A Prototype Tool of Optimal Wireless Sensor Placement for Structural Health Monitoring

  • Weixiang Shi
  • Changzhi WuEmail author
  • Xiangyu Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10864)


With increasing collapses of civil infrastructures and popularized utilization of large-scale structures, worldwide deployment of structural health monitoring (SHM) systems is of importance in emerging and future SHM industry. A reliable and practical tool of optimal wireless sensor placement (OWSP) can promote implementation of wireless-based SHM systems by reducing construction cost, extending lifetime and improving detection accuracy. This paper presents a prototype of wireless sensor placement (WSP) for bridge SHM based on multi-objective optimisation (MOO) technique and bridge information modelling (BrIM) technology. MOO technique is used to determine sensor locations by simultaneously searching for multiple trade-offs among structural engineering, wireless engineering and construction management. The BrIM model will be used as a platform to validate and visualize the proposed MOO. A BrIM integrated design tool will be developed to improve the efficiency in design stage through visualisation capabilities and semantic enrichment of a bridge model. As future applications, 4D BrIM that combines time-related information in visual environments with the 3D geometric and semantic BrIM model will help engineers and contractors to visualise possible defects and project costs in the real world.


Structural health monitoring (SHM) Optimal wireless sensor placement (OWSP) Multiple objective optimization (MOO) Bridge information modelling (BrIM) 



This research was partially supported under Australian Research Council Linkage Project scheme (project number: LP160100528).


