SpringerLink
Log in

Survey and study on intelligent monitoring and health management for large civil structure

  • Survey Paper
  • Published:
International Journal of Intelligent Robotics and Applications Aims and scope Submit manuscript

Abstract

With rapid development of the large-scaled civil infrastructure, their sustainability and dependability have been the most important issues concerning social orders and people’s daily lives. In recent years, Structural Health Monitoring (SHM) for civil engineering has attracted much attention of researchers. Meanwhile, the latest progresses on the cyber-physical system, intelligent robot, wireless sensor network and data mining techniques have promoted the growth of intelligent structure monitoring and health management. This paper firstly introduces the development and classification of SHM of civil infrastructure. Secondly, the recent research progresses on its enabling technologies including sensors, intelligent detection robot, wireless sensors network, data analysis and management are reported. Next, an intelligent monitoring and health management system for metal roof sheathings system is presented as an application example. Finally, some future trends for SHM are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38, 393–422 (2002)

    Article  Google Scholar 

  • Alam Bhuiyan, M.Z., Wu, J., Wang, G., Cao, J.: Sensing and decision making in cyber-physical systems: the case of structural event monitoring. IEEE Trans. Ind. Inform. 12, 2103–2114 (2016)

    Article  Google Scholar 

  • Anastasi, G., Conti, M., Di Francesco, M., Passarella, A.: Energy conservation in wireless sensor networks: a survey. Ad Hoc Netw. 7, 537–568 (2009)

    Article  Google Scholar 

  • Backes, P.G., Bar-Cohen, Y., Joffe, B.: The multifunction automated crawling system (MACS). In: IEEE International Conference on Robotics and Automation, 1997. Proceedings, 335–340 (1997)

  • Balaguer, C., Nez, A., Pastor, J.M.: A climbing autonomous robot for inspection applications in 3d complex environments. Robotica 18, 287–297 (2000)

    Article  Google Scholar 

  • Bandara, R.P., Chan, T.H., Thambiratnam, D.P.: Structural damage detection method using frequency response functions. Struct. Health Monit. 13, 418–429 (2014)

    Article  Google Scholar 

  • Bao, Y., Li, H., Ou, J.: Emerging data technology in structural health monitoring: compressive sensing technology. J. Civ. Struct. Health Monitor. 4, 77–90 (2014)

    Article  Google Scholar 

  • Bar-Cohen, Y., Backes, P.: Scanning large aerospace structures using open architecture crawlers. In: National Space and Missile Materials Symposium (NSMMS) (2000)

  • Betti, M., Facchini, L., Biagini, P.: Damage detection on a three-storey steel frame using artificial neural networks and genetic algorithms. Meccanica 50, 875–886 (2015)

    Article  Google Scholar 

  • Bodenmann, A., Thornton, B., Ura, T., Painumgal, U.V.: Visual map** of internal pipe walls using sparse features for application on board Autonomous Underwater Vehicles. In: Oceans, 1–8 (2009)

  • Boonlong, K.: Vibration-based damage detection in beams by cooperative coevolutionary genetic algorithm. Adv. Mech. Eng 2014, 155–171 (2014)

    Google Scholar 

  • Chen, Z., Zhou, X., Wang, X., Dong, L., Qian, Y.: Deployment of a smart structural health monitoring system for long-span arch bridges: a review and a case study. Sensors (Basel) 17, 586 (2017)

    Article  Google Scholar 

  • Chew, Z.J., Ruan, T., Zhu, M.: Strain energy harvesting powered wireless sensor node for aircraft structural health monitoring. Procedia Eng. 168, 1717–1720 (2016)

    Article  Google Scholar 

  • Choi, S., Song, B., Ha, R., Cha, H.: Energy-aware pipeline monitoring system using piezoelectric sensor. IEEE Sens. J. 12, 1695–1702 (2012)

    Article  Google Scholar 

  • Clements, R.: Advanced sensing, degradation detection, diagnostic and prognostic capabilities for structural health management. Proc. SPIE Int. Soc. Opt. Eng. 8347, 11 (2013)

    Google Scholar 

  • Contreras, W., Ziavras, S.: Wireless sensor network-based pattern matching technique for the circumvention of environmental and stimuli-related variability in structural health monitoring. IET Wirel. Sens. Syst. 6, 26–33 (2016)

    Article  Google Scholar 

  • Dantu, K., Rahimi, M., Shah, H., Babel, S.: Robomote: enabling mobility in sensor networks. In: International Symposium on Information Processing in Sensor Networks, 404-409 (2005)

