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An automatic finite element modelling for deformation analysis of composite structures
Composite Structures 212 (2019) 434–438
Contents lists available at ScienceDirect
Composite Structures journal homepage: www.elsevier.com/locate/compstruct
An automatic finite element modelling for deformation analysis of composite structures Hao Yanga,b,1, Xiangyang Xub, a b
T
⁎,1
, Ingo Neumannb
Jiangsu University of Science and Technology, Zhenjiang City, Jiangsu Province, PR China Geodetic Institute, Faculty of Civil Engineering and Geodetic Science, Leibniz University Hanover, Germany
A R T I C LE I N FO
A B S T R A C T
Keywords: Point cloud Displacement Deformation analysis Finite element method Terrestrial laser scanning
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.
1. Introduction The theories and methods of TLS are currently adopted to investigate the mechanical properties to analyze the deformation behavior of structures. The TLS technique is a promising method to monitor the deformations of artificial and natural constructions. In this paper, the focus lies mainly on the TLS measurement of the test specimen regarding deformation behavior and FEM analysis of arched structures. The goal of this paper is to construct an intelligent and efficient FEM model for the simulation and prediction of the deformation of arched structures. 1.1. Terrestrial laser scanning technology Terrestrial laser scanning is a new technology of spatial data acquisition [1] which is extensively adopted in the area of high-precision monitoring, three-dimensional measurement and so on [2–9]. Based on the experimental and numerical analysis, the deformation distributions are investigated for engineering applications which has gained rapid development in the past several years [10–18]. TLS uses 3D point clouds to depict the measured objects. In the case of a time of flight instrument, data acquisition consists of three phases, i.e. sending of a laser pulse along a direction instantaneously defined by
⁎
Corresponding author. E-mail address: [email protected] (X. Xu). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.compstruct.2019.01.047 Received 15 August 2018; Accepted 5 January 2019 Available online 11 January 2019 0263-8223/ © 2019 Elsevier Ltd. All rights reserved.
means of rotating mirrors, reflection of laser pulse on object surface and reception of the reflected laser pulse. In this way, TLS gains the coordinate information of 3D points over the object surface. Besides x, y and z coordinates information, intensity value of reflected laser can be also obtained, which together compose 4D information of the object. TLS measurement is reliable and relatively precise, and similar as other technologies, it will get broad application perspectives [19–23]. TLS technology has received significant interest in various applications including in the field of structural health monitoring. Since TLS has demonstrated the ability to capture surface geometry with millimeter accuracy, it is adopted for the investigation of many kinds of constructions and structures, such as bridges, tunnels, dams and so on. However, research on its application in large bridge structure with a mechanical construction model for a joint evaluation remains limited [24]. The TLS surface-based measurements applied with numerical FEM method has a good application prospective. Main reason is3D measurement techniques of the geometric structure behavior are very helpful in order to verify and improve FEM models, based on the comprehensive model comparison. One example is that Hansen et al. proposed systematic FEM analysis on a neo-Gothic vault [25] and foundation of offshore wind turbines [26] with a combination of static measurements by TLS instruments.
Composite Structures 212 (2019) 434–438
H. Yang et al.
1.2. Finite element description FEM is a well-known numerical technique, which uses variational methods to minimize an error function and provide a stabilizing solution [27]. FEM modeling is based on a discretization of the form of the function used to represent the solution, as opposed to the simple discretization of the physical space that characterize the finite difference method. Usually, the input data are geometric model, material parameters, loads, boundary conditions, etc. After numerical calculation under the defined conditions, it can simulate some physical response of structures of interest, e.g. displacement, strain and stress. ANSYS is one of the most powerful FEM software, which includes concrete element data in SOLID 65. It is applied in 3D modeling of concrete structures with reinforcement. For data process improvement, many methods e.g. adaptive Kalman-filtering methods can be adopted in model calibration, which will provide more reliable models [28]. Others models for simulation and predication are study [29–32]. Traditional FEM calibrations are based on numerical methods, where the parameters of FEM models are iteratively updated till the analysis result is close enough to the measurements. New methods beyond traditional numerical ones have been proposed for the parametric identification of FEM models combined with the geodetic measurements. General process of parametric identification of FEM model with Kalman filtering and geodetic systems was investigated by Heunecke [33]. Its applications for large-scale case study can be found in [34]. Eichhorn [35] developed a non-stationary thermal deformation FEM model with adaptive Kalman-filtering based on geodetic measurements. Lienhart [36] carried out integrated analysis of bridge structures from geodetic measurement results and FEM results, where the physical parameters are estimated in a least squares estimation. General model of a least-squares adjustment was also studied by Neitzel et al. [37] and Wu et al. [38], which inverted the procedure of finite element method. However, the answer is still unrevealed to the question of sensor selection for the optimal observation of the structural behaviors and corresponding measurement strategies and data processing methods [37]. The systematic research of FEM calibration based on TLS measurement is required urgently. FEM calibration based on static testing is gaining some attention in structure analysis, because it has high precision and stability in damage identification. Applications contain nondestructive strain measurement technology during static tests and the FEM model calibration based on the measurements, in order to represent the 3D system behavior of the structures [39–42]. Recently, Weisbrich and Neitzel et al. [43] presented a method for structural health monitoring using sensor measurements and a FEM model. Ribeiro et al. [44] described the calibration of the numerical model of a bowstring-arch railway bridge based on a vibration test, which allowed the identification of the natural frequencies, mode shapes and damping coefficients of vibration. Similar research topics could also be found in [45–47]. In these researches, the accuracy of experimental data will decrease due to bad test environment, where the experiment date is sensitive to noise. Furthermore, free vibration attenuation method cannot obtain higher-order vibration parameters; while extra test equipment is sometimes too large for transportation with forced resonance method [48]. Some non-contact techniques such as Digital Image Correlation (DIC), Global Positioning System (GPS) receivers and laser Doppler vibrometer (LDV) have also been adopted for the vibration testing of structures of bridges. However, large amount of data generated during measurement could be a main problem limited by computing power and memory available. Furthermore, the reduced testing time and appropriate measurement parameters can be difficult in structures subjected to random loading, and the relation between the external forces and the displacements is not well available [49–51].
