Chapter 17
Structural Complexity in Structural Health Monitoring: Design of Laboratory Model and Test Plan Craig J. L. Cowled, David P. Thambiratnam, Tommy H. T. Chan and Andy C. C. Tan
Abstract Many researchers in the field of civil structural health monitoring (SHM) have developed and tested their methods on simple to moderately complex laboratory structures such as beams, plates, frames, and trusses. Fieldwork has also been conducted by many researchers and practitioners on more complex operating bridges. Most laboratory structures do not adequately replicate the complexity of truss bridges. Informed by a brief review of the literature, this paper documents the design and proposed test plan of a structurally complex laboratory bridge model that has been specifically designed for the purpose of SHM research. Preliminary results have been presented in the companion paper.
17.1 Introduction The structural complexity of operational bridges poses a significant challenge for structural health monitoring (SHM) researchers. Reference [1] highlights that a ‘primary source of epistemic uncertainty [in structural identification] is related to the relatively high level of structural complexity typical of constructed systems,’ (p. 406). Ciloglu [2] found that structural complexity contributes significantly to the uncertainty of structural identification by operational modal analysis (OMA). Aktan et al. [3] argue that the basic assumptions that enable system identification of structures do not hold true for more complex constructed systems, particularly in a climate with daily temperature fluctuations of more than 10 °C where temperature and humidity can have a significant effect on the vibration characteristics of an operational structure.
C. J. L. Cowled (&) D. P. Thambiratnam T. H. T. Chan A. C. C. Tan Queensland University of Technology, 2 George Street, Brisbane, QLD 4001, Australia e-mail:
[email protected]
W. B. Lee et al. (eds.), Proceedings of the 7th World Congress on Engineering Asset Management (WCEAM 2012), Lecture Notes in Mechanical Engineering, DOI: 10.1007/978-3-319-06966-1_17, Ó Springer International Publishing Switzerland 2015
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Considering that one of the key goals of the concerted worldwide research efforts into SHM and damage detection (DD) is to be able to monitor complex operational structures and diagnose their health state, it stands to reason that experimental testing should likewise focus on similarly complex laboratory structures. Tee et al. [4] have noted a trend in research toward increasingly ‘large systems with many unknown parameters,’ (p. 398). Sami Masri (as cited in [5]) has called for multiple benchmark problems with increasing complexity. The evidence in the literature therefore supports a move toward using more complex structures in the laboratory for SHM research. This paper reviews some of the structures in the SHM literature and presents a structurally complex model of a bridge structure that has been designed by the primary author, fabricated by Taringa Steel Pty. Ltd. and assembled at Queensland University of Technology (QUT). This bridge model has been designed as a benchmark structure for SHM researchers at QUT to test DD methods and conduct SHM research. This paper will also outline the proposed test plan of the QUT Benchmark Structure. Preliminary results will be presented in a companion paper [6].
17.2 Structural Complexity in the Literature 17.2.1 Introduction The structural complexity in the SHM literature is first reviewed in this section. The review is divided into three types of structures, namely numerical models, laboratory structures, and operational structures. The QUT Benchmark Structure is then presented as the most complex laboratory structure of its kind. The term ‘structural complexity’ is used in this paper to encompass the complexities of geometry, connections, and boundary conditions.