  • Das, S., Saha, P., Patro, S.K.: Vibration-based damage detection techniques used for health monitoring of structures: a review. J. Civ. Struct. Health Monitor. 6, 477–507 (2016)

    Article  Google Scholar 

  • Devault, J.E.: Robotic system for underwater inspection of bridge piers. IEEE Instrument. Meas. Mag. 3, 32–37 (2000)

    Article  Google Scholar 

  • Diez, A., Khoa, N.L.D., Alamdari, M.M., Wang, Y., Chen, F., Runcie, P.: A clustering approach for structural health monitoring on bridges. J. Civ. Struct. Health Monitor. 6, 429–445 (2016)

    Article  Google Scholar 

  • Duisterwinkel, E.H.A., Talnishnikh, E., Krijnders, D., Wortche, H.J.: Sensor motes for the exploration and monitoring of operational pipelines. IEEE Trans. Instrum. Meas. 67, 655–666 (2018)

    Article  Google Scholar 

  • Engel, A., Friedmann, A., Koch, M., Rohlfing, J., Siebel, T., Mayer, D., Koch, A.: Hardware-accelerated wireless sensor network for distributed structural health monitoring. Procedia Technol. 15, 737–746 (2014)

    Article  Google Scholar 

  • Figueiredo, E., Figueiras, J., Park, G., Farrar, C.R., Worden, K.: Influence of the autoregressive model order on damage detection. Comput. Aided Civ. Inf. 26, 225–238 (2011)

    Article  Google Scholar 

  • Forstner, E., Wenzel, H.: The Application of Data Mining in Bridge Monitoring Projects: Exploiting Time Series Data of Structural Health Monitoring. In: 2011 22nd International Workshop on Database and Expert Systems Applications, Toulouse, 297–301 (2011)

  • Friedrich, M., Dobie, G., Chan, C.C., Pierce, S.G., Galbraith, W., Marshall, S., Hayward, G.: Miniature mobile sensor platforms for condition monitoring of structures. IEEE Sens. J. 9, 1439–1448 (2009)

    Article  Google Scholar 

  • Fukuda, Y., Feng, M.Q., Narita, Y., Kaneko, S.I.: Vision-based displacement sensor for monitoring dynamic response using robust object search algorithm. IEEE Sens. J. 13, 4725–4732 (2013)

    Article  Google Scholar 

  • Gibb, S., Le, T., La, H.M., Schmid, R., Berendsen, T.: A multi-functional inspection robot for civil infrastructure evaluation and maintenance. In: Ieee/rsj International Conference on Intelligent Robots and Systems, 2672–2677 (2017)

  • Gonzalez, I., Karoumi, R.: BWIM aided damage detection in bridges using machine learning. J. Civil Struct. Health Monitor. 5, 715–725 (2015)

    Article  Google Scholar 

  • Guo, J., Lee, K.M., Zhu, D., Yi, X., Wang, Y.: Large-deformation analysis and experimental validation of a flexure-based mobile sensor node. IEEE-ASME Trans. Mech. 17, 606–616 (2012)

    Article  Google Scholar 

  • Hackmann, G., Weijun, G., Guirong, Y., Zhuoxiong, S., Chenyang, L., Dyke, S.: Cyber-physical codesign of distributed structural health monitoring with wireless sensor networks. IEEE Trans. Parall. Distrbut. 25, 63–72 (2014)

    Article  Google Scholar 

  • Harvey, D.Y., Todd, M.D.: Structural health monitoring feature design by genetic programming. Smart Mater. Struct. 23, 095002 (2014)

    Article  Google Scholar 

  • Hodge, V.J., O’Keefe, S., Weeks, M., Moulds, A.: Wireless sensor networks for condition monitoring in the railway industry: a survey. IEEE T. Intell. Transp. 16, 1088–1106 (2015)

    Article  Google Scholar 

  • Hong, H., Yong, X.: Vibration-based damage detection of structures by genetic algorithm. J. Comput. Civil Eng. 16, 222–229 (2002)

    Article  Google Scholar 

  • Hu, X., Wang, B., Ji, H.: A wireless sensor network-based structural health monitoring system for highway bridges. Comput. Aided Civ. Inf. 28, 193–209 (2013)

    Article  Google Scholar 

  • Hu, Y., Rieutort-Louis, W.S.A., Sanz-Robinson, J., Huang, L., Glisic, B., Sturm, J.C., Wagner, S., Verma, N.: Large-Scale Sensing System Combining Large-Area Electronics and CMOS ICs for Structural-Health Monitoring. IEEE J. Solid-State Circ. 49, 513–523 (2014)