Fig. 1. Experimental setup [4].
Fig. 2. Work-flow of optimizing the FEM model.
Fig. 3. FEM model. Table 1 Parameters of FEM. Parameters
Values
Length, L Width, W Young’s Modulus, Y Poisson’s Ratio, P
200 mm 100 mm 25GPa 0.15
2. Experiment This measurement is set to observe and investigate arched structural deformation characterization [4]. In Fig. 1, we install the digital camera which takes the photo of arch and TLS which collect the point cloud on the top. There are several triangular displacement sensors under the concrete arch to record the displacement. Two cylinders are fixed at both sides of the arch structure for loading. The measured arch is cautiously inspected at every load step, and the loads are measured by force sensors. 3. FEM construction In the experiment, we attempt to investigate influence of load variation on the maximum displacement of arch. Measurements are executed for assessing the realistic spatial resolution of a TLS as a function of the sampling procedure in optimal observation. It should be noted that concrete is not a quasi-lambertian reflector for a typical TLS, where lambertian reflectance is the property that defines an ideal “matte” or 435
Composite Structures 212 (2019) 434–438
H. Yang et al.
Fig. 4. Feature points coordinates of arch points cloud.
expensive, and sometimes don’t model the circumstances of the factual conformation exactly. On another hand, the TLS measurement provides a high accurate and efficient data about the monitoring object. This measurement, which could be surface based, is also able to be contrasted to the prediction of the FEM model. The requirement of experimental research continues within the frame of optimizing a versatile FEM model for concrete arch construction. We present our work-flow in detail in Fig. 2. Firstly, the point cloud is extracted from TLS measurement, then we deal with the data as the steps of point cloud processing, deformation recognition and deformation pattern. After FEM modeling and meshing, we will get a theoretical deformation pattern. With the comparison of experimental deformation pattern and theoretical deformation pattern, we will gain an optimized FEM model. In this paper, the focus mainly lies on the deformation behavior of the test specimen and FEM analysis of displacements and stresses of concrete arch.
Fig. 5. 3D point cloud extraction of arched structure [4].
3.2. Data extraction The TLS measurement collects the information of point clouds, which includes the values of x, y, z coordinate and intensity. We obtain the intensity value of the laser reflected from the loaded arch. Then we are able to extract the information from the point clouds. The points cloud is a three-dimensional model with discrete data. However, it is necessary to obtain continuous surfaces in order to mesh the arch structure. The FEM model is reconstructed from the 3D point clouds of TLS which is presented in Fig. 3. According to Fig. 3, the blue part are 3D point clouds exacted by TLS measurement, and geomagic is adopted to build the FEM model. The Parameters of FEM is present in Table 1 where the length L and width W are 200 and 100 mm. The Young's Modulus Y is 25 GPa and Poisson's Ratio P is 0.15.
Fig. 6. Deformation simulation of arched structure.
diffusely reflecting surface. 4. Analysis 3.1. Framework The TLS measurement collects the information from point clouds, which includes the values of x, y and z coordinates and intensity. We obtain the intensity value of the laser reflected from the loaded arched structure. We can then extract the information from the point clouds. Thus, we can obtain the feature point coordinates of the arch’s point cloud in Fig. 4. Therefore, the high precision distances between points
This paper takes advantage of FEM to check and verify the experimental results, which is obtained by TLS measurement about the deformation of concrete arch. On the one hand, the reliable numerical models, for example FEM models, is able to decrease the quantity of experiments, acknowledging that most of the experiments are 436
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Fig. 7. The DOF solution of arch deformation.
The DOF solution is presented in Fig. 7, in which the NODAL solution is adopted, where (a), (b) and (c) correspond to the X, Y and Z components of displacement and (d) is the sum of the displacement vector.
can be gained. The simulation of 3D displacement and stress are presented where the deviation is acceptable to confirm whether the model is appropriate [4] (Fig. 5). Aiming at simulating the stress and displacement of an arched structure, we construct a corresponding FEM model which can be used to analyze the deformation behavior. In this simulation, we use the computer with a 32 GB RAM, 64-bit operating system and the computation time is approximately 30 min, which depends on the element of mesh and the type of refinement.
6. Conclusions The TLS is an indispensable technique to monitor the deformation of constructs and ensure structure safety with better accuracy, efficiency and flexibility. Combined with the FEM model, a prediction of structure deformation is available which could generate an evaluation report about a structure’s safety level and protection methods. The goal of this paper is to generate a reasonable and reliable FEM model that could improve the health monitoring from “real-time monitoring” to “multi-temporal prediction.” In this article, the FEM model of the arched structure is established and calibrated with TLS measurements that can obtain 3D point cloud data which could describe the constructs’ 3D coordinates distinctly. Within this paper, the deformation behavior of arched structures is analyzed based on ANSYS simulation and prediction with the calibration of TLS measurement. The ANSYS FEM was employed to perform a nonlinear analysis with a Solid 65 element. The carrying capacity, deformation character and mechanical properties of arched structures are investigated using the FEM simulation and the model predicates that the arched structure will be outside the safety level when the load
5. Results The deformed sharp is present in Fig. 6, and the write part describes the deformation where the middle of the arch is maximum different displacements. According to Fig. 6, the value of the maximum displacement DMX is 11.18 mm, which is smaller than the experiment value of 17.76 mm. Considering the displacement sedimentation caused by the deformation of the foundation, especially on elastic ground, the left side support is not absolutely fixed. Therefore, the lateral displacement will contribute part of the deformation of the arched structure, which would increase the vertical displacement of the arch. However, both supports are displacement constraints in the FEM simulation. Nevertheless, the displacement compensation analysis will be adopted. 437
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increase is doubled. Until the load is two times more the arched structure will fail. The experimental results show that the theoretical model presented in this article is effective for the prediction, which indicates that the dynamic model is valid and the simulation analysis is reliable.
