{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T04:10:36Z","timestamp":1748059836200,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819609932","type":"print"},{"value":"9789819609949","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-0994-9_22","type":"book-chapter","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T13:22:55Z","timestamp":1748006575000},"page":"235-244","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Machine Learning-Based Approach for\u00a0Evaluating Concrete Mix Design Properties"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2322-9743","authenticated-orcid":false,"given":"Costanza","family":"Anerdi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8721-8365","authenticated-orcid":false,"given":"Jonathan","family":"Melchiorre","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3640-8561","authenticated-orcid":false,"given":"Vincenzo","family":"Randazzo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8472-2956","authenticated-orcid":false,"given":"Giuseppe C.","family":"Marano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,24]]},"reference":[{"key":"22_CR1","unstructured":"Fetting, C.: The European green deal. ESDN Report (2020)"},{"key":"22_CR2","unstructured":"Cement and Concrete: The Environmental Impact (2020). https:\/\/psci.princeton.edu\/tips\/2020\/11\/3\/cement-and-concrete-the-environmental-impact"},{"key":"22_CR3","unstructured":"IEA - International Energy Agency: Cement (2022)"},{"key":"22_CR4","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.conbuildmat.2013.06.058","volume":"48","author":"A Fiore","year":"2013","unstructured":"Fiore, A., Marano, G.C., Monaco, P., Morbi, A.: Preliminary experimental study on the effects of surface-applied photocatalytic products on the durability of reinforced concrete. Constr. Build. Mater. 48, 137\u2013143 (2013)","journal-title":"Constr. Build. Mater."},{"key":"22_CR5","unstructured":"Sgobba, S., Marano, G.C., Borsa, M., Molfetta, M.: Use of rubber particles from recycled tires as concrete aggregate for engineering applications. In: 2nd International Conference on Sustainable Construction Materials and Technologies (2010)"},{"issue":"7","key":"22_CR6","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1038\/nmat4930","volume":"16","author":"PJ Monteiro","year":"2017","unstructured":"Monteiro, P.J., Miller, S.A., Horvath, A.: Towards sustainable concrete. Nat. Mater. 16(7), 698\u2013699 (2017)","journal-title":"Nat. Mater."},{"key":"22_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.cemconres.2018.05.002","volume":"112","author":"H Van Damme","year":"2018","unstructured":"Van Damme, H.: Concrete material science: past, present, and future innovations. Cement and Concret. Res. 112, 5\u201324 (2018)","journal-title":"Cement and Concret. Res."},{"key":"22_CR8","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/471\/3\/032006","volume":"471","author":"G Bertagnoli","year":"2018","unstructured":"Bertagnoli, G., Tondolo, F., Canonico, F., Anerdi, C., Buzzi, L., Giuseppe, M.: Calcium sulfoaluminate based concrete - mechanical characterization. IOP Conf. Ser.: Mater. Sci. Eng. 471, 032006 (2018)","journal-title":"IOP Conf. Ser.: Mater. Sci. Eng."},{"key":"22_CR9","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1016\/j.conbuildmat.2015.07.095","volume":"98","author":"S Sgobba","year":"2015","unstructured":"Sgobba, S., Borsa, M., Molfetta, M., Marano, G.C.: Mechanical performance and medium-term degradation of rubberised concrete. Constr. Build. Mater. 98, 820\u2013831 (2015)","journal-title":"Constr. Build. Mater."},{"key":"22_CR10","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.cemconres.2018.04.007","volume":"109","author":"M DeRousseau","year":"2018","unstructured":"DeRousseau, M., Kasprzyk, J., Srubar Iii, W.: Computational design optimization of concrete mixtures: a review. Cement and Concret. Res. 109, 42\u201353 (2018)","journal-title":"Cement and Concret. Res."},{"key":"22_CR11","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/s00158-013-0979-5","volume":"49","author":"G Quaranta","year":"2014","unstructured":"Quaranta, G., Fiore, A., Marano, G.C.: Optimum design of prestressed concrete beams using constrained differential evolution algorithm. Struct. Multidiscip. Optim. 49, 441\u2013453 (2014)","journal-title":"Struct. Multidiscip. Optim."},{"issue":"5","key":"22_CR12","doi-asserted-by":"publisher","first-page":"1689","DOI":"10.1007\/s12559-021-09910-0","volume":"14","author":"A Paviglianiti","year":"2022","unstructured":"Paviglianiti, A., Randazzo, V., Villata, S., Cirrincione, G., Pasero, E.: A comparison of deep learning techniques for arterial blood pressure prediction. Cognit. Comput. 14(5), 1689\u20131710 (2022)","journal-title":"Cognit. Comput."},{"issue":"6","key":"22_CR13","doi-asserted-by":"publisher","first-page":"1875","DOI":"10.1007\/s40620-021-01046-6","volume":"34","author":"F Alfieri","year":"2021","unstructured":"Alfieri, F., Ancona, A., Tripepi, G., Crosetto, D., Randazzo, V., Paviglianiti, A., Pasero, E., Vecchi, L., Cauda, V., Fagugli, R.M.: A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients. J. Nephrol. 