{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:45:46Z","timestamp":1742913946813,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031639883"},{"type":"electronic","value":"9783031639890"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-63989-0_21","type":"book-chapter","created":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T21:01:50Z","timestamp":1721336510000},"page":"408-425","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Let\u2019s Vibrate with\u00a0Vibration: Augmenting Structural Engineering with\u00a0Low-Cost Vibration Sensing"],"prefix":"10.1007","author":[{"given":"Masfiqur","family":"Rahaman","sequence":"first","affiliation":[]},{"given":"Md. Nazmul Hasan","family":"Sakib","sequence":"additional","affiliation":[]},{"given":"Nafisa","family":"Islam","sequence":"additional","affiliation":[]},{"given":"Saiful Islam","family":"Salim","sequence":"additional","affiliation":[]},{"given":"Uday","family":"Kamal","sequence":"additional","affiliation":[]},{"given":"Raihan","family":"Rasheed","sequence":"additional","affiliation":[]},{"given":"A. B. M. Alim Al","family":"Islam","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,19]]},"reference":[{"issue":"1","key":"21_CR1","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TVT.2014.2317736","volume":"64","author":"R Ahmed","year":"2014","unstructured":"Ahmed, R., El Sayed, M., Gadsden, S.A., Tjong, J., Habibi, S.: Automotive internal-combustion-engine fault detection and classification using artificial neural network techniques. IEEE Trans. Veh. Technol. 64(1), 21\u201333 (2014)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"21_CR2","unstructured":"Bachmann, H., et\u00a0al.: Vibration problems in structures: practical guidelines. Birkh\u00e4user (2012)"},{"issue":"5","key":"21_CR3","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/s40313-016-0255-1","volume":"27","author":"TS Barbosa","year":"2016","unstructured":"Barbosa, T.S., Ferreira, D.D., Pereira, D.A., Magalh\u00e3es, R.R., Barbosa, B.H.: Fault detection and classification in cantilever beams through vibration signal analysis and higher-order statistics. J. Control Autom. Electr. Syst. 27(5), 535\u2013541 (2016). https:\/\/doi.org\/10.1007\/s40313-016-0255-1","journal-title":"J. Control Autom. Electr. Syst."},{"key":"21_CR4","doi-asserted-by":"publisher","unstructured":"Berlin, E., Van\u00a0Laerhoven, K.: Sensor networks for railway monitoring: detecting trains from their distributed vibration footprints. In: 2013 IEEE International Conference on Distributed Computing in Sensor Systems, pp. 80\u201387. IEEE (2013). https:\/\/doi.org\/10.1109\/DCOSS.2013.38","DOI":"10.1109\/DCOSS.2013.38"},{"key":"21_CR5","doi-asserted-by":"publisher","first-page":"2490","DOI":"10.1016\/j.matpr.2021.08.270","volume":"56","author":"MHR Bhuiyan","year":"2022","unstructured":"Bhuiyan, M.H.R., Arafat, I.M., Rahaman, M., Toha, T.R., Alam, S.M.M.: Towards devising a vibration based machinery health monitoring system. Mater. Today Proc. 56, 2490\u20132496 (2022)","journal-title":"Mater. Today Proc."},{"key":"21_CR6","unstructured":"Brain, G.: Tensorflow (2015). https:\/\/www.tensorflow.org\/"},{"issue":"5","key":"21_CR7","doi-asserted-by":"publisher","first-page":"953","DOI":"10.3390\/en12050953","volume":"12","author":"P Casoli","year":"2019","unstructured":"Casoli, P., Pastori, M., Scolari, F., Rundo, M.: A vibration signal-based method for fault identification and classification in hydraulic axial piston pumps. Energies 12(5), 953 (2019). https:\/\/doi.org\/10.3390\/en12050953","journal-title":"Energies"},{"key":"21_CR8","doi-asserted-by":"publisher","unstructured":"Chakraborty, T., Khan, T.A., Islam, A.A.A.: Poster: Railcop: detecting missing rail on railway using wireless sensor networks. In: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services Companion, pp. 16\u201316 (2016). https:\/\/doi.org\/10.1145\/2938559.2948844","DOI":"10.1145\/2938559.2948844"},{"key":"21_CR9","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.jnca.2019.01.018","volume":"131","author":"T Chakraborty","year":"2019","unstructured":"Chakraborty, T., et al.: A new network paradigm for low-cost and lightweight real-time communication between train and rail track to detect missing and faulty rail blocks. J. Netw. Comput. Appl. 131, 40\u201354 (2019). https:\/\/doi.org\/10.1016\/j.jnca.2019.01.018","journal-title":"J. Netw. Comput. Appl."