[22]
[23]
Acknowledgments
[24]
The publication of this article was funded by the support of Natural Science Foundation of Jiangsu Province (No: BK20160558). The authors gratefully acknowledge the support of Massivbau Institute to this research work.
[25] [26]
[27]
Conflicts of interest [28]
The authors declare no conflict of interest.
[29] [30]
Appendix A. Supplementary data
[31]
Supplementary data to this article can be found online at https:// doi.org/10.1016/j.compstruct.2019.01.047.
[32]
References
[33]
[1] Park HS, Lee HM, Adeli H, Lee I. A new approach for health monitoring of structures: terrestrial laser scanning. Comput-Aided Civ Infrastruct Eng 2007;22:19–30. [2] Yang H, Xu X, Neumann I. Laser Scanning-Based Updating of a Finite-Element Model for Structural Health Monitoring. IEEE sensors; 2016. p. 2100–4. [3] Xu X, Yang H, Zhang Y, Neumann I. Intelligent 3D data extraction method for deformation analysis of composite structures. Compos Struct 2018;203:254–8. [4] Yang H, Omidalizarandi M, Xu X, Neumann I. Terrestrial laser scanning technology for deformation monitoring and surface modeling of arch structures. Compos Struct 2017;169:173–9. [5] Xu X, Yang H. Network method for deformation analysis of three-dimensional point cloud with terrestrial laser scanning sensor. Int J Distrib Sens Netw 2018;14. [6] Yang H, Xu X, Neumann I. Optimal finite element model with response surface methodology for concrete structures based on Terrestrial Laser Scanning technology. Compos Struct 2016. https://doi.org/10.1016/j.compstruct.2016.11.012. [7] Xu X, Bureick J, Yang H, Neumann I. TLS-based composite structure deformation analysis validated with laser tracker. Compos Struct 2018;202:60–5. [8] Yang H, Xu X, Xu W, Neumann I. Terrestrial laser scanning-based deformation analysis for arch and beam structures. IEEE Sens J 2017;17:4605–11. [9] Xu X, Yang H, Neumann I. Monotonic loads experiment investigate of composite structure based on terrestrial laser scanner measurement. Compos Struct 2018;183:563–7. [10] Tuno N, Mulahusic A, Kogoj D. Improving the positional accuracy of digital cadastral maps through optimal geometric transformation. J Surv Eng 2017;143. [11] Yang H, Xu X, Neumann I. The benefit of 3d laser scanning technology in the generation and calibration of FEM models for health assessment of concrete structures. Sensors 2014;14(11):21889–904. [12] Xu X, Zhao X, Yang H, Neumann I. TLS-based feature extraction and 3D modeling for arch structures. J Sensors 2017. Article ID 9124254. [13] Xu X, Yang H, Neumann I. Time-efficient filtering method for three-dimensional point clouds data of tunnel structures. Adv Mech Eng 2018;10(5):1–6. [14] Xu X, Kargoll B, Bureick J, Yang H, Neumann I. TLS-based profile model analysis of major composite structures with robust B-spline method. Compos Struct 2017;184:814–20. [15] Yang H, Xu X, Neumann I. Deformation behavior analysis of composite structures under monotonic loads based on terrestrial laser scanning technology. Compos Struct 2018;183:594–9. [16] Xu X, Yang H, Neumann I. A feature extraction method for deformation analysis of large-scale composite structures based on TLS measurement. Compos Struct 2018;184:591–6. [17] Xu X, Yang H, Neumann I. Deformation monitoring of typical composite structures based on terrestrial laser scanning technology. Composite Struct, Compos Struct 2018;202:77–81. [18] Wilkinson MW, Jones RR, Woods CE. A comparison of terrestrial laser scanning and structure-from-motion photogrammetry as methods for digital outcrop acquisition. Geosphere 2016;12:1865–80. [19] Wei Xu, Xu X, Yang H, Neumann I. Optimized finite element analysis model based on terrestrial laser scanning data. Compos Struct 2019;207:62–71. [20] Rodriguez-Gonzalvez P, Jimenez Fernandez-Palacios B, Luis Munoz-Nieto A. Mobile LiDAR system: new possibilities for the documentation and dissemination of large cultural heritage sites. Remote Sens 2017;9. [21] Moldovan N, Kulkarni S, Ferrari M. Use of laser scanning cytometry for analysis of
[34] [35]
[36] [37]
[38]
[39]
[40] [41]
[42]
[43]
[44]
[45] [46]
[47] [48] [49]
[50]
[51]
438