  1. 1.
    Lee, G.C., Mohan, S., Huang, C., Fard, B.N.: A study of US bridge failures (1980–2012). MCEER Technical report 13-0008 (2013)Google Scholar
  2. 2.
    Austroads: Investigating the Development of a Bridge Assessment Tool for Determining Access for High Productivity Freight Vehicles, Research report (2012)Google Scholar
  3. 3.
    Park, S., Savvides, A., Srivastava, M.B.: Simulating networks of wireless sensors. In: Proceedings of the 33nd Conference on Winter Simulation, pp. 1330–1338. IEEE Computer Society (2001)Google Scholar
  4. 4.
    Wong, K.Y.: Instrumentation and health monitoring of cable-supported bridges. Struct. Control Health Monit. 11, 91–124 (2004)CrossRefGoogle Scholar
  5. 5.
    Austroads: Guidelines for Ensuring Specified Quality Performance in Bridge Construction (2003)Google Scholar
  6. 6.
    Lynch, J.P., Loh, K.J.: A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vibr. Digest 38, 91–130 (2006)CrossRefGoogle Scholar
  7. 7.
    Spencer, B.F., Jo, H.K., Mechitov, K.A., Li, J., Sim, S.H., Kim, R.E., Cho, S., Linderman, L.E., Moinzadeh, P., Giles, R.K., Agha, G.: Recent advances in wireless smart sensors for multi-scale monitroing and control of civil infrastructure. J. Civil Struct. Health Monit. 6(1), 17–41 (2015)CrossRefGoogle Scholar
  8. 8.
    Li, J., Hao, H., Chen, Z.: Damage identification and optimal sensor placement for structures under unknown traffic-induced vibrations. J. Aerosp. Eng. 30, B4015001 (2015)CrossRefGoogle Scholar
  9. 9.
    Yi, T.-H., Li, H.-N., Zhang, X.-D.: Sensor placement on Canton Tower for health monitoring using asynchronous-climb monkey algorithm. Smart Mater. Struct. 21, 125023 (2012)CrossRefGoogle Scholar
  10. 10.
    Chang, M., Pakzad, S.N.: Optimal sensor placement for modal identification of bridge systems considering number of sensing nodes. J. Bridge Eng. 19, 04014019 (2014)CrossRefGoogle Scholar
  11. 11.
    Bhuiyan, M.Z.A., Wang, G.: Sensor placement with multiple objectives for structural health monitoring. ACM Trans. Sens. Netw. 10, 68 (2014)CrossRefGoogle Scholar
  12. 12.
    Yi, T.-H., Li, H.-N., Zhang, X.-D.: A modified monkey algorithm for optimal sensor placement in structural health monitoring. Smart Mater. Struct. 21, 105033 (2012)CrossRefGoogle Scholar
  13. 13.
    Yi, T.-H., Li, H.-N., Gu, M.: Optimal sensor placement for health monitoring of high-rise structure based on genetic algorithm. Math. Probl. Eng. 2011, Article ID 395101, 12 p. (2011)Google Scholar
  14. 14.
    Hou, L., Wu, C., Wang, X., Wang, J.: A framework design for optimizing scaffolding erection by applying mathematical models and virtual simulation. Comput. Civil Build. Eng. 2014, 323–330 (2014)Google Scholar
  15. 15.
    Hou, L., Zhao, C., Wu, C., Moon, S., Wang, X.: Discrete firefly algorithm for scaffolding construction scheduling. J. Comput. Civil Eng. 31, 04016064 (2016)CrossRefGoogle Scholar
  16. 16.
    Liang, J., Liu, M., Kui, X.: A survey of coverage problems in wireless sensor networks. Sens. Transducers 163, 240 (2014)Google Scholar
  17. 17.
    Zhu, J., Wright, G., Wang, J., Wang, X.: A critical review of the integration of geographic information system and building information modelling at the data level. ISPRS Int. J. Geo-Inf. 7, 66 (2018)CrossRefGoogle Scholar
  18. 18.
    Bhuiyan, M.Z.A., Wang, G., Cao, J., Wu, J.: Deploying wireless sensor networks with fault-tolerance for structural health monitoring. IEEE Trans. Comput. 64, 382–395 (2015)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Sengupta, S., Das, S., Nasir, M.D., Panigrahi, B.K.: Multi-objective node deployment in WSNs: in search of an optimal trade-off among coverage, lifetime, energy consumption, and connectivity. Eng. Appl. Artif. Intell. 26, 405–416 (2013)CrossRefGoogle Scholar
  20. 20.
    Wang, T., Wang, J., Wu, P., Wang, J., He, Q., Wang, X.: Estimating the environmental costs and benefits of demolition waste using life cycle assessment and willingness-to-pay: a case study in Shenzhen. J. Clean. Prod. 172, 14–26 (2018)CrossRefGoogle Scholar
  21. 21.
    You, T., Jin, H., Li, P.: Optimal placement of wireless sensor nodes for bridge dynamic monitoring based on improved particle swarm algorithm. Int. J. Distrib. Sens. Netw. 9(12), 390936 (2013)CrossRefGoogle Scholar
  22. 22.
    Cheng, L., Niu, J., Cao, J., Das, S.K., Gu, Y.: QoS aware geographic opportunistic routing in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 25, 1864–1875 (2014)CrossRefGoogle Scholar
  23. 23.
    Jaffres-Runser, K., Schurgot, M.R., Wang, Q., Comaniciu, C., Gorce, J.-M.: A cross-layer framework for multiobjective performance evaluation of wireless ad hoc networks. Ad Hoc Netw. 11, 2147–2171 (2013)CrossRefGoogle Scholar
  24. 24.
    Hu, H., Xu, L., Zhu, B., Wei, R.: A compatible control algorithm for greenhouse environment control based on MOCC strategy. Sensors 11, 3281–3302 (2011)CrossRefGoogle Scholar
  25. 25.
    Lee, C.-Y., Chong, H.-Y., Liao, P.-C., Wang, X.: Critical review of social network analysis applications in complex project management. J. Manag. Eng. 34, 04017061 (2017)CrossRefGoogle Scholar
  26. 26.
    Long, Q., Wu, C., Huang, T., Wang, X.: A genetic algorithm for unconstrained multi-objective optimization. Swarm Evol. Comput. 22, 1–14 (2015)CrossRefGoogle Scholar
  27. 27.
    Song, Y., Tan, Y., Song, Y., Wu, P., Cheng, J.C., Kim, M.J., Wang, X.: Spatial and temporal variations of spatial population accessibility to public hospitals: a case study of rural-urban comparison. GISci. Remote Sens. 1–27 (2018)Google Scholar
  28. 28.
    Tao, S., Wu, C., Sheng, Z., Wang, X.: Space-time repetitive project scheduling considering location and congestion. J. Comput. Civil Eng. 32, 04018017 (2018)CrossRefGoogle Scholar
  29. 29.
    Zhao, C., Wu, C., Chai, J., Wang, X., Yang, X., Lee, J.-M., Kim, M.J.: Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty. Appl. Soft Comput. 55, 549–564 (2017)CrossRefGoogle Scholar
  30. 30.
    Chi, H.-L., Kang, S.-C., Wang, X.: Research trends and opportunities of augmented reality applications in architecture, engineering, and construction. Autom. Constr. 33, 116–122 (2013)CrossRefGoogle Scholar
  31. 31.
    Chong, H.Y., Lopez, R., Wang, J., Wang, X., Zhao, Z.: Comparative analysis on the adoption and use of BIM in road infrastructure projects. J. Manag. Eng. 32, 05016021 (2016)CrossRefGoogle Scholar
  32. 32.
    Li, J., Wang, Y., Wang, X., Luo, H., Kang, S.-C., Wang, J., Guo, J., Jiao, Y.: Benefits of building information modelling in the project lifecycle: construction projects in Asia. Int. J. Adv. Rob. Syst. 11, 124 (2014)CrossRefGoogle Scholar
  33. 33.
    Wang, Y., Wang, X., Wang, J., Yung, P., Jun, G.: Engagement of facilities management in design stage through BIM: framework and a case study. Adv. Civil Eng. 2013, Article ID 189105, 8 p. (2013)Google Scholar
  34. 34.
    Fan, W., Qiao, P.: Vibration-based damage identification methods: a review and comparative study. Struct. Health Monit. 10, 83–111 (2011)CrossRefGoogle Scholar
  35. 35.
    Bhuiyan, M.Z.A., Wang, G., Cao, J., Wu, J.: Sensor placement with multiple objectives for structural health monitoring. ACM Trans. Sens. Netw. (TOSN) 10, 68 (2014)Google Scholar
  36. 36.
    Bae, S.-C., Jang, W.-S., Woo, S.: Prediction of WSN placement for bridge health monitoring based on material characteristics. Autom. Constr. 35, 18–27 (2013)CrossRefGoogle Scholar
  37. 37.
    Liu, X., Cao, J., Tang, S.: Enabling fast and reliable network-wide event-triggered wakeup in WSNs. In: 2013 IEEE 34th Real-Time Systems Symposium (RTSS), pp. 278–287. IEEE (2013)Google Scholar
  38. 38.
    Cheng, P., Chuah, C.-N., Liu, X.: Energy-aware node placement in wireless sensor networks. In: Global Telecommunications Conference, GLOBECOM 2004, pp. 3210–3214. IEEE (2004)Google Scholar
  39. 39.
    Olariu, S., Stojmenovic, I.: Design guidelines for maximizing lifetime and avoiding energy holes in sensor networks with uniform distribution and uniform reporting. In: INFOCOM 2006, pp. 1–12 (2006)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Australian Joint Research Centre for Building Information ModellingCurtin UniversityBentleyAustralia

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