Abstract
The diffusion of smart infrastructures for smart cities provides new opportunities for the improvement of both road infrastructure monitoring and maintenance management.
Often pavement management is based on the periodic assessment of the elastic modulus of the bound layers (i.e., asphalt concrete layers) by means of traditional systems, such as Ground Penetrating Radar (GPR) and Falling Weight Deflectometer (FWD). Even if these methods are reliable, well-known, and widespread, they are quite complex, expensive, and are not able to provide updated information about the evolving structural health condition of the road pavement. Hence, more advanced, effective, and economical monitoring systems can be used to solve the problems mentioned above.
Consequently, the main objective of the study presented in this paper is to present and apply an innovative solution that can be used to make smarter the road pavement monitoring. In more detail, an innovative Non-Destructive Test (NDT)-based sensing unit was used to gather the vibro-acoustic signatures of road pavements with different deterioration levels (e.g. with and without fatigue cracks) of an urban road. Meaningful features were extracted from the aforementioned acoustic signature and the correlation with the elastic modulus defined using GPR and FWD data was investigated.
Results show that some of the features have a good correlation with the elastic moduli of the road section under investigation. Consequently, the innovative solution could be used to evaluate the variability of elastic modulus of the asphalt concrete layers, and to monitor with continuity the deterioration of road pavements under the traffic loads.
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R. Fedele, Smart road infrastructures through vibro-acoustic signature analyses, Smart Innov. Syst. Technol. 178 (2020) 1481–1490.
J.B. Odoki, A. Di Graziano, R. Akena, A multi-criteria methodology for optimising road investments, Proc. Inst. Civ. Eng. Transp. 168(1) (2015) 34–47.
F.G. Praticò, Roads and Loudness: A More Comprehensive Approach, Road Mater. Pavement Des. 2(4) (2001) 359–377. https://doi.org/10.1080/14680629.2001.9689908
J. Guerrero-Ibáñez, S. Zeadally, J. Contreras-Castillo, 2018. Sensor technologies for intelligent transportation systems, Sensors 18(4) (2018) 1212.
N. Bahrani, J. Blanc, P. Hornych, F. Menant, Alternate method of pavement assessment using geophones and accelerometers for measuring the pavement response, Infras. 5(3) (2020) 25 https://doi.org/10.3390/infrastructures5030025
H. Hasni, A.H. Alavi, P. Jiao, N. Lajnef, K. Chatti, K. Aono, S. Chakrabarty, A new approach for damage detection in asphalt concrete pavements using battery-free wireless sensors with non-constant injection rates, Meas. J. Inter. Meas. Confed. 110 (2017) 217–229.
M. Manosalvas-Paredes, R. Roberts, M. Barriera, K. Mantalovas, Towards more sustainable pavement management practices using embedded sensor technologies, Infras. 5(1) (2020) 4 https://doi.org/10.3390/infrastructures5010004
M. Iodice, J.M. Muggleton, E. Rustighi, The in-situ evaluation of surface-breaking cracks in asphalt using a wave decomposition method. Nondestruct. Test. Eval. (2020) https://doi.org/10.1080/10589759.2020.1764553
F.G. Praticò, R. Fedele, D. Vizzari, Significance and reliability of absorption spectra of quiet pavements, Constr. Build. Mater. 140 (2017) 274–281 https://doi.org/10.1016/j.conbuildmat.2017.02.130
F. Bianco, L. Fredianelli, F. Lo Castro, P. Gagliardi, F. Fidecaro, G. Licitra, Stabilization of a p-u sensor mounted on a vehicle for measuring the acoustic impedance of road surfaces, Sensors 20(5) (2020) 1239.
S. Cafiso, A. Di Graziano, D.G. Goulias, C. D’Agostino, Distress and profile data analysis for condition assessment in pavement management systems, Inter. J. Pavement Res. Technol. 12(5) (2019) 527–536. https://doi.org/10.1007/s42947-019-0063-7
C.W. Yi, Y.T. Chuang, C.S. Nian, Toward Crowdsourcing-Based Road Pavement Monitoring by Mobile Sensing Technologies, IEEE Trans. Intell. Transp. Syst., Piscataway, NJ, USA, 16(4) (2015) 1905–1917
F.M. Fernandes, J.C. Pais, Laboratory observation of cracks in road pavements with GPR, Constr. Build. Mater. 154 (2017) 1130–1138
R. Grace, Sensors to support the IoT for infrastructure monitoring: technology and applications for smart transport/smart buildings, MEPTEC-IoT, San Jose, CA, USA, 2015.
H. Ceylan, M.B. Bayrak, K. Gopalakrishnan, Neural networks applications in pavement engineering: A recent survey, Inter. J. Pavement Res. Technol. 7(6) (2014) 434–444.
A. Di Graziano, V. Marchetta, S. Cafiso, Structural health monitoring of asphalt pavements using smart sensor networks: A comprehensive review, J. Traffic Transp. Eng. (English Ed) 7(5) (2020) 639–651.
T. Iuele, F.G. Pratico, R. Vaiana, Fine aggregate properties vs asphalt mechanical behavior: An experimental investigation, in: Pavement and Asset Management, Proc. World Conference on Pavement and Asset Management, WCPAM, Baveno, Italy, 2017.
Y. Yu, X. Zhao, Y. Shi, J. Ou, Design of a real-time overload monitoring system for bridges and roads based on structural response. Meas, J. Inter. Meas. Confed. 46(1) (2013) 345–352. https://doi.org/10.1016/j.measurement.2012.07.006.
