Reweighted Minimization for Networkwide Weak Spot Detection from Traffic Speed Deflectometer Measurements
Publication: Journal of Computing in Civil Engineering
Volume 37, Issue 4
Abstract
The traffic speed deflectometer (TSD) can collect network-level deflection data in a cost-effective and timely manner. However, an automated feature extraction method is needed to interpret such large amounts of data. Basis pursuit (BP) is one such technique: BP can sparsely decompose TSD measurements over a given basis or set of bases of the signal vector space that can represent a particular signal feature. For instance, the TSD surface deflection estimates can be reconstructed as a combination of wavelets, which represent continuously varying features like changes in pavement properties, plus pulses representing the response from structurally weak spots. Yet, the denoised measurements estimated by BP may either be riddled with several false positives (spikes that mismatch real weak spots) or be constructed out of true positive features with damped amplitude. This paper presents reweighed minimization (RWL1), an enhancement to BP to both correct the dampening and discard false positives. This paper introduces RWL1 with examples from simulated data to show its advantage over vanilla BP denoising, plus a demonstration featuring real TSD measurements from a networkwide survey to demonstrate RWL1’s potential as an exploratory analysis tool to detect structurally weak locations within the pavement network worthy of further investigation at the project level.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The original TSD data used in this study is currently freely available through FHWA’s InfoMaterials website (https://infomaterials.fhwa.dot.gov/). The computer code written to perform the calculations that support the findings presented herein is available from the corresponding author upon reasonable request. The cartography sources utilized for the maps presented in this study are either public domain (OpenStreetMap) or can be retrieved from the OpenDataDc website (https://opendata.dc.gov/datasets/roadway-block/explore).
Acknowledgments
The authors would like to express their gratitude to J. Daleiden from ARRB Systems Inc., for providing the NaMa’s TSD deflection data pavement surface imagery. Graham from the District of Columbia DOT for kindly furnishing GIS-friendly data utilized as base cartography in this study, and to N. Sivaneswaran and M. Elias from FHWA for their insight on the results’ interpretation and their revisions to the final manuscript.
References
Candès, E. J., M. B. Wakin, and S. P. Boyd. 2008. “Enhancing sparsity by reweighted ℓ1 minimization.” J. Fourier Anal. Appl. 14 (5–6): 877–905. https://doi.org/10.1007/s00041-008-9045-x.
Chen, S. S., D. L. Donoho, and M. A. Saunders. 2001. “Atomic decomposition by basis pursuit.” SIAM Rev. 43 (1): 129–159. https://doi.org/10.1137/S003614450037906X.
DDOT (District of Columbia DOT). 2021: “Roadway block.” Accessed July 12, 2021. https://opendata.dc.gov/datasets/roadway-block.
Deep, P., M. B. Andersen, S. Rasmussen, N. Thom, A. Marradi, and D. Lo Presti. 2020. “Evaluation of load transfer in rigid pavements by rolling wheel deflectometer and falling weight deflectometer.” Transp. Res. Procedia 45 (2020): 376–383. https://doi.org/10.1016/j.trpro.2020.03.029.
Donoho, D. L., and J. M. Johnstone. 1994. “Ideal spatial adaptation by wavelet shrinkage.” Biometrika 81 (3): 425–455. https://doi.org/10.1093/biomet/81.3.425.
FHWA (Federal Highway Administration). 2019. “Highway statistics 2019.” Accessed November 25, 2022. https://www.fhwa.dot.gov/policyinformation/statistics/2019/.
Flintsch, G. W., S. W. Katicha, J. Bryce, B. Ferne, S. Nell, and B. Diefenderfer. 2013. Assessment of continuous pavement deflection measuring technologies. Washington, DC: National Academies.
Friedman, J., T. Hastie, and R. Tibshirani. 2010. “Regularization paths for generalized linear models via coordinate descent.” J. Stat. Software 33 (1): 1. https://doi.org/10.18637/jss.v033.i01.
Hastie, T., R. Tibshirani, and J. Friedman. 2009. The elements of statistical learning: Data mining, inference, and prediction. Springer series in statistics. New York: Springer.
Hastie, T., R. Tibshirani, and M. Wainwright. 2015. Statistical learning with sparsity. The lasso and generalizations. Monographs on statistics and applied probability 143. Boca Raton, FL: CRC Press.
Katicha, S. W., G. W. Flintsch, J. Bryce, and B. Ferne. 2014. “Wavelet denoising of TSD deflection slope measurements for improved pavement structural evaluation.” Comput.-Aided Civ. Infrastruct. Eng. 29 (6): 399–415. https://doi.org/10.1111/mice.12052.
Katicha, S. W., G. W. Flintsch, and B. Ferne. 2013. “Optimal averaging and localized weak spot identification of traffic speed deflectometer measurements.” Transp. Res. Rec. 2367 (1): 43–52. https://doi.org/10.3141/2367-05.
Katicha, S. W., A. Loulizi, J. El Khouri, and G. W. Flintsch. 2016. “Adaptive false discovery rate for wavelet denoising of pavement continuous deflection measurements.” J. Comput. Civ. Eng. 35 (2): 04016049. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000603.
Mallat, S. 2008. A wavelet tour of signal processing: The sparse way. 3rd ed. Burlington, VT: Academic Press.
Mazumder, R., J. H. Friedman, and T. Hastie. 2011. “Sparsenet: Coordinate descent with nonconvex penalties.” J. Am. Stat. Assoc. 106 (495): 1125–1138. https://doi.org/10.1198/jasa.2011.tm09738.
Phares, B. M., H. Ceyland, R. Souleyrette, O. Smadi, and K. Gopalakrishnan. 2008. Development of a high-speed rigid pavement analyzer—Phase I feasibility and need study.” CTRE Project 06- 281 Rep. Ames, IA: Iowa State Univ.
Rada, G. R., S. Nazarian, B. A. Visintine, R. V. Siddharthan, and S. Thyagarajan. 2016. Pavement structural evaluation at the network level. Washington, DC: Federal Highway Administration.
Scavone, M., S. W. Katicha, and G. W. Flintsch. 2021. “Identifying weak joints in jointed concrete and composite pavements from traffic speed deflectometer measurements by basis pursuit.” J. Comput. Civ. Eng. 35 (2): 04020062. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000951.
Tibshirani, R. 1996. “Regression shrinkage and selection via the Lasso.” J. R. Stat. Soc.: Ser. B (Methodol.) 58 (1): 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.
Van Cauwelaert, F. 2004. Pavement design and evaluation: The required mathematics and applications. Brussels, Belgium: Federation of the Belgian Cement Industry.
Information & Authors
Information
Published In
Journal of Computing in Civil Engineering
Volume 37 • Issue 4 • July 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Apr 2, 2022
Accepted: Feb 7, 2023
Published online: Apr 27, 2023
Published in print: Jul 1, 2023
Discussion open until: Sep 27, 2023
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.