},{"key":"21_CR10","unstructured":"Chollet, F.: Keras (2015). https:\/\/keras.io\/"},{"key":"21_CR11","unstructured":"Garrity, P., Bhattacharyya, S., Shen, C., Dawadi, D., Panja, B.: Vibration monitoring and analysis using a wireless sensor network (WSN) to classify vehicles"},{"issue":"4","key":"21_CR12","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s11831-015-9145-0","volume":"23","author":"D Goyal","year":"2016","unstructured":"Goyal, D., Pabla, B.: The vibration monitoring methods and signal processing techniques for structural health monitoring: a review. Arch. Comput. Methods Eng. 23(4), 585\u2013594 (2016). https:\/\/doi.org\/10.1007\/s11831-015-9145-0","journal-title":"Arch. Comput. Methods Eng."},{"key":"21_CR13","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.jsv.2016.05.027","volume":"377","author":"O Janssens","year":"2016","unstructured":"Janssens, O., et al.: Convolutional neural network based fault detection for rotating machinery. J. Sound Vib. 377, 331\u2013345 (2016). https:\/\/doi.org\/10.1016\/j.jsv.2016.05.027","journal-title":"J. Sound Vib."},{"issue":"3","key":"21_CR14","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1504\/PIE.2019.10022055","volume":"13","author":"A Joshuva","year":"2019","unstructured":"Joshuva, A., Sugumaran, V.: Selection of a meta classifier-data model for classifying wind turbine blade fault conditions using histogram features and vibration signals: a data-mining study. Progress Ind. Ecol. Int. J. 13(3), 232\u2013251 (2019)","journal-title":"Progress Ind. Ecol. Int. J."},{"key":"21_CR15","unstructured":"keras: Adam optimizer (2020). https:\/\/keras.io\/api\/optimizers\/adam\/"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"K\u00fc\u00e7\u00fckbay, S.E., Sert, M., Yazici, A.: Use of acoustic and vibration sensor data to detect objects in surveillance wireless sensor networks. In: 2017 21st International Conference on Control Systems and Computer Science (CSCS), pp. 207\u2013212. IEEE (2017)","DOI":"10.1109\/CSCS.2017.35"},{"key":"21_CR17","unstructured":"scikit learn: Machine learning in python (2020). https:\/\/scikit-learn.org\/stable\/"},{"issue":"6","key":"21_CR18","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1016\/j.engstruct.2005.10.017","volume":"28","author":"JJ Lee","year":"2006","unstructured":"Lee, J.J., Yun, C.B.: Damage diagnosis of steel girder bridges using ambient vibration data. Eng. Struct. 28(6), 912\u2013925 (2006)","journal-title":"Eng. Struct."},{"issue":"12","key":"21_CR19","doi-asserted-by":"publisher","first-page":"1779","DOI":"10.1016\/j.engstruct.2004.06.014","volume":"26","author":"Q Li","year":"2004","unstructured":"Li, Q., Wu, J., Liang, S., Xiao, Y., Wong, C.: Full-scale measurements and numerical evaluation of wind-induced vibration of a 63-story reinforced concrete tall building. Eng. Struct. 26(12), 1779\u20131794 (2004)","journal-title":"Eng. Struct."},{"key":"21_CR20","unstructured":"LLC, G.: Kaggle (2010). https:\/\/www.kaggle.com\/"},{"key":"21_CR21","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.ymssp.2011.06.011","volume":"28","author":"F Magalh\u00e3es","year":"2012","unstructured":"Magalh\u00e3es, F., Cunha, \u00c1., Caetano, E.: Vibration based structural health monitoring of an arch bridge: from automated OMA to damage detection. Mech. Syst. Signal Process. 28, 212\u2013228 (2012)","journal-title":"Mech. Syst. Signal Process."},{"issue":"1","key":"21_CR22","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/JBHI.2016.2633287","volume":"21","author":"D Ravi","year":"2016","unstructured":"Ravi, D., Wong, C., Lo, B., Yang, G.Z.: A deep learning approach to on-node sensor data analytics for mobile or wearable devices. IEEE J. Biomed. Health Inform. 