bd186b9b-7952-4528-a105-823f9c759e1120210505045306534wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT2224-34961790-507910.37394/232015http://wseas.org/wseas/cms.action?id=40311820211820211710.37394/232015.2021.17https://wseas.org/cms.action?id=23258VincenzoBarrileDiceam – Civil, Energy Environmental and Material Engineering Department, Mediterranea University,località Feo Di Vito, 89124 Reggio Calabria, ItalyRossellaNocera(Dibt) – Biosciences and Territory Department, University of Molise, via Duca Degli Abruzzi, 86039 Termoli (Cb) Italy, ItalySalvatoreCalcagnoDiceam – Civil, Energy Environmental and Material Engineering Department, Mediterranea University,località Feo Di Vito, 89124 Reggio Calabria, ItalyOur society is heavily dependent on many interdependent and complex critical infrastructures. Deficiencies in the functionality of the transportation network (e.g., vehicular traffic interruptions or limitations) can cause enormous inconvenience to communities and people. The Italian transport infrastructure heritage and new infrastructure construction is so relevant that the issue of its preservation and safety has become a priority. Specialistic advice is therefore required to understand if the static behaviour of these infrastructure has changed significantly after extraordinary events (e.g., earthquakes, landslides). With the advent of the internet of things (IoT), infrastructures are becoming smart and procedures simpler. In the framework of smart infrastructure development, we implemented an experimental system that integrates soft computing and geomatic methodologies for solving early warning problems. This system, which has been tested on the Petrace bridge (Southern Italy), is able to generate forecasting information on the infrastructure behaviour over time, mainly exploiting geomatic parameters. We built this "early warning/predictive" system through integration of several significant (geometric/structural) infrastructure models, which have been merged into a final "type" model. The results derived from various possible scenarios have been implemented in a neural network. The only system’s input is represented by displacement measurements acquired by sensors placed on the infrastructure, and the output consists in an estimation of different risk levels. Sensor data are then transmitted to a control unit that sends them to a processing server, where the calculation system is hosted. All received data and model results are displayed on the Wordpress platform with colour codes calibrated on the calculated risk thresholds.552021552021466478https://www.wseas.org/multimedia/journals/environment/2021/a905115-623.pdf10.37394/232015.2021.17.45https://www.wseas.org/multimedia/journals/environment/2021/a905115-623.pdf10.12989/sss.2015.16.2.263Gentile, C., Cabboi, A.: Vibration-based structural health monitoring of stay cables by microwave remote sensing. Smart Struct. Syst. 16, 263–280 (2015) 10.1061/(asce)cf.1943-5509.0000844Harris, D.K., Brooks, C.N., Ahlborn, T.M.: Synthesis of field performance of remote sensing strategies for condition assessment of inservice bridges in Michigan. J. Perform. Constr. Facil. 30, 04016027 (2016) 10.1061/(asce)be.1943-5592.0000303Vaghefi, K., et al.: Evaluation of commercially available remote sensors for highway bridge condition assessment. J. Bridge Eng. 17, 886– 895 (2012) 646 V. Barrile et al. 10.1061/47625(404)16Chen, S.-E., Liu, W., Dai, K., Bian, H., Hauser, E.: Remote sensing for bridge monitoring. In: Condition, Reliability, and Resilience Assessment of Tunnels and Bridges, vol. 214, pp. 118–125. Geotechnical Special Publication, Reston (2011) Rytter, A.: Vibration BASED inspection of civil engineering structures. Department of Building Technology and Structural Engineering, Aalborg University, Denmark (1993) Alexander Makarenkot, Multiple-Valued and Branching Neural Networks, Engineering World, pp. 20-28, Volume 2, 2020. Gulnara N. Nabiyeva, Stephen M. Wheeler, Geographic Information Systems as a Tool to Support the Sustainable Development Goals, Engineering World, pp. 219-226, Volume 2, 2020. 