GSP219: Study of a Skewed HPS Bridge and a ...

Geotechnical Special Publication No. 219 © ASCE 2011

Study of a Skewed HPS Bridge and a Culvert Bridge Using LiDAR Scan Kaoshan Dai1,2, Chris Watson3, Haitao Bian3, Yonghong Tong3, and Shen-En Chen4, M. ASCE, P.E. 1

Research Scientist, Department of Civil and Environmental Engineering, University of North Carolina, Charlotte, 9201 University City Boulevard, Charlotte, NC, USA 28223. E-mail: [email protected]; 2 The State Key Laboratory for GeoMechanics and Deep Underground Engineering, Xuzhou, China 221008. 3 Research Assistant, Department of Civil and Environmental Engineering, University of North Carolina, Charlotte, 9201 University City Boulevard, Charlotte, NC, USA 28223. 4 Associate Professor, Department of Civil and Environmental Engineering, University of North Carolina, Charlotte, 9201 University City Boulevard, Charlotte, NC, USA 28223. E-mail: [email protected]

ABSTRACT: This paper summarizes two bridge studies: 1) testing and numerical modeling of a newly constructed skewed hybrid high-performance steel (HPS) bridge; and 2) testing of a culvert bridge near a rock blasting. The first study is for construction verification and the second study is for possible blast damage identification. In the first study, truck load tests were conducted on the first North Carolina HPS 100W girder bridge after construction completion. LiDAR scan technique was used to measure bridge displacements under truck loading. A detailed 3D finite element (FE) model of the bridge was established. Results obtained from numerical analyses were compared with static load tests. In the second study, 3D LiDAR scans of a culvert were performed before and after rock blasting. Possible bridge position changes and damages were investigated. This research demonstrates two applications of LiDAR scan for bridge condition assessment. INTRODUCTION Bridge maintenance, which typically replies on bridge inspection and structural evaluation, is crucial for public transportation safety. However, without detailed geometric imaging records, visual inspection alone cannot ensure that bridge condition has not been altered over time. This is a key element contributing to inspection errors observed in a study on bridge inspector practices (Moore et al. 2001). It is important to recognize that although direct measurements of surface area, depth, and location of defects are preferred (AASHTO 2003); it is not commonly performed during bridge inspection. Several image-based techniques are currently being considered for bridge inspections: Lee and Shinozuka (2006) used digital image processing techniques to

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measure dynamic displacement of bridges. Digital color image processing methods were developed by Lee et al. (2006) for steel bridge coating inspections. Nassif et al. (2005) compared dynamic measurements from a laser Doppler vibrometer system, linear variable differential transducer-cable system and geophone sensors. A laser range finder system, developed by FHWA, was used to measure bridge deflections on several bridges (Fuchs 2004a, 2004b): A baseline scan was conducted first with no loading on the bridge; then scans were taken while loading was present on the bridge. A comparison between two measurements then yielded bridge girder deflections under imposed loading. Recently, terrestrial laser scanners have been found to be useful for structural condition assessments: Teza et al. (2009) used a terrestrial laser scanner to evaluate mass loss of concrete and developed an automatic surface damage recognition method, which was successfully applied to a concrete bridge. This paper reports the application of the Light Detection And Ranging (LiDAR) system for two bridge studies. LiDAR is a type of terrestrial 3D laser scanner that can automatically acquire a 3D surface image of an object without physical contact. A 3D coordinate value is assigned to each physical point according to its relative position to the scanner. With a single full scan, the surface position information of the objects surrounding the scanner can be measured and recorded. LiDAR scan has several applications for bridge monitoring (Liu 2010). To demonstrate the applications, two bridge inspection case studies using the LiDAR scan are presented in this paper: 1) testing of a newly constructed hybrid, high-performance steel (HPS) girder bridge; and 2) testing of a culvert bridge near a rock blasting. The first study is for construction verification; LiDAR scan technique was used to obtain actual bridge girder curves under self-weight and girder displacement under static loads. In the second study, LiDAR scanner was used to collect image data of the culvert before and after construction rock blasts, which took place near the bridge. Through image analyses, structural evaluation was performed by comparing geometric parameters extracted from the images. TESTING OF A HPS BRIDGE A hybrid, high-performance steel girder bridge was load tested for construction validation (Scott 2010). Figure 1 shows a plan view of the bridge superstructure, where HPS 100W steel is used for girder flanges in the negative moment region (section AB) and the rest of steels used in girder are HPS 70W (sections A and B). The bridge is continuous across two equal length spans. The first span is labeled as span A and the second as span B. The clear span length between the centerline of the bearings is 147.0 feet (44.81 m). The total out-to-out width of the bridge is 90 feet-7 inches (27.61 m). The bridge is oriented on a large skew angle of 47o 37’ 30”. As labeled in Figure 1, there are nine girders in this bridge. Supports A and C are elastomeric expansion bearings with slotted holes for the anchor bolts used as end supports. Support B is a fixed pot bearing located on each intermediate pier. Static truck load testing was performed using two dump trucks. Both trucks, loaded with stones, were weighed approximately 55,000 lbs (24.96 t). The trucks were placed to create the maximum possible negative moment over the intermediate bent. Three different truck configurations (Figure 1) were used in load tests. Since the