21(1), 56\u201364 (2016)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"21_CR23","unstructured":"Richman, M.S., Deadrick, D.S.: Seismic method for vehicle detection and vehicle weight classification (2013), US Patent 8,405,524"},{"issue":"2","key":"21_CR24","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1109\/JSEN.2016.2628858","volume":"17","author":"J Rivas","year":"2016","unstructured":"Rivas, J., Wunderlich, R., Heinen, S.J.: Road vibrations as a source to detect the presence and speed of vehicles. IEEE Sens. J. 17(2), 377\u2013385 (2016)","journal-title":"IEEE Sens. J."},{"issue":"5","key":"21_CR25","doi-asserted-by":"publisher","first-page":"1763","DOI":"10.1213\/ANE.0000000000002864","volume":"126","author":"P Schober","year":"2018","unstructured":"Schober, P., Boer, C., Schwarte, L.A.: Correlation coefficients: appropriate use and interpretation. Anesthesia Analgesia 126(5), 1763\u20131768 (2018). https:\/\/doi.org\/10.1213\/ANE.0000000000002864","journal-title":"Anesthesia Analgesia"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Sigmund, K.J., Shelley, S.J., Bauer, M., Heitkamp, F.: Analysis of vehicle vibration sources for automatic differentiation between gas and diesel piston engines. In: Automatic Target Recognition XXII, vol.\u00a08391, p. 839109. International Society for Optics and Photonics (2012)","DOI":"10.1117\/12.919166"},{"key":"21_CR27","unstructured":"Sparkfun: Arduino mega 2560. https:\/\/www.sparkfun.com\/products\/11061"},{"key":"21_CR28","unstructured":"Sparkfun: Piezo element. https:\/\/www.sparkfun.com\/products\/10293"},{"key":"21_CR29","unstructured":"Techshop: Sim900a (2017). https:\/\/www.techshopbd.com\/product-categories\/eval-board\/2041\/sim900a-kit-techshop-bangladesh"},{"key":"21_CR30","doi-asserted-by":"publisher","unstructured":"Testoni, N., Zonzini, F., Marzani, A., Scarponi, V., De\u00a0Marchi, L.: A tilt sensor node embedding a data-fusion algorithm for vibration-based SHM. Electronics 8(1), 45 (2019). https:\/\/doi.org\/10.3390\/electronics8010045, https:\/\/www.mdpi.com\/2079-9292\/8\/1\/45","DOI":"10.3390\/electronics8010045"},{"issue":"4","key":"21_CR31","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1177\/1475921716643948","volume":"15","author":"F Ubertini","year":"2016","unstructured":"Ubertini, F., Comanducci, G., Cavalagli, N.: Vibration-based structural health monitoring of a historic bell-tower using output-only measurements and multivariate statistical analysis. Struct. Health Monit. 15(4), 438\u2013457 (2016)","journal-title":"Struct. Health Monit."},{"key":"21_CR32","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.patrec.2018.02.010","volume":"119","author":"J Wang","year":"2019","unstructured":"Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. Pattern Recogn. Lett. 119, 3\u201311 (2019)","journal-title":"Pattern Recogn. Lett."},{"issue":"10","key":"21_CR33","doi-asserted-by":"publisher","first-page":"7990","DOI":"10.1109\/TIM.2020.2982814","volume":"69","author":"F Zonzini","year":"2020","unstructured":"Zonzini, F., Malatesta, M.M., Bogomolov, D., Testoni, N., Marzani, A., De Marchi, L.: Vibration-based SHM with upscalable and low-cost sensor networks. IEEE Trans. Instrum. Meas. 69(10), 7990\u20137998 (2020). https:\/\/doi.org\/10.1109\/TIM.2020.2982814","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Mobile and Ubiquitous Systems: Computing, Networking and Services"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-63989-0_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T21:10:06Z","timestamp":1721337006000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-63989-0_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031639883","9783031639890"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-63989-0_21","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"19 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MobiQuitous","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Melbourne, VIC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mobiquitous2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mobiquitous.eai-conferences.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}