10.1016/j.measurement.2013.08.014Elnabwy, M.T., Kaloop, M.R., Elbeltagi, E.: Talkha steel highway bridge monitoring and movement identification using RTK-GPS technique. Measurement 46, 4282–4292 (2013) 10.1016/j.compstruc.2011.10.019Psimoulis, P.A., Stiros, S.C.: A supervised learning computer-based algorithm to derive the amplitude of oscillations of structures using noisy GPS and robotic theodolites (RTS) records. Comput. Struct. 92–93, 337–348 (2012) 10.1016/j.ijsolstr.2005.07.024Zhu, X.Q., Law, S.S.: Wavelet-based crack identification of bridge beam from operational deflection time history. Int. J. Solids Struct. 43, 2299–2317 (2006) 10.4028/www.scientific.net/kem.413-414.285Zhang, W.W., Wang, Z.H., Ma, H.W.: Studies on wavelet packet-based crack detection for a beam under the moving load. Key Eng. Mater. 413–414, 285–290 (2009) 10.1016/j.ymssp.2011.06.007Hester, D., González, A.: A wavelet-based damage detection algorithm based on bridge acceleration response to a vehicle. Mech. Syst. Signal Process. 28, 145–166 (2012) Bradley, M., González, A., Hester D.: Analysis of the structural response to a moving load using empirical mode decomposition, p. 117. Taylor & Francis, London (2010) 10.1142/9789812703347_0012Huang, N.E., Huang, K., Chiang, W.-L.: HHT based bridge structural health-monitoring method. In: Hilbert-Huang Transform and Its Applications, pp. 263–287. World Scientific (2014) 10.1016/j.jsv.2013.01.024González, A., Hester, D.: An investigation into the acceleration response of a damaged beamtype structure to a moving force. J. Sound Vib. 332, 3201–3217 (2013) 10.1177/1077546314564587He, W., Zhu, S.: Moving load-induced response of damaged beam and its application in damage localization. J. Vib. Control 22, 3601–3617 (2016) 10.1002/stc.1749OBrien, E., Carey, C., Keenahan, J.: Bridge damage detection using ambient traffic and moving force identification. Struct. Control Health Monit. 22, 1396–1407 (2015) Li, Z.H., Au, F.T.K.: Damage detection of a continuous bridge from response of a moving vehicle. Shock Vib. 2014, 1–7 (2014) Park, J., Moon, D.-S., Spencer, B.F.: Neutralaxis identification using strain and acceleration measurements. In: The 2017 World Congress on Advances in Structural Engineering and Mechanics (ASEM 2017), Seoul, Korea (2017) 10.3390/su12041606Barrile, V., Fotia, A., Leonardi, G., Pucinotti, R., Geomatics and Soft Computing Techniques for Infrastructural Monitoring, Sustainability 2020, Vol.12, 1606. doi:10.3390/su12041606 10.1007/978-3-030-58811-3_46Barrile, V., Fotia, A., Bernardo, E., Bilotta, G., Modafferi, A., Road Infrastructure Monitoring: An Experimental Geomatic Integrated System, In: Gervasi O. et al. (eds) Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12252. Springer, Cham 2020, pp. 634-648. Doi:10.1007/978-3-030-58811-3_4610.1088/0964-1726/22/7/075030Sigurdardottir, D.H., Glisic, B.: Neutral axis as damage sensitive feature. Smart Mater. Struct. 22, 075030 (2013) 10.1088/0964-1726/23/12/125042Sigurdardottir, D.H., Glisic, B.: Detecting minute damage in beam-like structures using the neutral axis location. Smart Mater. Struct. 23, 125042 (2014) 10.1007/s13349-015-0136-5Sigurdardottir, D.H., Glisic, B.: The neutral axis location for structural health monitoring: an overview. J. Civ. Struct. Health Monit. 5(5), 703–713 (2015). https://doi.org/10.1007/s13349-015-0136-5 Ye, X., Jin, T., Yun, C.: A review on deep learning-based structural health monitoring of civil infrastructures. Smart Struct. Syst. 24(5), 567–585 (2019) 10.5194/isprs-archives-xlii-2-w11-187-2019Barrile, V., Candela, G., Fotia, A.: Point cloud segmentation using image processing techniques for structural analysis. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XLII-2/W11, 187–193 (2019) 10.1007/978-3-030-24305-0_21Barrile, V., Candela, G., Fotia, A., Bernardo, E.: UAV survey of bridges and viaduct: workflow and application. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11622, pp. 269–284. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24305-0_21 10.1016/j.engstruct.2016.02.014Voutetaki, M.E., Papadopoulos, N.A., Angeli, G.M., Providakis, C.P.: Investigation of a new experimental method for damage assessment of RC beams failing in shear using piezoelectric transducers. Eng. Struct. 