{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T01:56:21Z","timestamp":1775181381355,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T00:00:00Z","timestamp":1705363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"reCITY","award":["ARS01_00592"],"award-info":[{"award-number":["ARS01_00592"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In a seismic context, it is fundamental to deploy distributed sensor networks for Structural Health Monitoring (SHM). Indeed, regularly gathering data from a structure\/infrastructure gives insight on the structural health status, and Artificial Intelligence (AI) technologies can help in exploiting this information to generate early warnings useful for decision-making purposes. With a perspective of developing a remote monitoring platform for the built environment in a seismic context, the authors tested self-sensing concrete beams in loading tests, focusing on the measured electrical impedance. The formed cracks were objectively assessed through a vision-based system. Also, a comparative analysis of AI-based and statistical prediction methods, including Prophet, ARIMA, and SARIMAX, was conducted for predicting electrical impedance. Results show that the real part of electrical impedance is highly correlated with the applied load (Pearson\u2019s correlation coefficient &gt; 0.9); hence, the piezoresistive ability of the manufactured specimens has been confirmed. Concerning prediction methods, the superiority of the Prophet model over statistical techniques was demonstrated (Mean Absolute Percentage Error, MAPE &lt; 1.00%). Thus, the exploitation of electrical impedance sensors, vision-based systems, and AI technologies can be significant to enhance SHM and maintenance needs prediction in the built environment.<\/jats:p>","DOI":"10.3390\/s24020572","type":"journal-article","created":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T11:37:18Z","timestamp":1705405038000},"page":"572","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["In the Direction of an Artificial Intelligence-Enabled Monitoring Platform for Concrete Structures"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7982-208X","authenticated-orcid":false,"given":"Gloria","family":"Cosoli","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering and Mathematical Sciences, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6658-5273","authenticated-orcid":false,"given":"Maria Teresa","family":"Calcagni","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Mathematical Sciences, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4570-9249","authenticated-orcid":false,"given":"Giovanni","family":"Salerno","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Mathematical Sciences, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5281-9200","authenticated-orcid":false,"given":"Adriano","family":"Mancini","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9053-0758","authenticated-orcid":false,"given":"Gagan","family":"Narang","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4140-6424","authenticated-orcid":false,"given":"Alessandro","family":"Galdelli","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0891-5649","authenticated-orcid":false,"given":"Alessandra","family":"Mobili","sequence":"additional","affiliation":[{"name":"Department of Science and Engineering of Matter, Environment and Urban Planning, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5805-6822","authenticated-orcid":false,"given":"Francesca","family":"Tittarelli","sequence":"additional","affiliation":[{"name":"Department of Science and Engineering of Matter, Environment and Urban Planning, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"},{"name":"Institute of Atmospheric Sciences and Climate, National Research Council (ISAC-CNR), 40129 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3372-7543","authenticated-orcid":false,"given":"Gian Marco","family":"Revel","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Mathematical Sciences, Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1201\/9781351182805-3","article-title":"Life-cycle of structural systems: Recent achievements and future directions","volume":"12","author":"Frangopol","year":"2019","journal-title":"Struct. Infrastruct. Syst."},{"key":"ref_2","first-page":"100442","article-title":"Multi-route fusion method of GNSS and accelerometer for structural health monitoring","volume":"32","author":"Shen","year":"2023","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1177\/1045389X221128580","article-title":"An island-bridge packaging piezoelectric sensor for structural health monitoring in high-strain environments","volume":"34","author":"Liao","year":"2023","journal-title":"J. Intell. Mater. Syst. Struct."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1177\/1475921718757405","article-title":"Computer vision and deep learning\u2013based data anomaly detection method for structural health monitoring","volume":"18","author":"Bao","year":"2019","journal-title":"Struct. Health Monit."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"132","DOI":"10.21014\/acta_imeko.v10i4.1140","article-title":"Continuous monitoring of the health status of cement-based structures: Electrical impedance measurements and remote monitoring solutions","volume":"10","author":"Giulietti","year":"2021","journal-title":"ACTA IMEKO"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4672","DOI":"10.1109\/JSEN.2023.3240092","article-title":"Noncontact Sensing Techniques for AI-Aided Structural Health Monitoring: A Systematic Review","volume":"23","author":"Sabato","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sun, L., Li, C., Zhang, C., Liang, T., and Zhao, Z. (2019). The Strain Transfer Mechanism of Fiber Bragg Grating Sensor for Extra Large Strain Monitoring. Sensors, 19.","DOI":"10.3390\/s19081851"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"109708","DOI":"10.1016\/j.ymssp.2022.109708","article-title":"A framework for quantifying the value of vibration-based structural health monitoring","volume":"184","author":"Kamariotis","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"03122006","DOI":"10.1061\/(ASCE)ST.1943-541X.0003498","article-title":"A Review of Data Management and Visualization Techniques for Structural Health Monitoring Using BIM and Virtual or Augmented Reality","volume":"149","author":"Sadhu","year":"2022","journal-title":"J. Struct. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"120808","DOI":"10.1016\/j.talanta.2020.120808","article-title":"Recent advances in self-actuation and self-sensing materials: State of the art and future perspectives","volume":"212","author":"Liu","year":"2020","journal-title":"Talanta"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.conbuildmat.2016.05.037","article-title":"The review of pore structure evaluation in cementitious materials by electrical methods","volume":"117","author":"Tang","year":"2016","journal-title":"Constr. Build. Mater."