This work discusses a laboratory resource and associated lecture material that are implemented in an interdisciplinary course ‘Smart Structures and Sensors.’ Instruction in structural behavior, sensor systems, and experimental systems issues is supported. The Smart Truss Bridge is a reconfigurable aluminum truss structure that is instrumented with strain sensors. It is designed to be representative of a full-scale steel bridge such that person loads produce reasonable strain levels. This multiple-bay truss can be configured with and without redundant members. Strain instrumentation with extrinsic Fabry-Perot Interferometric fiber optic sensors demonstrates the performance and use of sensor systems. Theoretical analysis and experimental measurement may be correlated for different load placements. Observations include elastic strain behavior as well as practical experimental issues such as noise and non-ideal connections. A smart structures case study shows the use of artificial neural networks in a monitoring application. The weight and location of a load are predicted using different combinations of member strain. The Smart Truss Bridge provides educational opportunities for students with different majors to investigate infrastructure technologies and to interact across the disciplines. The topics are relevant to current infrastructure research and problems.
Aktan, A.E., Helmicki, A.J. and Hunt, V.J.1998. ‘‘Issues in Health Monitoring for Intelligent Infrastructure,’’ Smart Materials and Structures , 7:674-692.
2.
American Society of Civil Engineers (ASCE).2009. Report Card for America’s Infrastructure: Transportation Issue Brief. Available at: http://www.infrastructurereportcard.org/report-cards (accessed date December 2009).
3.
Belarbi, A., Watkins, S.E., Chandrashekhara, K., Corra, J. and Konz, B.2001. ‘‘Smart FRP Reinforcing Bars for Health Monitoring of Concrete Structures,’’ Smart Materials and Structures, 10:427-431.
4.
Born, M. and Wolf, E.1980. Principles of Optics, 6th (corrected) edn, Pergamon Press, Oxford, UK.
5.
Carroll, D.R.1997. ‘‘Bridge Engineering for the Elementary Grades ,’’ Journal of Engineering Education, 86:221-226.
6.
Casas, J.R. and Cruz, P.J.S.2003. ‘‘Fiber Optic Sensors for Bridge Monitoring ,’’ Journal of Bridge Engineering, 8:326-373.
7.
Chang, C.C., Chang, T.Y.P. and Xu, Y.G.2000. ‘‘Adaptive Neural Networks for Model Updating of Structures,’’ Smart Materials and Structures , 9:59-68.
8.
Culshaw, B., Michie, C., Gardiner, P. and McGown, A.1996. ‘‘Smart Structures and Applications in Civil Engineering,’’Proceedings of the IEEE , 84:78-86.
9.
Dua, R., Watkins, S.E., Mulder, S.A. and Wunsch, D.C.2005. ‘‘MATLAB-based Introduction to Neural Networks for Sensors Curriculum,’’ International Journal of Engineering Education, 21:636-648.
10.
Dua, R., Watkins, S.E. and Wunsch, D.C.2004. ‘‘Demodulation of Extrinsic Fabry-Perot Interferometric Sensors for Vibration Testing Using Neural Networks, ’’ Optical Engineering, 43:2976-2985.
11.
Eckhoff, E.C., Eller, V.M., Watkins, S.E. and Hall, R.H.2002. ‘‘Interactive Virtual Laboratory for Experience with a Smart Bridge Test,’’ In: Proceedings of the 2002 ASEE Annual Conference, 16-19 June, Montreal , Canada.
12.
Fonda, J.W. and Watkins, S.E.2007. ‘‘Health Monitoring of a Truss Bridge using Adaptive Identification,’’ In: IEEE Conference on Intelligent Transportation Systems 2007 (ITSC2007), 30 September-3 October, Seattle, pp. 954-959.
13.
Goebel, K.F., Hu, X., Eklund, N.H.W. and Yan, W.2006a. ‘‘Fusing Diverse Monitoring Algorithms for Robust Change Detection,’’Proceedings of the SPIE on Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications, 6242: doi: 10.1117/12.665922.
14.
Goebel, K.F., Yan, W., Eklund, N.H.W., Hu, X., Avarasarala, V. and Celaya, J.2006b. ‘‘Defect Classification of Highly Noisy NDE Data using Classifier Ensembles,’’Proceedings of the SPIE on Smart Structures and Materials 2006: Smart Sensor Monitoring Systems and Applications, 6167: doi: 10.1117/12.659704.