endothelial cells attached to micropatterned silicon surfaces. Sens Mater 2002;14:179–87. Oberlander M, Bartsch K, Schulte C. Process monitoring of laser remote cutting of carbon fiber reinforced plastics by means of reflecting laser radiation. J Laser Appl 2017;29(022009). Xu X, Kargoll B, Bureick J, Yang H, Neumann I. An automatic and intelligent optimal surface modeling method for composite tunnel structures. Compos Struct 2019;208:702–10. Kitratporn Nuntikorn, Takeuchi Wataru, Matsumoto Koji. Structure deformation measurement with terrestrial laser scanner at pathein bridge in Myanmar. J Disaster Res 2018;13:40–9. Hansen M, Piehler J, Kapphahn G. Systemanalyse neugotischer Gewölbe, 8. Mauerwerkskalendertag, Dresden, March 24, 2015. Hansen M, Schmidt B, Kelma S, Schmoor K, Goretzka J. Probabilistic assessment of the foundation of offshore wind turbines. Proceedings of the IABSE Conference, Elegance in Structures. 2015. Banerjee U, Osborn J. Generalized finite element methods: main ideas, results, and perspective. Int J Comput Methods 2004:67–103. Schmalz T, Buhl V, Eichhorn A. An adaptive kalman-filtering approach for the calibration of finite difference models of mass movements. J Appl Geod 2010:127–35. Wang Yi, Bo Yu, Sun Shuyu. Fast prediction method for steady-state heat convection. Chem Eng Technol 2012;35(4):668–78. Ganapuram S, Adams M, Patnaik A. Quantification of cracks in concrete bridge decks in Ohio district 3. Akron, American: The University of Akron; 2012. Kang DS, Lee HM, Park HS, Lee I. Computing method for estimating strain and stress of steel beams using terrestrial laser scanning and FEM. Key Eng Mater 2007;347:517–22. Kumar S, Bag S, Baruah M. Finite element model for femtosecond laser pulse heating using dual phase lag effect. J Laser Appl 2016;28(032008). Heunecke O. Zur Identifikation und Verifikation von Deformations-prozessen mittels adaptiver KALMAN-Filterung (Hannoversches Filter). Hanover, Germany: Leibniz Universität Hannover; 1995. Thesis. Gülal E. Geodätische Überwachung einer Talsperre: eine Anwendung der KALMANFiltertechnik. Thesis: Leibniz Universität Hannover, Hanover, Germany; 1997. Eichhorn A. Ein Betrag zur Identifikation von dynamischen Strukturmodellen mit Methoden der adaptiven KALMAN-Filterung. Stuttgart, Germany: Universität Stuttgart; 2005. Ph. D thesis. Lienhart W. Analysis of inhomogeneous structural monitoring data. Granz, Austria: Graz University of Technology; 2007. Ph. D thesis. Becker T, Weisbrich S, Euteneuer F, Wu CC, Neitzel F. Neue Möglichkeiten in der Bauwerksüberwachung durch integrierte Analyse von Sensormessungen und 3DBauwerksmodell. In: DGPF Proceedings of Sensors, Measurement and Testing Techniques, March 26–28, Hamburg, 2014. Cheng-Chieh Wu, Sven Weisbrich and Frank Neitzel. Materials Today: Proceedings. Volume 3, Issue 4, 2016, Pages 1211-1215. Inverse Finite Element Adjustment of Material Parameters from Integrated Analysis of Displacement Field Measurement. Sanayei M, Phelps JE, Sipple JD, Bell ES, Brenner BR. Instrumentation, nondestructive testing, and finite-element model updating for bridge evaluation using strain measurements. J Bridge Eng 2012;17(1):130–8. Čecháková V, Rosmanit M, Fojtik R. FEM modeling and experimental tests of timber bridge structure. Procedia Eng 2012;40:79–84. Gasco F, Feraboli P, Braun J, Smith J, Stickler P, DeOto L. Wireless strain measurement for structural testing and health monitoring of carbon fiber composites. Compos A Appl Sci Manuf 2011;42(9):1263–74. Yang Y, Liu D, He Z, Luo Z. Optimization of preform shapes by RSM and FEM to improve deformation homogeneity in aerospace forgings. Chin J Aeronaut 2010;23(2):260–7. Becker T, Weisbrich S, Euteneuer F, Wu CC, Neitzel F. Neue Möglichkeiten in der Bauwerksüberwachung durch integrierte Analyse von Sensormessungen und 3DBauwerksmodell. Gemeinsame Tagung 2014 der DGfK, der DGPF, der GfGI und des GiN (DGPF Tagungsband 23/2014), 2014. Ribeiro D, Calçada R, Delgado R, Brehm M, Zabel V. Finite element model updating of a bowstring-arch railway bridge based on experimental modal parameters. Eng Struct July 2012;40:413–35. Aras F, Krstevska L, Altay G, Tashkov L. Experimental and numerical modal analyses of a historical masonry palace. Constr Build Mater 2011;25(1):81–91. Foti Dora, Diaferio Mariella, Giannoccaro Nicola Ivan. Michele Mongelli.Ambient vibration testing, dynamic identification and model updating of a historic tower. NDT E Int 2012;47:88–95. Sevim Bariş. Finite element model calibration of berke arch dam using operationalmodal testing. J Vibr Control 2011;17. pp. 7 1065-1079. Zong ZH, Ren WX. Finite element model updating and model validation of bridge structures. China: People's Commumication Press; 2012. Beberniss Timothy J, Ehrhardt David A. High-speed 3D digital image correlation vibration measurement: Recent advancements and noted limitations. Mech Syst Signal Process 2017;86(Part B):35–48. Reu Phillip L, Rohe Daniel P, Jacobs Laura D. Comparison of DIC and LDV for practical vibration and modal measurements. Mech Syst Signal Process 2017;86(Part B):2–16. Nakamura S. GPS measurement of wind-induced suspension bridge girder displacements. J Struct Eng 2000;126(12):1413–9.