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.conbuildmat.2017.04.088","article-title":"Evaluation of mortar setting time by using electrical resistivity measurements","volume":"146","author":"Yim","year":"2017","journal-title":"Constr. Build. Mater."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.sna.2008.10.001","article-title":"Experimental study on use of nickel powder-filled Portland cement-based composite for fabrication of piezoresistive sensors with high sensitivity","volume":"149","author":"Han","year":"2009","journal-title":"Sens. Actuators A Phys."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fisher, R.M., Cardoso, R.C., Collins, E.C., Dadswell, C., Dennis, L.A., Dixon, C., Farrell, M., Ferrando, A., Huang, X., and Jump, M. (2021). An Overview of Verification and Validation Challenges for Inspection Robots. Robot, 10.","DOI":"10.3390\/robotics10020067"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"50131","DOI":"10.1109\/ACCESS.2020.2980359","article-title":"Monitoring Ancient Buildings: Real Deployment of an IoT System Enhanced by UAVs and Virtual Reality","volume":"8","author":"Bacco","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1080\/15732479.2020.1862251","article-title":"The value of structural health monitoring in seismic emergency management of bridges","volume":"18","author":"Giordano","year":"2020","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"D\u2019Errico, L., Franchi, F., Graziosi, F., Marotta, A., Rinaldi, C., Boschi, M., and Colarieti, A. (2019, January 15\u201318). Structural health monitoring and earthquake early warning on 5g urllc network. Proceedings of the 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland.","DOI":"10.1109\/WF-IoT.2019.8767329"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.conbuildmat.2011.08.082","article-title":"Characterisation of concrete cracking during laboratorial tests using image processing","volume":"28","year":"2012","journal-title":"Constr. Build. Mater."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.conbuildmat.2017.04.096","article-title":"Assessment of cracks on concrete bridges using image processing supported by laser scanning survey","volume":"146","author":"Puente","year":"2017","journal-title":"Constr. Build. Mater."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"012091","DOI":"10.1088\/1742-6596\/1783\/1\/012091","article-title":"Cracks Evaluation of Reinforced Concrete Structure: A Review","volume":"1783","author":"Zaki","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104275","DOI":"10.1016\/j.autcon.2022.104275","article-title":"Automatic concrete crack segmentation model based on transformer","volume":"139","author":"Wang","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105225","DOI":"10.1016\/j.engappai.2022.105225","article-title":"Automated bridge surface crack detection and segmentation using computer vision-based deep learning model","volume":"115","author":"Zhang","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9940881","DOI":"10.1155\/2023\/9940881","article-title":"Enhanced Precision in Dam Crack Width Measurement: Leveraging Advanced Lightweight Network Identification for Pixel-Level Accuracy","volume":"2023","author":"Wu","year":"2023","journal-title":"Int. J. Intell. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"123092","DOI":"10.1016\/j.conbuildmat.2021.123092","article-title":"Internet of Things (IoT) for masonry structural health monitoring (SHM): Overview and examples of innovative systems","volume":"290","author":"Scuro","year":"2021","journal-title":"Constr. Build. Mater."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Galdelli, A., D\u2019Imperio, M., Marchello, G., Mancini, A., Scaccia, M., Sasso, M., Frontoni, E., and Cannella, F. (2022). A Novel Remote Visual Inspection System for Bridge Predictive Maintenance. Remote Sens., 14.","DOI":"10.3390\/rs14092248"},{"key":"ref_26","first-page":"403","article-title":"Strain Prediction of a Bridge Deploying Autoregressive Models with ARIMA and Machine Learning Algorithms","volume":"1826","author":"Psathas","year":"2023","journal-title":"Commun. Comput. Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1007\/s42107-023-00578-5","article-title":"Wireless sensor networks for bridge structural health monitoring: A novel approach","volume":"24","author":"Singh","year":"2023","journal-title":"Asian J. Civ. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"109621","DOI":"10.1016\/j.ymssp.2022.109621","article-title":"Structural Health Monitoring for impact localisation via machine learning","volume":"183","author":"Dipietrangelo","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1080\/00031305.2017.1380080","article-title":"Forecasting at Scale","volume":"72","author":"Taylor","year":"2018","journal-title":"Am. Stat."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/34.659930","article-title":"An unbiased detector of curvilinear structures","volume":"20","author":"Steger","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","unstructured":"(2024, January 03). ReCITY: Supporting Community Resilience. Available online: https:\/\/www.eng.it\/en\/case-studies\/recity-supportare-la-resilienza-di-comunita."},{"key":"ref_32","unstructured":"(2023, March 03). Home|Endurcrete. Available online: http:\/\/www.endurcrete.eu\/."},{"key":"ref_33","unstructured":"(2023, March 03). Eco-Friendly and Self-Sensing Mortar|Knowledgeshare. Available online: https:\/\/www.knowledge-share.eu\/en\/patent\/eco-friendly-and-self-sensing-mortar\/."},{"key":"ref_34","unstructured":"(2022, May 17). FIWARE\u2014Open APIs for Open Minds. Available online: https:\/\/www.fiware.org\/."},{"key":"ref_35","first-page":"V007T07A003","article-title":"A feature encoding approach and a cloud computing architecture to map fishing activities","volume":"7","author":"Galdelli","year":"2021","journal-title":"Proc. ASME Des. Eng. Tech. Conf."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Giulietti, N., Chiariotti, P., and Revel, G.M. (2023). Automated Measurement of Geometric Features in Curvilinear Structures Exploiting Steger\u2019s Algorithm. Sensors, 23.","DOI":"10.3390\/s23084023"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/572\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:48:16Z","timestamp":1760104096000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/572"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,16]]},"references-count":36,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24020572"],"URL":"https:\/\/doi.org\/10.3390\/s24020572","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,16]]}}}