17.2.2 SHM of Numerical Models Rather than spending time and money on laboratory experiments or fieldwork, most SHM researchers employ numerical models to test their DD methods. These numerical simulations often attempt to replicate real-world environmental and operational variations and measurement noise by introducing random noise of some kind into the data. When testing their DD methods, these researchers tend to use simple models, often in two dimensions only. Common examples include cantilevered beams, plates, simple frames, and simple trusses. This approach is understandably necessary for the initial development of a new method of DD. As the method is refined and developed further, however, it can be argued that it ought to be tested against
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increasingly complex models as a test of its robustness. Given that the complexity of numerical models is limited only by one’s imagination and the processing power of one’s computer, it is somewhat surprising that relatively few researchers attempt to test their methods on structurally complex models. Following is a review of the literature focusing on the degree of complexity in numerical models. Yan et al. [7] used a modal strain energy sensitivity-based method on three numerical models: a simply supported beam (15 elements), a continuous beam (30 elements), and a three-storey planar frame (33 elements). Mehrjoo et al. [8] conducted a numerical analysis using artificial neural networks (ANN) to detect damage at the connections of two truss structures. Both structures were planar trusses with 7 members in the first structure and 29 members in the second structure. Yan et al. [9] tested their wavelet-based DD method on a numerical model of a three-dimensional, five-storey structure with 55 members. Rama Mohan Rao et al. [10] developed a DD technique combining proper orthogonal decomposition with a self-adaptive differential evolution algorithm which was tested on numerical models of a cantilevered beam (20 elements), a simply supported plate (342 elements), and a planar truss having 55 members. All of the aforementioned examples have used relatively simple numerical models, often limited to two dimensions, to test their DD methods.
17.2.3 SHM of Laboratory Structures When testing their DD methods in the laboratory, most researchers tend to use structures that are simple to moderately complex. There are very few examples of structurally complex laboratory structures in the SHM literature. Following is a review of the literature focusing on the degree of complexity in laboratory structures. Samali et al. [11] conducted a detailed study of a laboratory timber structure that consisted of four F11 radiata pine girders and an F11 structural plywood deck (4.5 m 9 2.4 m 9 0.1 m). Papatheou et al. [12] investigated the feasibility of using known masses to guide feature extraction on an aluminum structure consisting of two C section ribs, two angle section stringers and a 3-mm-thick plate (0.75 m 9 0.5 m 9 0.05 m). Quek et al. [13] tested the damage locating vector (DLV) method on an aluminum space truss with 24 members (3 m 9 1 m 9 0.7 m). Weber and Paultre [14] used a sensitivity-based damage identification method to detect damage on a 112 member tower truss (0.3 m 9 0.3 m 9 2.8 m). Gao et al. [15] conducted experimental verification of the DLV method on a 156 member space truss. Meruane and Heylen [16] investigated the use of antiresonances in DD on a three-dimensional aluminum space truss with 43 members (3 m 9 0.5 m 9 0.5 m). Dackermann et al. [17] used neural network ensembles and the conventional damage index method in their numerical (200 elements) and experimental study of simply supported steel beams (2.4 m 9 0.032 m 9 0.012 m). Catbas et al. [18] presented a
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benchmark problem intended to represent a large proportion of the bridge stock, namely short- to medium-span highway bridges. The 15-member steel structure, located at the University of Central Florida, consists of two parallel longitudinal beams connected by seven transverse beams and supported on six columns (5.48 m 9 1.83 m 9 1.07 m). The finite element (FE) model of this moderately complex structure consists of 181 elements and 1,056 DOFs. Johnson et al. [19] introduced a benchmark problem based on the complex four-storey steel frame structure (2.5 m 9 2.5 m 9 3.6 m) located at the University of British Columbia. This structure has 112 members. Ciloglu [2] conducted detailed structural identification of a simply supported steel grid with composite sandwich plate deck (6.1 m 9 2.75 m) supported by six steel pedestal frames. The steel grid consisted of three longitudinal beams and 14 transverse members made from rectangular hollow steel sections bolted together with gusset plates. The composite deck consisted of a sandwich plate with a balsa wood core and E-glass sheets top and bottom bonded together with a vinyl ester resin. The boundary conditions of the fabricated steel and composite deck consisted of roller bearings fixed to the pedestal frames. The structure is located in the Drexel Intelligent Infrastructure Laboratory in Philadelphia, PA. Caicedo [20] undertook an experimental study to verify the parameter identification SHM technique, using a stainless steel model of a cable-stayed bridge structure (2 m 9 0.29 m 9 0.5 m). The main tower of the structure is an H frame and holds 60 cables that support the deck. The deck consists of 30 transverse members, 60 longitudinal members, and 28 lead masses. Although the structure has 379 DOFs, only the last six elements of the structure, totaling just 12 DOFs, were studied. All of the aforementioned examples demonstrate that researchers have used laboratory structures that are relatively simple to moderately complex in their research.