114, 226–240 (2016) 10.12989/sss.2015.15.4.997Karayannis, C.G., Voutetaki, M.E., Chalioris, C.E., Providakis, C.P., Angeli, G.M.: Detection of flexural damage stages for RC beams using Piezoelectric sensors (PZT). Smart Struct. Syst. 15, 997–1018 (2015) 10.1016/j.measurement.2013.09.046Kaloop, M.R., Li, H.: Multi input-single output models identification of tower bridge movements using GPS monitoring system. Measurement 47, 531–539 (2014) 10.3390/s18124312Chen, Y., Xue, X.: Advances in the structural health monitoring of bridges using piezoelectric transducers. Sensors 18, 4312 (2018) 10.3390/app9020312Liao, W.I., Hsiao, F.P., Chiu, C.K., Ho, C.E.: Structural health monitoring and interface damage detection for infill reinforced concrete walls in seismic retrofit of reinforced concrete frames using piezoceramic-based transducers under the cyclic loading. Appl. Sci. 9, 312 (2019) 10.1016/j.engstruct.2010.09.013Moschas, F., Stiros, S.: Measurement of the dynamic displacements and of the modal frequencies of a short-span pedestrian bridge using GPS and an accelerometer. Eng. Struct. 33, 10–17 (2011) Moschasa, F., Stiros, S.: Noise characteristics of short-duration, high frequency GPSrecords. In: Advanced Mathematical and Computational Tools in Metrology and Testing. Series on Advances in Mathematics for Applied Sciences, vol. 84, pp. 284–291 (2012) 10.1111/mice.12298Matarazzo, T.J., Pakzad, S.N.: Scalable structural modal identification using dynamic sensor network data with STRIDEX. Comput. Aided Civ. Infrastruct. Eng. 33(1), 4–20 (2018) 10.1016/j.apor.2018.02.020Qu, K., Tang, H.S., Agrawal, A., Cai, Y., Jiang, C.B.: Numerical investigation of hydrodynamic load on bridge deck under joint action of solitary wave and current. Appl. Ocean Res. 75, 100–116 (2018). https://doi.org/10.1016/j.apor.2018.02.020 Wu, D., Yuan, C., Kumfera, W., Liu, H.: A life-cycle optimization model using semiMarkov process for highway bridge maintenance. Appl. Math. Model. 43, 45–60 (2017) 10.1109/jsen.2013.2273309Fukuda, Y., Feng, M.Q., Narita, Y., Kaneko, S., Tanaka, T.: Vision-based displacement sensor for monitoring dynamic response using robust object search algorithm. IEEE Sens. J. 13, 4725–4732 (2013) 10.1016/j.ymssp.2018.11.015Lydon, D., Lydon, M., Taylor, S., Del Rincon, J.M., Hester, D., Brownjohn, J.: Development and field testing of a vision-based displacement system using a low cost wireless action camera. Mech. Syst. Signal Process. 121, 343–358 (2019). ISSN 0888-3270 10.3390/s20030911Li, S., Zuo, X., Li, Z., Wang, H.: Applying deep learning to continuous bridge deflection detected by fiber optic gyroscope for damage detection. Sensors 20(3), 911 (2020) 10.1061/(asce)em.1943-7889.0001046Matarazzo, T.J., Pakzad, S.N.: Structural identification for mobile sensing with missing observations. J. Eng. Mech. 142(5), 04016021 (2016) Barbaro, G., Fiamma, V., Barrile, V., Foti, G., Ielo, G. Analysis of the shoreline changes of Reggio Calabria (Italy) International Journal of Civil Engineering and Technology, 2017, 8(10), pp. 1777-1791 Barrile, V., Meduri, G., Bilotta, G. Laser scanner surveying techniques aiming to the study and the spreading of recent architectural structures. Proceedings of the 9th WSEAS International Conference on Signal, Speech and Image Processing, SSIP '09, Proc. 9th WSEAS Int. Conf. Multimedia, Internet and Video Technologies, MIV '09, 2009, pp. 92-9510.1016/j.jairtraman.2006.09.003Postorino, M.N., Barrile, V., Cotroneo, F. Surface movement ground control by means of a GPS-GIS system Journal of Air Transport Management, 2006, 12(6), pp. 375-381 Barrile, V., Bilotta, G., D’Amore, E., Meduri, G.M., Trovato, S. Structural modeling of a historic castle using close range photogrammetry International Journal of Mathematics and Computers in Simulation, 2016, 10, pp. 370-380 10.1007/978-3-030-24305-0_21Barrile, V., Candela, G., Fotia, A., Bernardo, E. UAV Survey of Bridges and Viaduct: Workflow and Application. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11622 LNCS, pp. 269-284 Barrile, V., Bilotta, G., Fotia, A. Analysis of hydraulic risk territories: Comparison between LIDAR and other different techniques for 3D modeling WSEAS Transactions on Environment and Development, 2018, 14, pp. 45-52