    Article  Google Scholar 

  • Huston, D., Esser, B., Miller, J., Wang, X.: Robotic and Mobile Sensor Systems for Structural Health Monitoring (2014)

  • Kamegawa, T., Baba, T., Gofuku, A.: V-shift control for snake robot moving the inside of a pipe with helical rolling motion. In: IEEE International Symposium on Safety, Security, and Rescue Robotics, 1–6 (2011)

  • Kane, M.B., Peckens, C., Lynch, J.P.: Design and selection of wireless structural monitoring systems for civil infrastructures. In: Sensor Technologies for Civil Infrastructures (2014)

  • Kim, J.T.: vibration-based damage detection in beams using genetic algorithm. Smart Struct. Syst. 3, 263–280 (2007)

    Article  Google Scholar 

  • Kosorus, H., Honigl, J., Kung, J.: Using R, WEKA and RapidMiner in Time Series Analysis of Sensor Data for Structural Health Monitoring. In: International Workshop on Database and Expert Systems Applications, 306–310 (2012)

  • Krishnamurthy, T., Hochhalter, J., Gallegos, A.: Damage characterization method for structural health management using reduced number of sensor inputs. In: Aiaa/asme/asce/ahs/asc Structures, Structural Dynamics and Materials Conference Aiaa/asme/ahs Adaptive Structures Conference Aiaa, (2012)

  • Kullaa, J.: Structural health monitoring under nonlinear environmental or operational influences. Shock Vib. 2014, 1–9 (2014)

    Article  Google Scholar 

  • Kundu, T., Khazaeli, S., Ravandi, A.G., Banerji, S., Bagchi, A.: The application of data mining and cloud computing techniques in data-driven models for structural health monitoring. SPIE 9805, 98052 (2016)

    Google Scholar 

  • Kundu, T., Mueller, I., Das, S., Roy, S., Janapati, V., Vonnieda, K., Zhang, D., Chang, F.-K.: An integrated health management system for real-time impact monitoring and prediction of impact-induced damage on composite structures. SPIE 7650, 76501 (2010)

    Google Scholar 

  • La, H.M., Lim, R.S., Basily, B.B., Gucunski, N., Yi, J., Maher, A., Romero, F.A., Parvardeh, H.: Mechatronic systems design for an autonomous robotic system for high-efficiency bridge deck inspection and evaluation. IEEE-ASME Trans. Mech. 18, 1655–1664 (2013)

    Article  Google Scholar 

  • Li, C., Chen, W., Liu, G., Yan, R., Xu, H., Qi, Y.: A noncontact FMCW radar sensor for displacement measurement in structural health monitoring. Sensors (Basel) 15, 7412–7433 (2015a)

    Article  Google Scholar 

  • Li, J., Deng, J., **e, W.: Damage detection with streamlined structural health monitoring data. Sensors (Basel) 15, 8832–8851 (2015b)

    Article  Google Scholar 

  • Li, X., Yu, W., Villegas, S.: Structural health monitoring of building structures with online data mining methods. IEEE Syst. J. 10, 1291–1300 (2016)

    Article  Google Scholar 

  • Li, X., Zhang, W.: Design of Prognostic and Health Management Structure for UAV System. In: 2011 21st International Conference on Systems Engineering, 12–16 (2011)

  • Li, Y., Wang, Y., Chase, J.G., Mattila, J., Myung, H., Sawodny, O.: Survey and introduction to the focused section on mechatronics for sustainable and resilient civil infrastructure. IEEE-ASME Trans. Mech. 18, 1637–1646 (2013)

    Article  Google Scholar 

  • Lins, R.G., Givigi, S.N.: Autonomous robot system architecture for automation of structural health monitoring. In: Systems Conference, 1–7 (2016)

  • Liu, X., Tong, X., Ding, K., Zhao, X., Zhu, L., Zhang, X.: Measurement of long-term periodic and dynamic deflection of the long-span railway bridge using microwave interferometry. IEEE J. Sel. Top. Appl. 8, 4531–4538 (2015)

    Google Scholar 

  • Loupos, K., Doulamis, A.D., Stentoumis, C., Protopapadakis, E., Makantasis, K., Doulamis, N.D., Amditis, A., Chrobocinski, P., Victores, J., Montero, R., Menendez, E., Balaguer, C., Lopez, R., Cantero, M., Navarro, R., Roncaglia, A., Belsito, L., Camarinopoulos, S., Komodakis, N., Singh, P.: Autonomous robotic system for tunnel structural inspection and assessment. Int. J. Intell. Robot. Appl. 47, 777–780 (2017)

