Abstract
Instrumentation during tunnel construction can be used as reference data to identify anomalies, find the causes, and develop countermeasures through data trend analysis in terms of the construction, maintenance, and management of tunnels. Therefore, obtaining reliable and accurate data is very important to prevent large accidents and minimize damage. However, measurement sensors used in the field often show abnormal data because of the noise caused by functional and environmental factors. Such data may be misinterpreted as actual data caused by accidents in the analysis process and lead to construction delays due to investigating the situation. Moreover, the accumulation of such data may also have negative effects on analyzing the trend of the entire measurement data. Therefore, this paper proposes data filter-based correction techniques to correct noisy measurement data so that it does not affect the trend of accumulated data. By applying data filters of various sizes through simulations, this paper also presents the appropriate data filter size by examining the performance by filter size and validates the data correction performance by applying the proposed techniques to accumulated actual data measured from a construction site.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adam A, Josephson P, Lindahl G (2014) Implications of cost overruns and time delays on major public construction projects. In: Shen L, Ye K, Mao C (eds) Proceedings of the 19th international symposium on advancement of construction management and real estate. Springer, Berlin, Heidelberg, Germany, DOI: https://doi.org/10.1007/978-3-662-46994-1_61
Bhalla S, Yang YW, Zhao J, Soh CK (2005) Structural health monitoring of underground facilities — Technological issues and challenges. Tunnelling and Underground Space Technology 20:487–500, DOI: https://doi.org/10.1016/j.tust.2005.03.003
Devarajan G, Aatre VK, Sridhar CS (1990) Analysis of median filter. ACE’ 90. Proceeding of XVI annual convention and exhibition of the IEEE in India, January 22–25, Bangalore, India, 274–276, DOI: https://doi.org/10.1109/ACE.1990.762694
García-Gila D, Luengo J, García S, Herrera F (2019) Enabling smart data: Noise filtering in big data classification. Information Sciences 479:135–152, DOI: https://doi.org/10.1016/j.ins.2018.12.002
Gonzales-Barajas J, Montenegro D (2016) Average filtering: Theory, design and implementation. In: Digital signal processing (DSP): Fundamentals, techniques and applications, Chapter 3. Nova Science Publisher, Smithtown, NY, USA
Kumar N, Nachamai M (2017) Noise removal and filtering techniques used in medical images. Oriental Journal of Computer Science & Technology 10(1):103–113, DOI: https://doi.org/10.13005/ojcst/10.01.14
Luo P, Zhang M, Liu Y, Han D, Li Q (2012) A moving average filter based method of performance improvement for ultraviolet communication system. 2012 8th international symposium on communication systems, networks & digital signal processing (CSNDSP), July 18–20, Poznan, Poland, DOI: https://doi.org/10.1109/CSNDSP.2012.6292672
Maghsoudi A, Kalantari B (2014) Monitoring instrumentation in underground structures. Open Journal of Civil Engineering 4:135–146, DOI: https://doi.org/10.4236/ojce.2014.42012
Micek J, Kapitulík J (2003) Median filter. Journal of Information, Control and Management Systems 1:51–56
Patidar PK, Dadheech P (2019) Performance of fuzzy filter and mean filter for removing gaussian noise. International Journal of Computer Applications 182:29–35, DOI: https://doi.org/10.5120/ijca2019918399
Rastogi VK (2008) Instrumentation and monitoring of underground structures and metro railway tunnels. World tunnel congress 2008 — Underground facilities for better environment and safety, September 22–24, Agra, India, 795–808
Shi P, Zhang D, Pan J, Liu W (2016) Geological investigation and tunnel excavation aspects of the weakness zones of Xiang’an subsea tunnels in China. Rock Mechanics and Rock Engineering 49:4853–4867, DOI: https://doi.org/10.1007/s00603-016-1076-z
Teoh SH, Ibrahim H (2012) Median filtering frameworks for reducing impulse noise from grayscale digital images: A literature survey. International Journal of Future Computer and Communication 1:323–326, DOI: https://doi.org/10.7763/IJFCC.2012.V1.87
Underground facilities for better environment and safety, September 22–24, Agra, India, 795–808
Wettayaprasit W, Laosen N, Chevakidagarn S (2007) Data filtering technique for neural networks forecasting. Proceedings of the 7th WSEAS international conference on simulation, modelling and optimization, September 15–17, Beijing, China, 225–230
Zhou CH, Chen B, Gao Y, Zhang C, Guo ZJ (2011) A technique of filtering dirty data based on temporal-spatial correlation in wireless sensor network. Procedia Environmental Sciences 10:511–516, DOI: https://doi.org/10.1016/j.proenv.2011.09.083
Zhu Y, Huang C (2012) An improved median filtering algorithm for image noise reduction. Physics Procedia 25:609–616, DOI: https://doi.org/10.1016/j.phpro.2012.03.133
Acknowledgments
This research was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA). (Project: Research of Advanced Technology for Construction and Operation of Underground Transportation Infrastructure, No. 21UUTI-B157787-02).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Choi, SI., Shim, S., Kong, SM. et al. Efficiency Analysis of Filter-Based Calibration Technique to Improve Tunnel Measurement Reliability. KSCE J Civ Eng 26, 2926–2938 (2022). https://doi.org/10.1007/s12205-022-0891-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-022-0891-x