Contents lists available at ScienceDirect
Composite Structures journal homepage: www.elsevier.com/locate/compstruct
An automatic finite element modelling for deformation analysis of composite structures Hao Yanga,b,1, Xiangyang Xub, a b
T
⁎,1
, Ingo Neumannb
Jiangsu University of Science and Technology, Zhenjiang City, Jiangsu Province, PR China Geodetic Institute, Faculty of Civil Engineering and Geodetic Science, Leibniz University Hanover, Germany
A R T I C LE I N FO
A B S T R A C T
Keywords: Point cloud Displacement Deformation analysis Finite element method Terrestrial laser scanning
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.
1. Introduction The theories and methods of TLS are currently adopted to investigate the mechanical properties to analyze the deformation behavior of structures. The TLS technique is a promising method to monitor the deformations of artificial and natural constructions. In this paper, the focus lies mainly on the TLS measurement of the test specimen regarding deformation behavior and FEM analysis of arched structures. The goal of this paper is to construct an intelligent and efficient FEM model for the simulation and prediction of the deformation of arched structures. 1.1. Terrestrial laser scanning technology Terrestrial laser scanning is a new technology of spatial data acquisition [1] which is extensively adopted in the area of high-precision monitoring, three-dimensional measurement and so on [2–9]. Based on the experimental and numerical analysis, the deformation distributions are investigated for engineering applications which has gained rapid development in the past several years [10–18]. TLS uses 3D point clouds to depict the measured objects. In the case of a time of flight instrument, data acquisition consists of three phases, i.e. sending of a laser pulse along a direction instantaneously defined by
⁎
Corresponding author. E-mail address: [email protected] (X. Xu). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.compstruct.2019.01.047 Received 15 August 2018; Accepted 5 January 2019 Available online 11 January 2019 0263-8223/ © 2019 Elsevier Ltd. All rights reserved.
means of rotating mirrors, reflection of laser pulse on object surface and reception of the reflected laser pulse. In this way, TLS gains the coordinate information of 3D points over the object surface. Besides x, y and z coordinates information, intensity value of reflected laser can be also obtained, which together compose 4D information of the object. TLS measurement is reliable and relatively precise, and similar as other technologies, it will get broad application perspectives [19–23]. TLS technology has received significant interest in various applications including in the field of structural health monitoring. Since TLS has demonstrated the ability to capture surface geometry with millimeter accuracy, it is adopted for the investigation of many kinds of constructions and structures, such as bridges, tunnels, dams and so on. However, research on its application in large bridge structure with a mechanical construction model for a joint evaluation remains limited [24]. The TLS surface-based measurements applied with numerical FEM method has a good application prospective. Main reason is3D measurement techniques of the geometric structure behavior are very helpful in order to verify and improve FEM models, based on the comprehensive model comparison. One example is that Hansen et al. proposed systematic FEM analysis on a neo-Gothic vault [25] and foundation of offshore wind turbines [26] with a combination of static measurements by TLS instruments.
Composite Structures 212 (2019) 434–438
H. Yang et al.
1.2. Finite element description FEM is a well-known numerical technique, which uses variational methods to minimize an error function and provide a stabilizing solution [27]. FEM modeling is based on a discretization of the form of the function used to represent the solution, as opposed to the simple discretization of the physical space that characterize the finite difference method. Usually, the input data are geometric model, material parameters, loads, boundary conditions, etc. After numerical calculation under the defined conditions, it can simulate some physical response of structures of interest, e.g. displacement, strain and stress. ANSYS is one of the most powerful FEM software, which includes concrete element data in SOLID 65. It is applied in 3D modeling of concrete structures with reinforcement. For data process improvement, many methods e.g. adaptive Kalman-filtering methods can be adopted in model calibration, which will provide more reliable models [28]. Others models for simulation and predication are study [29–32]. Traditional FEM calibrations are based on numerical methods, where the parameters of FEM models are iteratively updated till the analysis result is close enough to the measurements. New methods beyond traditional numerical ones have been proposed for the parametric identification of FEM models combined with the geodetic measurements. General process of parametric identification of FEM model with Kalman filtering and geodetic systems was investigated by Heunecke [33]. Its applications for large-scale case study can be found in [34]. Eichhorn [35] developed a non-stationary thermal deformation FEM model with adaptive Kalman-filtering based on geodetic measurements. Lienhart [36] carried out integrated analysis of bridge structures from geodetic measurement results and FEM results, where the physical parameters are estimated in a least squares estimation. General model of a least-squares adjustment was also studied by Neitzel et al. [37] and Wu et al. [38], which inverted the procedure of finite element method. However, the answer is still unrevealed to the question of sensor selection for the optimal observation of the structural behaviors and corresponding measurement strategies and data processing methods [37]. The systematic research of FEM calibration based on TLS measurement is required urgently. FEM calibration based on static testing is gaining some attention in structure analysis, because it has high precision and stability in damage identification. Applications contain nondestructive strain measurement technology during static tests and the FEM model calibration based on the measurements, in order to represent the 3D system behavior of the structures [39–42]. Recently, Weisbrich and Neitzel et al. [43] presented a method for structural health monitoring using sensor measurements and a FEM model. Ribeiro et al. [44] described the calibration of the numerical model of a bowstring-arch railway bridge based on a vibration test, which allowed the identification of the natural frequencies, mode shapes and damping coefficients of vibration. Similar research topics could also be found in [45–47]. In these researches, the accuracy of experimental data will decrease due to bad test environment, where the experiment date is sensitive to noise. Furthermore, free vibration attenuation method cannot obtain higher-order vibration parameters; while extra test equipment is sometimes too large for transportation with forced resonance method [48]. Some non-contact techniques such as Digital Image Correlation (DIC), Global Positioning System (GPS) receivers and laser Doppler vibrometer (LDV) have also been adopted for the vibration testing of structures of bridges. However, large amount of data generated during measurement could be a main problem limited by computing power and memory available. Furthermore, the reduced testing time and appropriate measurement parameters can be difficult in structures subjected to random loading, and the relation between the external forces and the displacements is not well available [49–51].