17.2.4 SHM of Operational Structures There are a wide variety of operational bridge structures that have been instrumented, analyzed and reported in the SHM literature, ranging from relatively simple short-span slab on girder bridges to extremely complex truss and suspension structures. Following is a review of the literature focusing on the degree of complexity in operational structures. Thambiratnam [21] conducted a study of the vibration characteristics of an approach span of the Story Bridge in Brisbane, a deck on simply supported steel truss structure, and compared the results with an FE model to show that the structure was operating within limits for strength and serviceability. James et al. [22] detail tests and present some results of DD on the I40 Bridge over the Rio Grande in Albuquerque, New Mexico, USA. This three-span steel plate girder bridge was subjected to various damage scenarios. Krämer et al. [23] presented DD tests on the widely documented Z24 Bridge in Switzerland. This three-span prestressed concrete box
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girder bridge became a benchmark structure [24]. Heywood et al. [25] used strain and deflection data along with a code-based analysis to assess the fitness-for-purpose of four steel bridges, of various configurations, in Australia and New Zealand. Senthilvasan et al. [26] tested the dynamic response of a multi-span-curved prestressed concrete box girder bridge under a truck load. Results were used to validate a FE model and to compare the experimentally determined dynamic strains with code predictions. It was evident that the code predictions underestimated the dynamic strains. Catbas et al. [27] conducted structural identification studies on the Commodore Barry Bridge spanning the Delaware River in the USA. Agrawal et al. [28] introduced the 91/5 highway bridge as a benchmark structure. This structure is a prestressed concrete box girder bridge with two spans. The FE model of the actual structure is very simple; however, the boundary conditions are quite complex. Chan et al. [29] describe the extensive wind and structural health monitoring system (WASHMS) installed on the famous Tsing Ma Bridge in Hong Kong, a longspanning suspension bridge. The WASHMS is described as the most comprehensive SHM system installed on any operational structure in the world. All these examples demonstrate that operational structures in the SHM literature tend to be far more structurally complex than numerical models or experimental structures in the literature.
17.2.5 Structural Complexity of the QUT Benchmark Structure The most structurally complex laboratory structure in the SHM literature is the one that has been designed, fabricated, and assembled for this study (8.55 m 9 0.9 m 9 2.6 m, see Fig. 17.1 overleaf). This structure is esthetically similar to Brisbane’s Story Bridge, which is a long-span cantilevered steel through truss bridge. Not counting gusset plates, the deck plate or support members, the superstructure of this bridge model has 314 members in a configuration with 100 nodes. There are 600 DOFs in the superstructure plus additional DOFs in the supports. The first mention of this bridge model in the literature can be found in [30] where a FE model of the structure was developed to test a correlation-based DD method using modal strain energy and optimized via a multilayer genetic algorithm.
17.2.6 Conclusion This section of the paper constituted a review of structural complexity in the literature of numerical models, laboratory structures, and operational structures. This review confirmed the gap in structural complexity between examples of
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Fig. 17.1 The QUT Benchmark structure
numerical models / laboratory structures in the literature and operational structures in the literature. Finally, this review presented the QUT Benchmark Structure as the most structurally complex steel through truss bridge model in the SHM literature, having complexities of geometry, connection detailing, and boundary conditions.
17.3 QUT Benchmark Structure Benchmark problems in SHM allow researchers to objectively compare and contrast DD methods and have also been shown to highlight the limitations of current techniques [12]. The previous section of this paper advocated the need for increasing structural complexity in laboratory structures in the field of SHM research. This section of the paper outlines and discusses the design, specifications, and assembly of the QUT Benchmark Structure (see Fig. 17.1). The 600+ DOF bridge model is structurally complex and has been designed for the purpose of studying SHM and DD. The structure is raised approximately 800 mm off the concrete slab to allow room for static load tests and modal tests using a shaker.