    Google Scholar 

  • Luk, B.L., White, T.S., Cooke, D.S., Hazel, G., Chen, S., Centre, B., Pb, G.G.: Climbing service robot for duct inspection and maintenance applications in a nuclear reactor. J. Algebra 107, 63–74 (2001)

    Google Scholar 

  • Luo, M., Lin, S., Feng, D., He, Z., Chen, L.: Design of the prognostics and health management platform of high-speed railway traction power supply equipment. In: Prognostics and System Health Management Conference, (2017)

  • Lynch, J.P., Alampalli, S., Alampalli, S., Ettouney, M.: Big data and high-performance analytics in structural health monitoring for bridge management. SPIE 9803, 980315 (2016)

    Google Scholar 

  • Lynch, J.P., Wang, Y., Loh, K.J., Yi, J.H., Yun, C.B.: Performance monitoring of the Geumdang Bridge using a dense network of high-resolution wireless sensors. Smart Mater. Struct. 15, 49–55 (2006)

    Article  Google Scholar 

  • Mascarenas, D., Flynn, E., Farrar, C., Park, G., Todd, M.: A mobile host approach for wireless powering and interrogation of structural health monitoring sensor networks. IEEE Sens. J. 9, 1719–1726 (2009)

    Article  Google Scholar 

  • Miranda-Vega, J.E., Flores-Fuentes, W., Sergiyenko, O., Rivas-López, M., Lindner, L., Rodríguez-Quiñonez, J.C., Hernández-Balbuena, D.: Optical cyber-physical system embedded on an FPGA for 3D measurement in structural health monitoring tasks. Microprocess. Microsy. 56, 121–133 (2018)

    Article  Google Scholar 

  • Moradipour, P., Chan, T.H.T., Gallag, C.: An improved modal strain energy method for structural damage detection, 2D simulation. Struct. Eng. Mech. 54, 105–119 (2015)

    Article  Google Scholar 

  • Moreu, F., Ayorinde, E., Mason, J., Farrar, C., Mascarenas, D.: Remote railroad bridge structural tap testing using aerial robots. Int. J. Intell. Robot. Appl. 2, 67–80 (2017)

    Article  Google Scholar 

  • Mori, M., Hirose, S.: Three-dimensional serpentine motion and lateral rolling by active cord mechanism ACM-R3. In: Ieee/rsj International Conference on Intelligent Robots and Systems, 821, 829-834 (2002)

  • Myung, H., Jeon, H., Bang, Y.S., Wang, Y.: Robotic sensing for assessing and monitoring civil infrastructures. Sens. Technol. Civil Infrastruct. 56, 410–445 (2014)

    Article  Google Scholar 

  • Myung, H., Lee, S., Lee, B.: Paired structured light for structural health monitoring robot system. Struct. Health Monit. 9, 49–64 (2010)

    Google Scholar 

  • Neves, A.C., González, I., Leander, J., Karoumi, R.: Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. J. Civil Struct. Health Monitor. 7, 689–702 (2017)

    Article  Google Scholar 

  • Nagata, S., Sekiguchi, M., Asakawa, K.: Mobile robot control by a structured hierarchical neural network. IEEE Contr. Syst. Mag. 10, 69–76 (1990)

    Article  Google Scholar 

  • Noel, A.B., Abdaoui, A., Elfouly, T., Ahmed, M.H., Badawy, A., Shehata, M.S.: Structural health monitoring using wireless sensor networks: a comprehensive survey. IEEE Commun. Surv. Tuttor. 19, 1403–1423 (2017)

    Article  Google Scholar 

  • Prasanna, P., Dana, K.J., Gucunski, N., Basily, B.B., La, H.M., Lim, R.S., Parvardeh, H.: Automated Crack Detection on Concrete Bridges. IEEE T. Autom. Sci. Eng. 13, 591–599 (2016)

    Article  Google Scholar 

  • Rainieri, C., Fabbrocino, G., Cosenza, E.: Integrated seismic early warning and structural health monitoring of critical civil infrastructures in seismically prone areas. Struct. Health Monit. 10, 291–308 (2010)

    Article  Google Scholar 

  • Rao, R.M., Lakshmi, A.K.: Damage diagnostic technique combining POD with time-frequency analysis and dynamic quantum PSO. Meccanica 50, 1551–1578 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  • Rault, T., Bouabdallah, A., Challal, Y.: Energy efficiency in wireless sensor networks: a top-down survey. Comput. Netw. 67, 104–122 (2014)