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bridge is symmetric about its intermediate support, for simplicity, all of the cases of truck loads were placed on span B. Each truck pair was located at approximately 62 feet (18.9 m) from the centerline of the middle bridge support.

Figure 1. Plan view of the HPS bridge superstructure (US units) A commercial LiDAR system (Faro LS 880HE) was used in load tests: 1) to obtain actual geometrical information of the bridge for finite element model verification, and 2) to measure girder deflections under truck loads. First, a baseline scan of the bridge was created without vehicular loads on the bridge. The baseline scan can give actual girder cambering curves under bridge self-weight. Once the baseline scan was completed, the bridge was then loaded and rescanned. Girder deflections under different truck loads were determined by comparing the scan results without live load with those scans with live load. Three cable extension transducers were also used to measure girder deflections under each truck load configuration. The displacements obtained from the transducer measurements were compared with LiDAR scan results. Figure 2 shows the set-up of the LiDAR scan and the cable extensive transducer. A typical LiDAR scan image is also shown in the figure. NUMERICAL STUDY OF THE HPS BRIDGE A 3D finite element model for the bridge superstructure was developed by using ANSYS (2007). Steel girders, concrete deck, intermediate cross frames, end bent diaphragms, elastomeric expansion bearings, pot bearings, and non-structural components such as the concrete parapet walls, were modeled. Elements used in the model are summarized in Table 1. Steel modulus of elasticity is 29,000 ksi (199.95 Gpa) and density is 490 lb/ft3 (7849.05 kg/m3) in the model. For concrete deck, modulus of elasticity was estimated to be 4,132 ksi (28.9 Gpa) and the density is 150 lb/ft3(2402.77 kg/m3). Moduli of elasticity for the end support (elastomeric bearing) and the middle support (pot bearing) are 38,260 psi (263.79 Mpa) and 6,932 psi (47.79 Mpa), respectively. Rotational stiffness for the end support is 616,524 lb-inch (69.66 kN-m) for X direction (parallel to the girder) and is 369,914 lb-inch (41.79 kN-m) for Y direction (perpendicular to the girder). For the middle support, rotational stiffness is calculated as 4,260 lb-inch (481.32 N-m) for both X and Y directions.


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Static load analysis was performed under bridge self-weight. The deformation of the bridge superstructure was obtained and displayed in Figure 3. The numerical model was validated through comparing the girder elevation curves obtained from LiDAR scans to the FE analysis results. Figure 4 shows Girder 8 deformed shapes relative to the support B obtained from: FE analysis under self weight and the actual measurement using LiDAR. The deformed shape based off the design documentation is also shown in the figure. It can be seen that the overall Girder 8 deflected shapes from FE analysis and LiDAR measurements are very similar.

