Performance Evaluation of Truss Structure via Cloud Matter Element Fusion
Publication: Journal of Performance of Constructed Facilities
Volume 36, Issue 2
Abstract
The performance evaluation of truss bridges is very difficult owing to their complex structure and numerous components. This paper develops an evaluation framework for truss structures using the internet of things (IOT) technique and proposes a novel methodology to increase evaluation accuracy based on cloud matter element fusion (CMEF). An experiment was carried out on a laboratory truss that was equipped with monitoring devices. Then the monitoring system, data storage module, and loading equipment were connected by IOT and then transmitted to the data processing module to carry out real-time evaluation. Furthermore, in the data processing module, the cloud matter element can effectively integrate multiple evaluation features into a global evaluation system, and the randomness contained in the monitoring data and the fuzziness inherent in the evaluation process can be considered synthetically by the cloud models established in this study. Then these evaluation features with their respective weights are fused by evidence fusion, so as to obtain the final performance of the experimental truss. The results have indicated that the evaluation accuracy can be obviously increased from 46.1% to 99.5% by the proposed methodology. The proposed method in this study provides an illustration of an effective evaluation for the truss structure.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.
Acknowledgments
The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (51474048) and the Fundamental Research Funds for the Central Universities (Grant No. N170104024). This research was also supported by the Open Projects Foundation (BHSKL20-03-GF) of the State Key Laboratory for Health and Safety of Bridge Structures.
References
Abruzzese, D., A. Micheletti, A. Tiero, M. Cosentino, and P. Abiuso. 2020. “IoT sensors for modern structural health monitoring. A new frontier.” Procedia Struct. Integrity 25 (Jan): 378–385. https://doi.org/10.1016/j.prostr.2020.04.043.
Berman, J. W., B. S. Wang, A. W. Olson, C. W. Roeder, and D. E. Lehman. 2012. “Rapid assessment of gusset plate safety in steel truss bridges.” J. Bridge Eng. 17 (2): 221–231. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000246.
Certa, A., F. Hopps, R. Inghilleri, and C. M. La Fata. 2017. “A Dempster-Shafer theory-based approach to the failure mode, effects and criticality analysis (FMECA) under epistemic uncertainty: Application to the propulsion system of a fishing vessel.” Reliab. Eng. Syst. Saf. 159 (Mar): 69–79. https://doi.org/10.1016/j.ress.2016.10.018.
Chen, X., and Z. Qiu. 2018. “A novel uncertainty analysis method for composite structures with mixed uncertainties including random and interval variables.” Compos. Struct. 184 (1): 400–410. https://doi.org/10.1016/j.compstruct.2017.09.068.
Hao, S. 2010. “I-35W bridge collapse.” J. Bridge Eng. 15 (5): 608–614. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000090.
Jin, P., W. Wu, and M. Xu. 2010. “The design and analysis of pile protection devices for the Qiantang River bridge.” In Proc., ASME Int. Conf. on Ocean 2010. New York: ASME.
Li, D., M. Haijun, and S. Xuemei. 1995. “Membership clouds and membership cloud generators.” J. Comput. Res. Dev. 32 (6): 15–20.
Li, H., F. Zhang, and Y. Jin. 2014. “Real-time identification of time-varying tension in stay cables by monitoring cable transversal acceleration.” Struct. Control Health Monit. 21 (7): 1100–1117. https://doi.org/1100-1117.10.1002/stc.1634.
Li, J., and H. Hao. 2016. “Health monitoring of joint conditions in steel truss bridges with relative displacement sensors.” Measurement 88 (Jun): 360–371. https://doi.org/10.1016/j.measurement2015.12.009.
Liang, L., S. Sun, M. Li, and X. Li. 2019. “Data fusion technique for bridge safety assessment.” J. Test. Eval. 47 (3): 20170760. https://doi.org/10.1520/JTE20170760.
Liao, M., T. Okazaki, R. Ballarini, A. E. Schultz, and T. V. Galambos. 2011. “Nonlinear finite element analysis of critical gusset plates in the I-35W bridge in Minnesota.” J. Struct. Eng. 137 (1): 59–68. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000269.