34(6), 1875\u20131886 (2021)","journal-title":"J. Nephrol."},{"issue":"8","key":"22_CR14","doi-asserted-by":"publisher","first-page":"2047","DOI":"10.1007\/s40620-022-01335-8","volume":"35","author":"F Alfieri","year":"2022","unstructured":"Alfieri, F., Ancona, A., Tripepi, G., Randazzo, V., Paviglianiti, A., Pasero, E., Vecchi, L., Politi, C., Cauda, V., Fagugli, R.M.: External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients. J. Nephrol. 35(8), 2047\u20132056 (2022)","journal-title":"J. Nephrol."},{"issue":"8","key":"22_CR15","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.3390\/ma12081256","volume":"12","author":"P Ziolkowski","year":"2019","unstructured":"Ziolkowski, P., Niedostatkiewicz, M.: Machine learning techniques in concrete mix design. Materials 12(8), 1256 (2019)","journal-title":"Materials"},{"key":"22_CR16","unstructured":"British-Standard-Institution: Part 2: Testing fresh concrete - slump test. EN12350 (2019)"},{"issue":"4","key":"22_CR17","doi-asserted-by":"publisher","first-page":"4341","DOI":"10.1002\/suco.202201139","volume":"24","author":"SL Matthews","year":"2023","unstructured":"Matthews, S.L.: fib model code 2020: enabling advances for new and existing concrete structures. Struct. Concret. 24(4), 4341\u20134351 (2023)","journal-title":"Struct. Concret."},{"issue":"8","key":"22_CR18","doi-asserted-by":"publisher","first-page":"2035","DOI":"10.3390\/ijms20082035","volume":"20","author":"I Roberti","year":"2019","unstructured":"Roberti, I., Lovino, M., Di Cataldo, S., Ficarra, E., Urgese, G.: Exploiting gene expression profiles for the automated prediction of connectivity between brain regions. Int. J. Mol. Sci. 20(8), 2035 (2019)","journal-title":"Int. J. Mol. Sci."},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Ferretti, J., Randazzo, V., Cirrincione, G., Pasero, E.: 1-d convolutional neural network for ECG arrhythmia classification. In: Progresses in Artificial Intelligence and Neural Systems pp. 269\u2013279 (2021)","DOI":"10.1007\/978-981-15-5093-5_25"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Paviglianiti, A., Randazzo, V., Pasero, E., Vallan, A.: Noninvasive arterial blood pressure estimation using abpnet and vital-ecg. In: 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). pp. 1\u20135. IEEE (2020)","DOI":"10.1109\/I2MTC43012.2020.9129361"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Randazzo, V., Cirrincione, G., Pasero, E.: Shallow neural network for biometrics from the ecg-watch. In: Intelligent Computing Theories and Application: 16th International Conference, ICIC 2020, Bari, Italy, October 2\u20135, 2020, Proceedings, Part I 16. pp. 259\u2013269. Springer (2020)","DOI":"10.1007\/978-3-030-60799-9_22"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Cirrincione, G., Randazzo, V., Pasero, E.: A neural based comparative analysis for feature extraction from ECG signals. In: Neural Approaches to Dynamics of Signal Exchanges, pp. 247\u2013256 (2020)","DOI":"10.1007\/978-981-13-8950-4_23"},{"key":"22_CR23","doi-asserted-by":"publisher","first-page":"2201","DOI":"10.1109\/ACCESS.2020.3047202","volume":"9","author":"RR Kumar","year":"2020","unstructured":"Kumar, R.R., Randazzo, V., Cirrincione, G., Cirrincione, M., Pasero, E., Tortella, A., Andriollo, M.: Induction machine stator fault tracking using the growing curvilinear component analysis. IEEE Access 9, 2201\u20132212 (2020)","journal-title":"IEEE Access"},{"key":"22_CR24","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.neucom.2021.11.094","volume":"488","author":"M Lovino","year":"2022","unstructured":"Lovino, M., Randazzo, V., Ciravegna, G., Barbiero, P., Ficarra, E., Cirrincione, G.: A survey on data integration for multi-omics sample clustering. Neurocomputing 488, 494\u2013508 (2022)","journal-title":"Neurocomputing"},{"key":"22_CR25","doi-asserted-by":"crossref","unstructured":"Randazzo, V., Cirrincione, G., Paviglianiti, A., Pasero, E., Morabito, F.C.: Neural feature extraction for the analysis of parkinsonian patient handwriting. In: Progresses in Artificial Intelligence and Neural Systems, pp. 243\u2013253 (2021)","DOI":"10.1007\/978-981-15-5093-5_23"}],"container-title":["Smart Innovation, Systems and Technologies","Advanced Neural Artificial Intelligence: Theories and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0994-9_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T13:23:03Z","timestamp":1748006583000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0994-9_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819609932","9789819609949"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0994-9_22","relation":{},"ISSN":["2190-3018","2190-3026"],"issn-type":[{"value":"2190-3018","type":"print"},{"value":"2190-3026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"24 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}