Fig. 1. Experimental setup [4].
Fig. 2. Work-flow of optimizing the FEM model.
Fig. 3. FEM model. Table 1 Parameters of FEM. Parameters
Values
Length, L Width, W Young’s Modulus, Y Poisson’s Ratio, P
200 mm 100 mm 25GPa 0.15
2. Experiment This measurement is set to observe and investigate arched structural deformation characterization [4]. In Fig. 1, we install the digital camera which takes the photo of arch and TLS which collect the point cloud on the top. There are several triangular displacement sensors under the concrete arch to record the displacement. Two cylinders are fixed at both sides of the arch structure for loading. The measured arch is cautiously inspected at every load step, and the loads are measured by force sensors. 3. FEM construction In the experiment, we attempt to investigate influence of load variation on the maximum displacement of arch. Measurements are executed for assessing the realistic spatial resolution of a TLS as a function of the sampling procedure in optimal observation. It should be noted that concrete is not a quasi-lambertian reflector for a typical TLS, where lambertian reflectance is the property that defines an ideal “matte” or 435
Composite Structures 212 (2019) 434–438
H. Yang et al.
Fig. 4. Feature points coordinates of arch points cloud.
expensive, and sometimes don’t model the circumstances of the factual conformation exactly. On another hand, the TLS measurement provides a high accurate and efficient data about the monitoring object. This measurement, which could be surface based, is also able to be contrasted to the prediction of the FEM model. The requirement of experimental research continues within the frame of optimizing a versatile FEM model for concrete arch construction. We present our work-flow in detail in Fig. 2. Firstly, the point cloud is extracted from TLS measurement, then we deal with the data as the steps of point cloud processing, deformation recognition and deformation pattern. After FEM modeling and meshing, we will get a theoretical deformation pattern. With the comparison of experimental deformation pattern and theoretical deformation pattern, we will gain an optimized FEM model. In this paper, the focus mainly lies on the deformation behavior of the test specimen and FEM analysis of displacements and stresses of concrete arch.
Fig. 5. 3D point cloud extraction of arched structure [4].
3.2. Data extraction The TLS measurement collects the information of point clouds, which includes the values of x, y, z coordinate and intensity. We obtain the intensity value of the laser reflected from the loaded arch. Then we are able to extract the information from the point clouds. The points cloud is a three-dimensional model with discrete data. However, it is necessary to obtain continuous surfaces in order to mesh the arch structure. The FEM model is reconstructed from the 3D point clouds of TLS which is presented in Fig. 3. According to Fig. 3, the blue part are 3D point clouds exacted by TLS measurement, and geomagic is adopted to build the FEM model. The Parameters of FEM is present in Table 1 where the length L and width W are 200 and 100 mm. The Young's Modulus Y is 25 GPa and Poisson's Ratio P is 0.15.
Fig. 6. Deformation simulation of arched structure.
diffusely reflecting surface. 4. Analysis 3.1. Framework The TLS measurement collects the information from point clouds, which includes the values of x, y and z coordinates and intensity. We obtain the intensity value of the laser reflected from the loaded arched structure. We can then extract the information from the point clouds. Thus, we can obtain the feature point coordinates of the arch’s point cloud in Fig. 4. Therefore, the high precision distances between points
This paper takes advantage of FEM to check and verify the experimental results, which is obtained by TLS measurement about the deformation of concrete arch. On the one hand, the reliable numerical models, for example FEM models, is able to decrease the quantity of experiments, acknowledging that most of the experiments are 436
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Fig. 7. The DOF solution of arch deformation.
The DOF solution is presented in Fig. 7, in which the NODAL solution is adopted, where (a), (b) and (c) correspond to the X, Y and Z components of displacement and (d) is the sum of the displacement vector.
can be gained. The simulation of 3D displacement and stress are presented where the deviation is acceptable to confirm whether the model is appropriate [4] (Fig. 5). Aiming at simulating the stress and displacement of an arched structure, we construct a corresponding FEM model which can be used to analyze the deformation behavior. In this simulation, we use the computer with a 32 GB RAM, 64-bit operating system and the computation time is approximately 30 min, which depends on the element of mesh and the type of refinement.
6. Conclusions The TLS is an indispensable technique to monitor the deformation of constructs and ensure structure safety with better accuracy, efficiency and flexibility. Combined with the FEM model, a prediction of structure deformation is available which could generate an evaluation report about a structure’s safety level and protection methods. The goal of this paper is to generate a reasonable and reliable FEM model that could improve the health monitoring from “real-time monitoring” to “multi-temporal prediction.” In this article, the FEM model of the arched structure is established and calibrated with TLS measurements that can obtain 3D point cloud data which could describe the constructs’ 3D coordinates distinctly. Within this paper, the deformation behavior of arched structures is analyzed based on ANSYS simulation and prediction with the calibration of TLS measurement. The ANSYS FEM was employed to perform a nonlinear analysis with a Solid 65 element. The carrying capacity, deformation character and mechanical properties of arched structures are investigated using the FEM simulation and the model predicates that the arched structure will be outside the safety level when the load
5. Results The deformed sharp is present in Fig. 6, and the write part describes the deformation where the middle of the arch is maximum different displacements. According to Fig. 6, the value of the maximum displacement DMX is 11.18 mm, which is smaller than the experiment value of 17.76 mm. Considering the displacement sedimentation caused by the deformation of the foundation, especially on elastic ground, the left side support is not absolutely fixed. Therefore, the lateral displacement will contribute part of the deformation of the arched structure, which would increase the vertical displacement of the arch. However, both supports are displacement constraints in the FEM simulation. Nevertheless, the displacement compensation analysis will be adopted. 437
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increase is doubled. Until the load is two times more the arched structure will fail. The experimental results show that the theoretical model presented in this article is effective for the prediction, which indicates that the dynamic model is valid and the simulation analysis is reliable.