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17.3.1 Design The six main criteria that guided the concept design of the structure were as follows: (1) complex structural form; (2) dynamic characteristics that can be captured by standard sensors and data acquisition equipment at reasonable sampling rates; (3) structural steel materials; (4) bolted connection details; (5) modular design; and (6) detailing that facilitates simulation of damage scenarios. The geometry of this structure was chosen to resemble the main bridge span of Brisbane’s iconic Story Bridge, a cantilevered steel through truss bridge with suspended span. The geometry is also similar to that of the Commodore Barry Bridge, spanning the Delaware River in the USA, which has been extensively studied (e.g., [26]). The structure was designed in accordance with Australian Standard AS4100-1998 [32].
17.3.2 Specifications A breakdown of the materials that were used in the structure can be found in Table 17.1.
17.4 Proposed Test Plan Considering the intent to present the structurally complex laboratory structure as a benchmark structure for SHM research, it is prudent to conduct parallel tests using a variety of different methods. Both experimental modal analysis (EMA) and OMA will be examined using two different kinds of excitation within each method. The EMA tests will employ the use of an impact hammer and a shaker as alternative excitation tools. The OMA tests will employ the use of fans and simulated traffic as alternative excitation tools. In order to reduce the effects of noise and nonlinearities in the results, the tests will be repeated and averaged. The number of repeat tests is expected to be in the order of 10–30; however, the final number will be determined at the time of testing by analyzing the averaged frequency response functions and applying a cost/ benefit criteria. The modal information will be used, in conjunction with a suitable FE model updating method, to validate the FE model of the structure for the purpose of establishing a baseline model of the undamaged structure. A static load test of the undamaged structure, within serviceability limits, will also be conducted to help validate the FE model. Once the structural identification process is complete, progressive damage scenarios will be applied to the structure and the cycle of testing will proceed.
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Table 17.1 Materials Member type
Qty
Material
Size (mm section)
Comments
Deck Deck beams Chords, webs, and struts Tower frames
–
850 9 5.0 PL 50 9 25 9 2.0 RHS 20 9 1.6 SHS
Future installation
21 215
MS grade 250 MS grade 350 MS grade 350
2
MS grade 350
2/30 9 3.0 SHS 2/20 9 1.6 SHS 20 9 3.0 FL Half 250UB31 2/100 9 6.0 FL Half 250UB31 4/100 9 6.0 FL 200 9 200 9 10 PL 3.0 PL/FL
Fully welded
Various details
5.0 FL
Near tower frames
M6
+ nuts and split washers + nuts and split washers
Bracing Static loading points Supports
Gusset plates, cleats, and capping plates Cleats for high load Bolts
70
MS MS MS 8 MS MS MS *900 MS 6
20
250 300+ 250 300+ 250 250 250
MS grade 250
*800 MS 4.6/S
Bolts for high load Screws Plinth beams
grade grade grade grade grade grade grade
4
32
MS 4.6/S
M8
80
Zinc-Plated Teks MS grade 300+ Neoprene MS 4.6/S
6g 310UC96.8 2 layers 9 3.0 mm 6/M16
Hilti chemical anchors
6/HIS-N M16x170 ferrules
Welded to detail +6/M20 pins Welded to detail +8/M20 pins
10 PL stiffeners For damping Hand tightened with extension bar Anchored to RC substrate by Hilti HIT-HY 150 Max
Notes MS mild steel, PL plate, FL flat bar, RHS rectangular hollow section, SHS square hollow section, UB universal beam, UC universal column, RC reinforced concrete
Some proposed damage scenarios include the following: progressive loss of crosssectional area on a deck beam or truss member to mimic corrosion; buckling of a compression truss member; progressive loosening of bolts at connections; growing crack in a cleat or gusset plate; fatigue failure of a weld; complete failure of a bracing member; impact damage; differential settlement at supports; and altered stiffness at supports to mimic bearing seizure or failure. The data from the various damage scenarios will be analyzed using a number of different DD methods. Modal flexibility and modal strain energy methods show some promise for application to steel truss structures such as this one [31, 33].