Figure 2. LiDAR scan and cable-extension transducer set-up

Figure 3. Superstructure deflections under non-composite dead load (unit: inch) Dead load FE analysis results were also compared to design calculations. Comparisons of Girder 8 maximum deflection and stress are summarized in Tables 2 and 3. The FE model predicts different maximum deflections for each span of the girder, which are smaller than the design values. FE analysis stresses are also lower


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than those of the design calculations. Other researchers (Mabsout et al. 2000) also found that the design method and the finite element analysis may predict different results. But overall, values in this paper are close. There are several possible reasons for the difference: 1) the FE model considered the vertical curvature and skew of the bridge while the design was conducted in two dimensions for a straight girder and did not account for this; 2) the design procedure itself had many safety factors built in that may lead to higher deflection estimates. Table 1. Elements used in the finite element model Elements BEAM188 SHELL63 MPC184 LINK8 LINK10 COMBIN14 MASS21

Bridge superstructure members Upper and bottom flanges of the bridge girders Channel members in the endbent diaphragms Concrete parapet walls along both edges of the bridge deck Steel girder webs Concrete deck Shear studs between the steel girders and the concrete deck Intermediate cross frame members T-section members in the endbent diaphragms Steel anchor bolts in the supports Elastomeric bearings in the end supports and the pot bearing in the middle support Rotation restrictions for the supports Precast concrete barriers along the middle of the bridge deck

Figure 4. Comparison of Girder 8 dead load deflections (unit: inch) The FE model was further validated by comparing the truck load test results with those from numerical analyses. Girder deflections under different load cases at locations where the transducers were attached are small. LiDAR scan testing in this study has the resolution of ±0.2 inch (±5 mm) for displacement measurement. It is found that out of the four measurements that are within this ±0.2 inch (±5 mm) range, three records are close between the LiDAR and the transducers. Although the FE model predicts slightly lower deflections than field test results for each load case, they appear to be close within the small deflection amplitude (Dai et al. 2010).


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The validity of the FE model was confirmed by the above comparisons. The FE model can be then used as a baseline model for further studies of this bridge. Along with this comparison, construction verification was completed. Table 2. Maximum dead load deflections of Girder 8 Span A maximum deflection 3.09 inches (78.49 mm)

Finite element analysis results Distance from Span B Support A maximum deflection 69.33 feet 2.35 inches (21.13 m) (59.69 mm)

Distance from Support C 64.71 feet (19.72 m)

Design calculations Maximum Location deflection from Support from design A 4.24 inches 58.78 feet (107.7 mm) (17.92 m)

Table 3. Maximum dead load stresses of Girder 8 Girder sections Section AB Section A (B)

Finite element analysis results Max tensile Max compressive 20.6 ksi 24.2 ksi (142 Mpa) (166.85 Mpa) 11 ksi 13.3 ksi (75.84 Mpa) (91.7 Mpa)

Design calculations Max tensile Max compressive 29.54 ksi 27.57 ksi (203.67 Mpa) (190.09 Mpa) 16.21 ksi 19.03 ksi (111.76 Mpa) (131.21 Mpa)

EXPERIMENTAL STUDY OF A CULVERT BRIDGE A rock blasting for utility line installation was conducted near a concrete culvert bridge located on the Colony Road at Charlotte, North Carolina. The blast occurred about 250 feet (76.2-m) east of the bridge. There were concerns that there may be damages to a culvert bridge due to the close proximity of the blasting. Field testing was performed using the same LiDAR system described earlier. To obtain full scale 3D images of the culvert, several reference spheres were placed at different locations surrounding the bridge. The reference spheres allowed the scans to be tied together into one coordinate system. Each set of scan data were collected both before and after blasting. Figure 5 (a) shows a typical pre-blast scan of one side of the culvert; while Figure 5 (b) shows an after-blast scan image of the same side of the bridge. For pre- and post- blast scan images to be compared, they have to be aligned. By using coordinate transformation functions, the second set of scans were made to match the first set. The point data from these images were reduced to only the points describing the concrete culvert. A surface wrap was created by taking the point cloud data to create a 3D shape, representing the curved surface of the culvert. The pre-blast surface wrap was then compared to the post-blast point cloud by finding the shortest distance to the surface wrap from each point. A 3D map of displacement with the preblast bridge as the reference was generated after this comparison process (Figure 6). After reviewing the results of the scans, there were no significant changes found to the bridge structure. Although some displacement measurements go beyond 0.75 inches (19 mm), most of them are small. There is no general trend to suggest that the structure has moved or has been damaged by the blasting. Although other techniques



may show minor damages, the scan image comparison suggests no structural movements occurred. It is concluded that there is no evidence of blast damage for this culvert based on the LiDAR observations.