    Article  Google Scholar 

  • Shin, J.U., Kim, D., Kim, J.H., Myung, H.: Micro aerial vehicle type wall-climbing robot mechanism. In: 2013 IEEE RO-MAN: The 22nd IEEE International Symposium on Robot and Human Interactive Communication, 8692, 86921B (2013)

  • Small, N., Lee, K., Mann, G.: An assigned responsibility system for robotic teleoperation control. Int. J. Intell. Robot. Appl. 2, 81–97 (2018)

    Article  Google Scholar 

  • Toivola, J., Hollmén, J.: Feature Extraction and Selection from Vibration Measurements for Structural Health Monitoring. In: International Symposium on Intelligent Data Analysis, 213–224 (2009)

  • Tomizuka, M., Spencer, J.B.F., Nagayama, T., Rice, J.A.: Decentralized structural health monitoring using smart sensors. In: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2008, (2008)

  • Udd, E., Sazonov, E., Inaudi, D., Janoyan, K., Jha, R.: Wireless intelligent sensor network for autonomous structural health monitoring. In: Proceedings of SPIE—the international society for optical engineering, 5384, 305 (2004)

  • Voltz, L., Cardoso, E.L., Medeiros, R.D.: Damage detection on aluminum beams using vibration-based method and Artificial Neural Networks. In: Abcm International Congress of Mechanical Engineering—Cobem (2017)

  • Wang, W., Joshi, A., Tirpankar, N., Erickson, P., Cline, M., Thangaraj, P., Henderson, T.C.: Bayesian computational sensor networks: small-scale structural health monitoring. Procedia Comput Sci 51, 2603–2612 (2015)

    Article  Google Scholar 

  • Wang, Y., Lynch, J., Law, K.: A wireless structural health monitoring system with multithreaded sensing devices: design and validation. Struct. Infrastruct. Eng. 3, 103–120 (2007)

    Article  Google Scholar 

  • Weihua, S., Hongjun, C., Ning, X.: Navigating a miniature crawler robot for engineered structure inspection. IEEE Trans. Autom. Sci. Eng. 5, 368–373 (2008)

    Article  Google Scholar 

  • **ao, J., **ao, J., **, N., Tummala, R.L., Mukherjee, R.: Fuzzy controller for wall-climbing microrobots. IEEE Trans. Fuzzy Syst. 12, 466–480 (2004)

    Article  Google Scholar 

  • **aodan, F.: Design and implementation of remote health monitoring system for 3D visual bridge. Procedia Eng 174, 1330–1335 (2017)

    Article  Google Scholar 

  • Yu, L., Lin, J.C.: Cloud computing-based time series analysis for structural damage detection. J. Eng. Mech. ASCE 143, 4015002 (2015)

    Article  Google Scholar 

  • Zhang, T., Wang, D., Cao, J., Ni, Y.Q., Chen, L.-J., Chen, D.: Elevator-assisted sensor data collection for structural health monitoring. IEEE Trans. Mobile Comput. 11, 1555–1568 (2012)

    Article  Google Scholar 

  • Zhao, Z., Chen, C.: Concrete bridge deterioration diagnosis using fuzzy inference system. Adv. Eng. Softw. 32, 317–325 (2001)

    Article  MATH  Google Scholar 

  • Zheng, W., Zhu, Y.: A bio-inspired memory model for structural health monitoring. Meas. Sci. Technol. 20, 045704 (2009)

    Article  Google Scholar 

  • Zhou, Q., Zhou, H., Zhou, Q., Yang, F., Luo, L., Li, T.: Structural damage detection based on posteriori probability support vector machine and Dempster-Shafer evidence theory. Appl. Soft Comput. 36, 368–374 (2015)

    Article  Google Scholar 

  • Zhu, D., Yi, X., Wang, Y., Lee, K.M., Guo, J.: A mobile sensing system for structural health monitoring: design and validation. Smart Mater. Struct. 19, 241–247 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Beihang University, Bei**g, People’s Republic of China

    Liman Yang, Chenyao Fu, Yunhua Li & Lianming Su

Authors
  1. Liman Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chenyao Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yunhua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lianming Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yunhua Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, L., Fu, C., Li, Y. et al. Survey and study on intelligent monitoring and health management for large civil structure. Int J Intell Robot Appl 3, 239–254 (2019). https://doi.org/10.1007/s41315-019-00079-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41315-019-00079-2

Keywords

Search

Navigation