15.
Hagan, M., Demuth, H. and Beale, M.1996. Neural Network Design, pp. 2-1-2-14, PWS Publishing Company, Boston.
16.
Hogg, W.D., Janzen, D., Valis, T. and Measures, R.M.1991. ‘‘Development of a Fiber Fabry-Perot Strain Gauge,’’Proceedings of the SPIE on Fiber Optic Smart Structures and Skins IV, 1588: doi: 10.1117/12.50190.
Lee, D.G., Mitrovic, M., Friedman, A. and Carman, G.P.2002. ‘‘Characterization of Fiber Optic Sensors for Structural Health Monitoring,’’ Journal of Composite Materials, 36:1349-1366.
19.
Mitchell, K., Watkins, S.E., Fonda, J.W. and Sarangapani, J.2007. ‘‘Embeddable Modular Hardware for Multi-Functional Sensor Networks,’’ Smart Materials and Structures , 16:N27-N34.
20.
Perrey, A., Watkins, S.E., Flori, R. and Swift, T.M.2008. ‘‘Work in Progress - Instrumentation and Measurement on a Truss,’’ In: ASEE/IEEE Frontiers in Education Conference, 22-25 October, Saratoga Springs, NY.
21.
RISA Technologies.2009. RISA-2D . Available at: http://www.risatech.com/p_risa2d.html (accessed date December 2009).
22.
Spillman Jr, W.B.1996. ‘‘Sensing and Processing for Smart Structures,’’Proceedings of the IEEE, 84:68-77.
23.
Udd, E.1996. ‘‘Fiber Optic Smart Structures,’’Proceedings of the IEEE, 84:884-894.
24.
Wang, M.L., Heo, G. and Satpathi, D.1998. ‘‘A Health Monitoring System for Large Structural Systems,’’ Smart Materials and Structures , 7:674-692.
25.
Watkins, S.E.2003. ‘‘Smart Bridges using Fiber Optic Sensors,’’IEEE Instrumentation and Measurement Magazine, 6:25-30.
26.
Watkins, S.E., Akhavan, F., Dua, R., Chandrashekhara, K. and Wunsch, D.C.2007a. ‘‘Impact-Induced Damage Characterization of Composite Plates using Neural Networks,’’ Smart Materials and Structures, 16:515-524.
27.
Watkins, S.E., Eller, V.M., Corra, J., Molander, M.J., Konz, B., Hall, R.H., Chandrashekhara, K. and Belarbi, A.2002a. ‘‘Interdisciplinary Graduate Experience: Lessons Learned,’’ In: Proceedings of the 2002 ASEE Annual Conference, 16-19 June 2002, Montreal, Canada.
28.
Watkins, S.E., Fonda, J.W. and Nanni, A.2007b. ‘‘Assessment of an Instrumented Reinforced-Concrete Bridge with Fiber-Reinforced-Polymer Strengthening,’’Optical Engineering, 46:051010.
29.
Watkins, S.E., Hall, R.H., Chandrashekhara, K. and Baker, J.M.2004. ‘‘Interdisciplinary Learning through a Connected Classroom,’’ International Journal of Engineering Education, 20:176-187.
30.
Watkins, S.E., Sanders, G.W., Akhavan, F. and Chandrashekhara, K.2002b. ‘‘Modal Analysis using Fiber Optic Sensors and Neural Networks for Prediction of Composite Beam Delamination , ’’ Smart Materials and Structures, 11:489-495.
31.
Watkins, S.E., Swift, T.M. and Fonda, J.W.2008. ‘‘Development of Autonomous Triggering Instrumentation,’’Proceedings of the SPIE on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 6932: doi: 10.1117/ 12.776008.
32.
Wen, Y., Ghaboussi, J., Venini, P. and Nikzad, K.1995. ‘‘Control of Structures using Neural Networks ,’’ Smart Materials and Structures, 4:A149-A157.
33.
Yoshino, T., Kurasawa, K. and Katsuji, I.1982. ‘‘Fiber-Optic Fabry-Perot Interferometer and its Sensor Applications,’’ IEEE Journal Quantum Electronics, 10:1624-1633.