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Acknowledgments
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The publication of this article was funded by the support of Natural Science Foundation of Jiangsu Province (No: BK20160558). The authors gratefully acknowledge the support of Massivbau Institute to this research work.
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Conflicts of interest [28]
The authors declare no conflict of interest.
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Appendix A. Supplementary data
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Supplementary data to this article can be found online at https:// doi.org/10.1016/j.compstruct.2019.01.047.
[32]
References
[33]
[1] Park HS, Lee HM, Adeli H, Lee I. A new approach for health monitoring of structures: terrestrial laser scanning. Comput-Aided Civ Infrastruct Eng 2007;22:19–30. [2] Yang H, Xu X, Neumann I. Laser Scanning-Based Updating of a Finite-Element Model for Structural Health Monitoring. IEEE sensors; 2016. p. 2100–4. [3] Xu X, Yang H, Zhang Y, Neumann I. Intelligent 3D data extraction method for deformation analysis of composite structures. Compos Struct 2018;203:254–8. [4] Yang H, Omidalizarandi M, Xu X, Neumann I. Terrestrial laser scanning technology for deformation monitoring and surface modeling of arch structures. Compos Struct 2017;169:173–9. [5] Xu X, Yang H. Network method for deformation analysis of three-dimensional point cloud with terrestrial laser scanning sensor. Int J Distrib Sens Netw 2018;14. [6] Yang H, Xu X, Neumann I. Optimal finite element model with response surface methodology for concrete structures based on Terrestrial Laser Scanning technology. Compos Struct 2016. https://doi.org/10.1016/j.compstruct.2016.11.012. [7] Xu X, Bureick J, Yang H, Neumann I. TLS-based composite structure deformation analysis validated with laser tracker. Compos Struct 2018;202:60–5. [8] Yang H, Xu X, Xu W, Neumann I. Terrestrial laser scanning-based deformation analysis for arch and beam structures. IEEE Sens J 2017;17:4605–11. [9] Xu X, Yang H, Neumann I. Monotonic loads experiment investigate of composite structure based on terrestrial laser scanner measurement. Compos Struct 2018;183:563–7. [10] Tuno N, Mulahusic A, Kogoj D. Improving the positional accuracy of digital cadastral maps through optimal geometric transformation. J Surv Eng 2017;143. [11] Yang H, Xu X, Neumann I. The benefit of 3d laser scanning technology in the generation and calibration of FEM models for health assessment of concrete structures. Sensors 2014;14(11):21889–904. [12] Xu X, Zhao X, Yang H, Neumann I. TLS-based feature extraction and 3D modeling for arch structures. J Sensors 2017. Article ID 9124254. [13] Xu X, Yang H, Neumann I. Time-efficient filtering method for three-dimensional point clouds data of tunnel structures. Adv Mech Eng 2018;10(5):1–6. [14] Xu X, Kargoll B, Bureick J, Yang H, Neumann I. TLS-based profile model analysis of major composite structures with robust B-spline method. Compos Struct 2017;184:814–20. [15] Yang H, Xu X, Neumann I. Deformation behavior analysis of composite structures under monotonic loads based on terrestrial laser scanning technology. Compos Struct 2018;183:594–9. [16] Xu X, Yang H, Neumann I. A feature extraction method for deformation analysis of large-scale composite structures based on TLS measurement. Compos Struct 2018;184:591–6. [17] Xu X, Yang H, Neumann I. Deformation monitoring of typical composite structures based on terrestrial laser scanning technology. Composite Struct, Compos Struct 2018;202:77–81. [18] Wilkinson MW, Jones RR, Woods CE. A comparison of terrestrial laser scanning and structure-from-motion photogrammetry as methods for digital outcrop acquisition. Geosphere 2016;12:1865–80. [19] Wei Xu, Xu X, Yang H, Neumann I. Optimized finite element analysis model based on terrestrial laser scanning data. Compos Struct 2019;207:62–71. [20] Rodriguez-Gonzalvez P, Jimenez Fernandez-Palacios B, Luis Munoz-Nieto A. Mobile LiDAR system: new possibilities for the documentation and dissemination of large cultural heritage sites. Remote Sens 2017;9. [21] Moldovan N, Kulkarni S, Ferrari M. Use of laser scanning cytometry for analysis of
[34] [35]
[36] [37]
[38]
[39]
[40] [41]
[42]
[43]
[44]
[45] [46]
[47] [48] [49]
[50]
[51]
438