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17.5 Conclusion This paper has presented a review of structural complexity in the SHM literature that identifies a gap between the structural complexity of numerical models and laboratory structures compared with the structural complexity of operational structures, particularly steel truss bridges. The QUT Benchmark Structure was presented as the most complex purpose-built laboratory structure of its kind, designed for research into DD and SHM. The proposed test plan was also outlined in brief. Acknowledgments This paper was developed within the CRC for Infrastructure and Engineering Asset Management, established, and supported under the Australian Government’s Cooperative Research Centres Programme. The primary author is a postgraduate student, studying at Queensland University of Technology, Brisbane. The primary author wishes to acknowledge the Australian Research Council for providing a living allowance scholarship, and the Cooperative Research Centre for Integrated Engineering Asset Management, Queensland Department of Transport and Main Roads and Brisbane City Council for providing top-up scholarships. Taringa Steel Pty. Ltd. is also acknowledged for their high-quality craftsmanship in the fabrication of the bridge model.
References 1. Moon FL, Aktan AE (2006) Impacts of epistemic (bias) uncertainty on structural identification of constructed (civil) systems. Shock Vib Digest 38(5):399–420 2. Ciloglu SK (2006) The impact of uncertainty in operational modal analysis for structural identification of constructed systems. Dissertation, Drexel University 3. Aktan AE, Ciloglu SK, Grimmelsman KA et al (2005) Opportunities and challenges in health monitoring of constructed systems by modal analysis. In: Proceedings of the international conference on experimental vibration analysis for civil engineering structures, Bordeaux, France, 26–28 Oct 2005 4. Tee KF, Koh CG, Quek ST (2009) Numerical and experimental studies of a substructural identification strategy. Struct Health Monit 8(5):397–410 5. Catbas FN (2004) Meeting of the bridge health monitoring committee of international association of bridge maintenance and safety. Meeting Minutes, Kyoto, Japan, 21 Oct 2004 6. Cowled CJL, Thambiratnam DP, Chan THT, Tan ACC (2012) Structural complexity in structural health monitoring: preliminary experimental modal testing and analysis. Paper presented at the 7th world congress on engineering asset management, Daejeon, Korea, 8–10 Oct 2012 7. Yan W-J, Huang T-L, Ren W-X (2010) Damage detection method based on element modal strain energy sensitivity. Adv Struct Eng 13(6):1075–1088 8. Mehrjoo M, Khaji N, Moharrami H, Bahreininejad A (2008) Damage detection of truss bridge joints using artificial neural networks. Expert Syst Appl 35(3):1122–1131 9. Yan G, Duan Z, Ou J, De Stefano A (2010) Structural damage detection using residual forces based on wavelet transform. Mech Syst Signal Process 24(1):224–239 10. Rama Mohan Rao A, Lakshmi K, Venkatachalam D (2012) Damage diagnostic technique for structural health monitoring using POD and self adaptive differential evolution algorithm. Comput Struct 106–107:228–244
