is adopted to investigate the tunnel structure, focusing on the extraction of the characteristic section and central curve, which could be applied in deformation monitoring. Point cloud data from TLS measurement is processed in four steps: section extraction, section projection, calculation of central points and curve approximation. The innovation of this paper lies in the projection and iterative filtering of the ring data and rasterization of the point clouds for vertical and horizontal lines. The random sample consensus (RANSAC) algorithm is implemented to approximate the vertical and horizontal lines. The central curve, approximated from the central points, agrees with the general design model and the accuracy falls within the millimeter range. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

High-accuracy deformation analysis of composite structures is an important topic in the field of civil engineering and increasing attention is given to geometry-based analytical model, such as B-splines where the determination of the parameter values has a great influence on the accuracy of the B-spline approximation. In this paper, B-spline approximation is carried out with point cloud data of an arch structure obtained with terrestrial laser scanner (TLS) and the parameter control point is investigated with the aid of laser tracker (LT) and corner cube reflectors (CCRs). We focused on the discussion about the influence factors of the optimal parameters, such as CCR uncertainty, curvature of object, and order of the B-spline. The innovation of this paper is that we adopt LT to optimize the parameters of B-spline fitting, and the results are compared and verified by Bayesian information criterion. [ABSTRACT FROM AUTHOR]