endothelial cells attached to micropatterned silicon surfaces. Sens Mater 2002;14:179–87. Oberlander M, Bartsch K, Schulte C. Process monitoring of laser remote cutting of carbon fiber reinforced plastics by means of reflecting laser radiation. J Laser Appl 2017;29(022009). Xu X, Kargoll B, Bureick J, Yang H, Neumann I. An automatic and intelligent optimal surface modeling method for composite tunnel structures. Compos Struct 2019;208:702–10. Kitratporn Nuntikorn, Takeuchi Wataru, Matsumoto Koji. Structure deformation measurement with terrestrial laser scanner at pathein bridge in Myanmar. J Disaster Res 2018;13:40–9. Hansen M, Piehler J, Kapphahn G. Systemanalyse neugotischer Gewölbe, 8. Mauerwerkskalendertag, Dresden, March 24, 2015. Hansen M, Schmidt B, Kelma S, Schmoor K, Goretzka J. Probabilistic assessment of the foundation of offshore wind turbines. Proceedings of the IABSE Conference, Elegance in Structures. 2015. Banerjee U, Osborn J. Generalized finite element methods: main ideas, results, and perspective. Int J Comput Methods 2004:67–103. Schmalz T, Buhl V, Eichhorn A. An adaptive kalman-filtering approach for the calibration of finite difference models of mass movements. J Appl Geod 2010:127–35. Wang Yi, Bo Yu, Sun Shuyu. Fast prediction method for steady-state heat convection. Chem Eng Technol 2012;35(4):668–78. Ganapuram S, Adams M, Patnaik A. Quantification of cracks in concrete bridge decks in Ohio district 3. Akron, American: The University of Akron; 2012. Kang DS, Lee HM, Park HS, Lee I. Computing method for estimating strain and stress of steel beams using terrestrial laser scanning and FEM. Key Eng Mater 2007;347:517–22. Kumar S, Bag S, Baruah M. Finite element model for femtosecond laser pulse heating using dual phase lag effect. J Laser Appl 2016;28(032008). Heunecke O. Zur Identifikation und Verifikation von Deformations-prozessen mittels adaptiver KALMAN-Filterung (Hannoversches Filter). Hanover, Germany: Leibniz Universität Hannover; 1995. Thesis. Gülal E. Geodätische Überwachung einer Talsperre: eine Anwendung der KALMANFiltertechnik. Thesis: Leibniz Universität Hannover, Hanover, Germany; 1997. Eichhorn A. Ein Betrag zur Identifikation von dynamischen Strukturmodellen mit Methoden der adaptiven KALMAN-Filterung. Stuttgart, Germany: Universität Stuttgart; 2005. Ph. D thesis. Lienhart W. Analysis of inhomogeneous structural monitoring data. Granz, Austria: Graz University of Technology; 2007. Ph. D thesis. Becker T, Weisbrich S, Euteneuer F, Wu CC, Neitzel F. Neue Möglichkeiten in der Bauwerksüberwachung durch integrierte Analyse von Sensormessungen und 3DBauwerksmodell. In: DGPF Proceedings of Sensors, Measurement and Testing Techniques, March 26–28, Hamburg, 2014. Cheng-Chieh Wu, Sven Weisbrich and Frank Neitzel. Materials Today: Proceedings. Volume 3, Issue 4, 2016, Pages 1211-1215. Inverse Finite Element Adjustment of Material Parameters from Integrated Analysis of Displacement Field Measurement. Sanayei M, Phelps JE, Sipple JD, Bell ES, Brenner BR. Instrumentation, nondestructive testing, and finite-element model updating for bridge evaluation using strain measurements. J Bridge Eng 2012;17(1):130–8. Čecháková V, Rosmanit M, Fojtik R. FEM modeling and experimental tests of timber bridge structure. Procedia Eng 2012;40:79–84. Gasco F, Feraboli P, Braun J, Smith J, Stickler P, DeOto L. Wireless strain measurement for structural testing and health monitoring of carbon fiber composites. Compos A Appl Sci Manuf 2011;42(9):1263–74. Yang Y, Liu D, He Z, Luo Z. Optimization of preform shapes by RSM and FEM to improve deformation homogeneity in aerospace forgings. Chin J Aeronaut 2010;23(2):260–7. Becker T, Weisbrich S, Euteneuer F, Wu CC, Neitzel F. Neue Möglichkeiten in der Bauwerksüberwachung durch integrierte Analyse von Sensormessungen und 3DBauwerksmodell. Gemeinsame Tagung 2014 der DGfK, der DGPF, der GfGI und des GiN (DGPF Tagungsband 23/2014), 2014. Ribeiro D, Calçada R, Delgado R, Brehm M, Zabel V. Finite element model updating of a bowstring-arch railway bridge based on experimental modal parameters. Eng Struct July 2012;40:413–35. Aras F, Krstevska L, Altay G, Tashkov L. Experimental and numerical modal analyses of a historical masonry palace. Constr Build Mater 2011;25(1):81–91. Foti Dora, Diaferio Mariella, Giannoccaro Nicola Ivan. Michele Mongelli.Ambient vibration testing, dynamic identification and model updating of a historic tower. NDT E Int 2012;47:88–95. Sevim Bariş. Finite element model calibration of berke arch dam using operationalmodal testing. J Vibr Control 2011;17. pp. 7 1065-1079. Zong ZH, Ren WX. Finite element model updating and model validation of bridge structures. China: People's Commumication Press; 2012. Beberniss Timothy J, Ehrhardt David A. High-speed 3D digital image correlation vibration measurement: Recent advancements and noted limitations. Mech Syst Signal Process 2017;86(Part B):35–48. Reu Phillip L, Rohe Daniel P, Jacobs Laura D. Comparison of DIC and LDV for practical vibration and modal measurements. Mech Syst Signal Process 2017;86(Part B):2–16. Nakamura S. GPS measurement of wind-induced suspension bridge girder displacements. J Struct Eng 2000;126(12):1413–9.