Abstract
Monitoring pavement sub-surface layer thicknesses is essential to ensure stable pavement performance under heavy traffic loading. In addition, accurate estimation of pavement subsurface layer thicknesses is required for pavement condition evaluation and remaining life analysis. Traditionally this vital information is ascertained using conventional techniques such as coring/drilling at discrete locations, which are often destructive. In contrast, ground-penetrating radar (GPR) is a non-destructive proximal sensing technique gaining popularity in pavement structural condition monitoring and thickness estimation. In this work, data collected using a 1.5 GHz ground-coupled GPR system is used to estimate asphalt layer thicknesses for a 3 km long tollway in Queensland, Australia. An automated adaptative fuzzy inference system is proposed to evaluate pavement conditions. Specific parameters need to be considered before feeding inputs to the fuzzy block. The segmentation of a large section is based on mean, standard deviation, and variation in thicknesses. The inputs to the fuzzy module are boundary limits in thickness variations and thickness counts that fall within the standard distribution curve. The fuzzy module uses Mamdani fuzzy inference with triangular and trapezoidal membership functions. The rules are designed to determine the priority of the expert system, which is input dependent. The output from the fuzzy module is a pavement condition classification rating which is a pavement performance indicator. Successful implementation of this algorithm is envisaged to benefit the pavement engineers in planning rehabilitation and maintenance of existing infrastructure.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Karim, D.F., Rubasi, D.K.A.H., Saleh, D.A.A.: The road pavement condition index (PCI) evaluation and maintenance: a case study of Yemen. Org. Technol. Manag. Construct.: Int. J. 8(1), 1446–1455 (2016)
Arhin, S.A., Williams, L.N., Ribbiso, A., Anderson, M.F.: Predicting pavement condition index using international roughness index in a dense urban area. J. Civ. Eng. Res. 5(1), 10–17 (2015)
Pinatt, J.M., Chicati, M.L., Ildefonso, J.S., Filetti, C.R.G.D.A.: Evaluation of pavement condition index by different methods: case study of Maringá, Brazil. Transp. Res. Interdisc. Perspect. 4, 100100 (2020)
Evdorides, H.: A prototype knowledge-based system for pavement analysis. Ph.D. these titled. University of Birmingham (1994)
Ismail, N., Ismail, A., Atiq, R.: An overview of expert systems in pavement management. Eur. J. Sci. Res. 30(1), 99–111 (2009)
Setyawan, A., Nainggolan, J., Budiarto, A.: Predicting the remaining service life of road using pavement condition index. Proc. Eng. 125, 417–423 (2015)
Khamzin, A.K., Varnavina, A.V., Torgashov, E.V., Anderson, N.L., Sneed, L.H.: Utilization of air-launched ground penetrating radar (GPR) for pavement condition assessment. Construct. Build. Mater. 141, 130–139 (2017)
Shahnazari, H., Tutunchian, M.A., Mashayekhi, M., Amini, A.A.: Application of soft computing for prediction of pavement condition index. J. Transp. Eng. 138(12), 1495–1506 (2012)
Nguyen, T., Nguyen, T., Sidorov, D.N., Dreglea, A.: Machine learning algorithms application to road defects classification. Intell. Decis. Technol. 12(1), 59–66 (2018)
Pongpaibool, P., Tangamchit, P., Noodwong, K.: Evaluation of road traffic congestion using fuzzy techniques. In: TENCON 2007 - 2007 IEEE Region 10 Conference, 30 October–2 November 2007, pp. 1–4 (2007)
Shah, Y.U., Jain, S.S., Tiwari, D., Jain, M.K.: Development of overall pavement condition index for urban road network. Proc. - Soc. Behav. Sci. 104, 332–341 (2013)
Mahmood, M., Rahman, M., Nolle, L., Mathavan, S.: A fuzzy logic approach for pavement section classification. Int. J. Pavement Res. Technol. 6(5), 620–626 (2013)
MATLAB and Statistics Toolbox Release 2020b, Natick, Massachusetts, United States (2020)
Acknowledgements
This research work is part of a research project (Project No. IH18.10.1) sponsored by the Smart Pavements Australia Research Collaboration (SPARC) Hub at the Department of Civil Engineering, Monash University, funded by the Australia Research Council (ARC) Industrial Transformation Research (ITRH) Scheme (Grant number: IH180100010). The authors gratefully acknowledge the financial and in-kind support from Pavement Management Services (PMS), Monash University and SPARC Hub. Also, the financial and in-kind support provided by Transurban to collect GPR data on a toll road in Queensland, Australia is highly acknowledged.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Singh, N., Kishore, K., Deo, R., Lu, Y., Urbaez, E., Kodikara, J. (2023). Adaptive Fuzzy Inference System for Automated Pavement Condition Evaluation of Large Pavement Sections from Ground Penetrating Radar (GPR) Thickness Data. In: Gomes Correia, A., Azenha, M., Cruz, P.J.S., Novais, P., Pereira, P. (eds) Trends on Construction in the Digital Era. ISIC 2022. Lecture Notes in Civil Engineering, vol 306. Springer, Cham. https://doi.org/10.1007/978-3-031-20241-4_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-20241-4_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20240-7
Online ISBN: 978-3-031-20241-4
eBook Packages: EngineeringEngineering (R0)