Lin, C. J., M. Zhang, L. P. Li, Z. Q. Zhou, and T. Li. 2020. “Risk assessment of tunnel construction based on improved cloud model.” J. Perform. Constr. Facil. 34 (3): 04020028. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001421.
Ministry of Transport of the People's Republic of China. 2015. Specifications for design of highway steel bridge. JTG D64-2015. Beijing: Ministry of Transport of the People's Republic of China.
Pernille, E., and A. Karyne. 2017. “Stakeholder value constructs in megaprojects: A long-term assessment case study.” Project Manage. J. 48 (6): 60–75. https://doi.org/10.1177/875697281704800606.
Shieh, J. I., and H. H. Wu. 2017. “A framework of applying ordering coefficient based on the information energy to identify the causal relationships among critical factors from raw data.” J. Test. Eval. 46 (2): 20150328. https://doi.org/10.1520/JTE20150328.
Sun, S., L. Liang, and M. Li. 2021. “Condition assessment of stay cables via cloud evidence fusion.” KSCE J. Civ. Eng. 25 (3): 866–878. https://doi.org/10.1007/s12205-021-0139-1.
Verbert, K., R. Babuška, and B. De Schutter. 2017. “Bayesian and Dempster–Shafer reasoning for knowledge-based fault diagnosis—A comparative study.” Eng. Appl. Artif. Intell. 60 (Apr): 136–150. https://doi.org/10.1016/j.engappai.2017.01.011.
Wang, N., Y. Mi, G. Hong, and L. Wei. 2016. “Logistics network model based on matter element node.” Procedia Comput. Sci. 91 (Jan): 351–356. https://doi.org/10.1016/j.procs.2016.07.093.
Wang, Q., and S. Q. Li. 2019. “Shale gas industry sustainability assessment based on WSR methodology and fuzzy matter-element extension model: The case study of China.” J. Cleaner Prod. 226 (Jul): 336–348. https://doi.org/10.1016/j.jclepro.2019.03.346.
Wang, Y. L., J. L. Yang, and M. H. Zhou. 2021. “Evaluating the sustainability of China’s power generation industry based on a matter-element extension model.” Util. Policy 69 (Apr): 101166. https://doi.org/10.1016/j.jup.2021.101166.
Wang, Z., Y. Ran, Y. Chen, H. Yu, and G. Zhang. 2020. “Failure mode and effects analysis using extended matter-element model and AHP.” Comput. Ind. Eng. 140 (2): 106233. https://doi.org/10.1016/j.cie.2019.106233.
Xu, X., Q. Huang, Y. Ren, and X. Liu. 2018. “Determination of index weights in suspension bridge condition assessment based on group-AHP.” J. Hunan Univ. Nat. Sci. 45 (3): 122–128. https://doi.org/10.16339/j.cnki.hdxbzkb.2018.03.015.
Yanez-Borjas, J. J., M. Valtierra-Rodriguez, D. Camarena-Martinez, and J. P. Amezquita-Sanchez. 2020. “Statistical time features for global corrosion assessment in a truss bridge from vibration signals.” Measurement 160 (Aug): 107858. https://doi.org/10.1016/j.measurement.2020.107858.
Zhao, Y., S. H. He, and Y. F. Song. 2005. “Evaluation methodology for existing truss-cable composite continuous steel bridge.” J. Chang’an Univ. (Nat. Sci. Ed.) 25 (5): 47–50. https://doi.org/10.19721/j.cnki.1671-8879.2005.05.011.
Zong-You, D., Z. Wen-Long, S. Yan-An, and W. Hai-Tao. 2017. “The application of cloud matter-element in information security risk assessment.” In Proc., Int. Conf. on Information Management. New York: IEEE.
Information & Authors
Information
Published In
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Aug 19, 2021
Accepted: Dec 8, 2021
Published online: Jan 27, 2022
Published in print: Apr 1, 2022
Discussion open until: Jun 27, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.