Figure 5. Typical scan images: (a) pre-blast; (b) post-blast

Figure 6. Comparison between the pre- and post-blasting scans (unit: inch) CONCLUSIONS Using two case studies, applications of LiDAR scan for bridge structural inspections are demonstrated. In the new construction case, actual girder curves were obtained from scan image coordination information, which were then used to verify a FE model of the bridge superstructure. LiDAR scan was also used to measure girder deflections under truck loads. The comparison with transducer measurements and the original design showed that LiDAR scan is an effective technique for deformation measurements during load testing. In the second case, LiDAR scan technique was applied to assess blast effects on a culvert. Through comparing the pre- and post- blast measurements, no significant


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structural damages were found. It shows the potential use of LiDAR scan to generate baseline geometric images of a bridge for the intent of damage detection. REFERENCES AASHTO. (2003). Manual for condition evaluation and load and resistance factor rating (LRFR) of highway bridges. American Association of State Highway and Transportation Officials. The United States of America. ANSYS Inc. (2007). Release 11.0 documentation for ANSYS®. Computer Software. ANSYS, INC. Dai, K., Watson, C., Liu, W., Chen, S., and Hauser, E. (2010). “Validation of bridge girder deflection measurement using LiDAR scan. In: Proceedings for NDE/NDT for Highways and Bridges: Structural Materials Technology, New York, NY. Fuchs P.A., Washer G. A., Chase S. B., and Moore M. (2004a). “Laser-based instrumentation for bridge load testing”. Journal of Performance of Constructed Facilities, Vol. 18 (4): 213-219. Fuchs P.A., Washer G. A., Chase S. B., and Moore M. (2004b). “Applications of laser-based instrumentation for highway bridges”. Journal of Bridge Engineering, Vol. 9 (6): 541-549. Lee, S., Chang, L., and Skibniewski, M. (2006). “Automated recognition of surface defects using digital color image processing”. Automation in Construction, Vol. 15: 540 – 549. Lee, J. J. and Shinozuka, M. (2006). “A vision-based system for remote sensing of bridge displacement”. NDT&E International, Vol. 39: 425–431. Liu, C., Olund, J., Cardini, A., D’Attilio, P., Feldblum, E and DeWolf, J. (2008). “Structural health monitoring of bridges in the State of Connecticut”. Earthquake Engineering & Engineering Vibration, Vol.7: 427-437. Liu, W. (2010). Terrestrial LiDAR-based bridge evaluation. Ph.D. Dissertation, Department of Civil and Environmental Engineering, University of North Carolina at Charlotte. Mabsout, M., Jabakhanji, R., Tarhini, K., and Frederick, G. R. (2000). “Finite element analysis of concrete slab bridges”. In: Proceedings of the Eighth International Conference on Computing in Civil and Building Engineering. Fruchter, R. et al. ed., Stanford, California. Moore, M., Phares, B., Graybeal, B., Rolander, D. and Washer, G., (2001). Reliability of visual inspection for highway bridges, Vol. 1, FHWA-RD-01-020, FHWA. Scott, J. (2010). Construction and design validation of a hybrid high performance 100W steel girder bridge through welding documentation, static load testing and numerical modeling. M.S. Thesis, Department of Civil and Environmental Engineering, University of North Carolina at Charlotte. Scott, M., Rezaizadeh, A., Delahaza, A., Santos, C.G., Moore, M., Graybeal, B., and Washer, G. (2003). “A comparison of nondestructive evaluation methods for bridge deck assessment”. NDT&E International, Vol. 36: 245–255. Washer, G. (1999). “Developing NDT technologies for highway bridges”. Materials Evaluation, Vol. 57(11): 1151-1161.


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