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11. Samali B, Li J, Choi FC, Crews K (2010) Application of the damage index method for platelike structures to timber bridges. Struct Control Health Monit 17(8):849–871 12. Papatheou E, Manson G, Barthorpe RJ, Worden K (2010) The use of pseudo-faults for novelty detection in SHM. J Sound Vib 329(12):2349–2366 13. Quek ST, Tran VA, Hou XY, Duan WH (2009) Structural damage detection using enhanced damage locating vector method with limited wireless sensors. J Sound Vib 328(4–5):411–427 14. Weber B, Paultre P (2010) Damage identification in a truss tower by regularized model updating. J Struct Eng-ASCE 136(3):307–316 15. Gao Y, Spencer BF Jr, Bernal D (2004) Experimental verification of the damage locating vector method. In: Chang F-K, Yum CB, Spencer BF Jr (eds) Proceedings of the first international workshop on advanced smart materials and smart structures technology, Honolulu, Hawaii 16. Meruane V, Heylen W (2012) Structural damage assessment with antiresonances versus mode shapes using parallel genetic algorithms. Struct Control Health Monit 11(3):345–357 17. Dackermann U, Li J, Samali B (2010) Dynamic-based damage identification using neural network ensembles and damage index method. Adv Struct Eng 13(6):1001–1016 18. Catbas FN, Caicedo JM, Dyke SJ (2006) Development of a benchmark problem for bridge health monitoring. In: da Sousa Cruz PJ, Frangopol DM, Canhoto Neves LC (eds) Advances in bridge maintenance, safety management, and life-cycle performance: proceedings of the third international conference on bridge maintenance, safety and management, Porto, Portugal, 16–19 July 2006 19. Johnson EA, Lam HF, Katafygiotis LS, Beck JL (2004) Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data. J Eng Mech-ASCE 130(1):3–15 20. Caicedo JM (2003) Structural health monitoring of flexible civil structures. Dissertation, Washington University 21. Thambiratnam D (1995) Vibration analysis of Storey Bridge. Aust Civ Eng 37(2):91–97 22. James G, Mayes R, Carne T et al (1995) Health monitoring of operational structures: initial results. In: Proceedings of the 36th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, New Orleans, Louisiana, 10–12 Apr 1995 23. Krämer C, De Smet CAM, De Roeck G (1999) Z24 bridge damage detection tests. In: Proceedings of the 17th international modal analysis conference, vol 3727, Kissimmee, Florida, Feb 1999. SPIE, pp 1023–1029 24. Maeck J, De Roeck G (2003) Description of Z24 benchmark. Mech Syst Signal Process 17(1):127–131 25. Heywood R, Roberts W, Taylor R, Andersen R (2000) Fitness-for-purpose evaluation of bridges using health monitoring technology. Transport Res Rec 1696:193–201 26. Senthilvasan J, Thambiratnam DP, Brameld GH (2002) Dynamic response of a curved bridge under moving truck load. Eng Struct 24(10):1283–1293 27. Catbas FN, Grimmelsman KA, Aktan AE (2000) Structural identification of the Commodore Barry Bridge. In: Aktan AE, Gosselin SR (eds) Proceedings of SPIE: nondestructive evaluation of highways, utilities and pipelines IV, vol 3995, Newport Beach, California, Mar 2000. SPIE, pp 84–97 28. Agrawal A, Tan P, Nagarajaiah S, Zhang J (2009) Benchmark structural control problem for a seismically excited highway bridge—part I: phase I problem definition. Struct Control Health Monit 16(5):509–529 29. Chan THT, Wong KY, Li ZX, Ni Y-Q (2011) Structural health monitoring for long span bridges: Hong Kong experience and continuing onto Australia. In: Chan THT, Thambiratnam DP (eds) Structural health monitoring in Australia. Nova Publishers, Hauppage, New York, pp 1–32 30. Wang L, Chan THT, Thambiratnam DP et al (2012) Correlation-based damage detection for complicated truss bridges using multi-layer genetic algorithm. Adv Struct Eng 15(5):693–706
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31. Wang L (2012) Innovative damage assessment of steel truss bridges using modal strain energy correlation. Dissertation, Queensland University of Technology 32. Standards Association Australia (1998) AS4100-1998: steel structures. Standards Association Australia, Sydney 33. Shih HW, Thambiratnam DP, Chan THT (2009) Vibration based structural damage detection in flexural members using multi-criteria approach. J Sound Vib 323(3–5):645–661