R. Fedele, M. Merenda, F.G. Praticò, R. Carotenuto, F.G. Della Corte, Energy harvesting for IoT road monitoring systems, Instrum. Mes. Metrol. 18(4) (2018) 605–623.
H. Hasni, A.H. Alavi, K. Chatti, N. Lajnef, A self-powered surface sensing approach for detection of bottom-up cracking in asphalt concrete pavements: Theoretical/numerical modeling. Constr. Build. Mater. 144 (2017) 728–746.
D. Mounier, H. Di Benedetto, C. Sauzéat, Determination of bituminous mixtures linear properties using ultrasonic wave propagation. Constr. Build. Mater. 36 (2012) 638–647.
Y.O. Ouma, M. Hahn, Pothole detection on asphalt pavements from 2D-colour pothole images using fuzzy c-means clustering and morphological reconstruction, Autom. Constr. 83 (2017) 196–211 https://doi.org/10.1016/j.autcon.2017.08.017
H. Ceylan, K. Gopalakrishnan, S. Kim, P.C. Taylor, M. Prokudin, A.F. Buss, Highway infrastructure health monitoring using micro-electromechanical sensors and systems (MEMS), J. Civ. Eng. Manage. 19(1) (2013) S188–S201.
R. Fedele, F.G. Pratico, G. Pellicano, Sustainable Road Infrastructures Using Smart Materials, NDT, and FEM-Based Crack Prediction, International Conference on Society with Future:Smart and Liveable Cities, Braga, Portugal, 318 (2020) 3–14.
F.G. Pratico, R. Fedele, V. Naumov, T. Sauer, Detection and monitoring of bottom-up cracks in road pavement using a machine-learning approach, Algorithms 13(4) (2020) 81. https://doi.org/10.3390/a13040081
D.G. Goulias, S. Cafiso, A. Di Graziano, S.G. Saremi, V. Currao, Condition Assessment of Bridge Decks through Ground-Penetrating Radar in Bridge Management Systems, J. Perform. Constr. Facil. 34(5) (2020) 1–13
Standard Test Method for Determining the Thickness of Bound Pavement Layers Using Short-Pulse Radar. ASTM D4748-10. ASTM International, West Conshohocken, PA, USA, 2010.
I. Al-Qadi, S. Lahouar, Part 4: Portland cement concrete pavement: measuring rebar cover depth in rigid pavements with ground-penetrating radar, Transp. Res. Rec. 1907 (2005) 80–85.
Hexagon, K2_FW GPR system (IDS GeoRadar — Part of Hexagon, 2020), https://idsgeoradar.com/products.Accessed July 2020.
S. Cafiso, A. Di Graziano, Monitoring and performance of AC pavements reinforced with steel mesh, Inter. J.Pavement Res. Technol. 2(3) (2009) 82–90.
R. Fedele, F.G. Pratico, Monitoring infrastructure asset through its acoustic signature, INTER-NOISE 2019 MADRID — 48th International Congress and Exhibition on Noise Control Engineering, Madrid, Spain, 2019.
R. Fedele, F.G. Pratico, R. Carotenuto, F.G. Della Corte, Damage detection into road pavements through acoustic signature analysis: First results, 24th International Congress on Sound and Vibration, ICSV 2017, London, UK, 2017.
M. Merenda, R. Carotenuto, F.G. Della Corte, F. G. Pratico, R. Fedele, Self-powered wireless IoT nodes for emergency management, Proc. 20th IEEE Mediterranean Electrotechnical Conference, MELECON 2020, Palermo, Italy, 2020.
P.N. Schmalzer, Long-Term Pavement Performance Program Manual for Falling Weight Deflectometer Measurements. Report FHWA-HRT-06-132. Springfield, VA, USA, 2006 https://www.fhwa.dot.gov/publications/research/infrastructure/pavements/ltpp/06132/06132.pdf.
Dyatest, ELMOD Software for Pavement Analysis. (Dynatest, 2020). www.dynatest.com. Accessed July 2020.
R. Fedele, F.G. Pratico, G. Pellicano, The prediction of road cracks through acoustic signature: Extended finite element modeling and experiments, J. Test. Eval. 49 (4) (2019) https://doi.org/10.1520/JTE20190209
E. Schubert, J. Wolfe, Timbral brightness and spectral centroid, Acta Acust. united with Acust. 92(5) (2006) 820–825.
J. M. Bland, D. G. Altman, Calculating correlation coefficients with repeated observations: part 1 -correlation within subjects, BMJ 310(6977) (1995) 446.
J. M. Bland, D. G. Altman, Statistics notes: calculating correlation coefficients with repeated observations: part 2—correlation between subjects, BMJ 310(6980) (1995) 633.
K. Y. Liang, S. L. Zeger, Longitudinal data analysis using generalized linear models, Biometrika 73(1) (1986) 13.
D. McFadden, Conditional logit analysis of qualitative choice behavior. P. Zarembka (ed.), Frontiers in Econometrics. Academic Press, Cambridge, Massachusetts, USA, 1974, pp. 105–142.
Acknowledgement
This work has been partially financed by the PRIN 2017–2022 within the ongoing project USR342, by the University of Catania within the project TIMUC (PIACERI) and by the European Commission social fund and the Calabria Region (PAC Calabria 2014–2020).
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Cafiso, S., Di Graziano, A., Fedele, R. et al. Sensor-based pavement diagnostic using acoustic signature for moduli estimation. Int. J. Pavement Res. Technol. 13, 573–580 (2020). https://doi.org/10.1007/s42947-020-6007-4
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DOI: https://doi.org/10.1007/s42947-020-6007-4