{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:37:12Z","timestamp":1768822632410,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Programa Operacional Competitividade e Internacionalizacao","award":["POCI-01-0145-FEDER-032223"],"award-info":[{"award-number":["POCI-01-0145-FEDER-032223"]}]},{"name":"Programa Operacional Competitividade e Internacionalizacao","award":["PTDC\/ECI-EGC\/32223\/2017"],"award-info":[{"award-number":["PTDC\/ECI-EGC\/32223\/2017"]}]},{"name":"Programa Operacional Competitividade e Internacionalizacao","award":["UIDB\/04625\/2020"],"award-info":[{"award-number":["UIDB\/04625\/2020"]}]},{"name":"Programa Operacional Competitividade e Internacionalizacao","award":["UI\/BD\/151151\/2021"],"award-info":[{"award-number":["UI\/BD\/151151\/2021"]}]},{"name":"Programa Operacional Competitividade e Internacionalizacao","award":["UIDB\/04028\/2020"],"award-info":[{"award-number":["UIDB\/04028\/2020"]}]},{"name":"Foundation for Science and Technology","award":["POCI-01-0145-FEDER-032223"],"award-info":[{"award-number":["POCI-01-0145-FEDER-032223"]}]},{"name":"Foundation for Science and Technology","award":["PTDC\/ECI-EGC\/32223\/2017"],"award-info":[{"award-number":["PTDC\/ECI-EGC\/32223\/2017"]}]},{"name":"Foundation for Science and Technology","award":["UIDB\/04625\/2020"],"award-info":[{"award-number":["UIDB\/04625\/2020"]}]},{"name":"Foundation for Science and Technology","award":["UI\/BD\/151151\/2021"],"award-info":[{"award-number":["UI\/BD\/151151\/2021"]}]},{"name":"Foundation for Science and Technology","award":["UIDB\/04028\/2020"],"award-info":[{"award-number":["UIDB\/04028\/2020"]}]},{"name":"CERIS","award":["POCI-01-0145-FEDER-032223"],"award-info":[{"award-number":["POCI-01-0145-FEDER-032223"]}]},{"name":"CERIS","award":["PTDC\/ECI-EGC\/32223\/2017"],"award-info":[{"award-number":["PTDC\/ECI-EGC\/32223\/2017"]}]},{"name":"CERIS","award":["UIDB\/04625\/2020"],"award-info":[{"award-number":["UIDB\/04625\/2020"]}]},{"name":"CERIS","award":["UI\/BD\/151151\/2021"],"award-info":[{"award-number":["UI\/BD\/151151\/2021"]}]},{"name":"CERIS","award":["UIDB\/04028\/2020"],"award-info":[{"award-number":["UIDB\/04028\/2020"]}]},{"name":"Science and Technology","award":["POCI-01-0145-FEDER-032223"],"award-info":[{"award-number":["POCI-01-0145-FEDER-032223"]}]},{"name":"Science and Technology","award":["PTDC\/ECI-EGC\/32223\/2017"],"award-info":[{"award-number":["PTDC\/ECI-EGC\/32223\/2017"]}]},{"name":"Science and Technology","award":["UIDB\/04625\/2020"],"award-info":[{"award-number":["UIDB\/04625\/2020"]}]},{"name":"Science and Technology","award":["UI\/BD\/151151\/2021"],"award-info":[{"award-number":["UI\/BD\/151151\/2021"]}]},{"name":"Science and Technology","award":["UIDB\/04028\/2020"],"award-info":[{"award-number":["UIDB\/04028\/2020"]}]},{"name":"CERENA","award":["POCI-01-0145-FEDER-032223"],"award-info":[{"award-number":["POCI-01-0145-FEDER-032223"]}]},{"name":"CERENA","award":["PTDC\/ECI-EGC\/32223\/2017"],"award-info":[{"award-number":["PTDC\/ECI-EGC\/32223\/2017"]}]},{"name":"CERENA","award":["UIDB\/04625\/2020"],"award-info":[{"award-number":["UIDB\/04625\/2020"]}]},{"name":"CERENA","award":["UI\/BD\/151151\/2021"],"award-info":[{"award-number":["UI\/BD\/151151\/2021"]}]},{"name":"CERENA","award":["UIDB\/04028\/2020"],"award-info":[{"award-number":["UIDB\/04028\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>This study aims to evaluate the potential of machine learning algorithms (Random Forest and Support Vector Machine) in predicting the open porosity of a general-use industrial mortar applied to different substrates based on the characteristics of both the mortar and substrates. This study\u2019s novelty lies in predicting the mortar\u2019s porosity considering the substrate\u2019s influence on which this mortar is applied. For this purpose, an experimental database comprising 1592 datapoints of industrial mortar applied to five different substrates (hollowed ceramic brick, solid ceramic brick, concrete block, concrete slab, and lightweight concrete block) was generated using an experimental program. The samples were characterized by bulk density, open porosity, capillary water absorption coefficient, drying index, and compressive strength. This database was then used to train and test the machine learning algorithms to predict the open porosity of the mortar. The results indicate that it is possible to predict the open porosity of mortar with good prediction accuracy, and that both Random Forest (RF) and Support Vector Machine (SVM) algorithms (RF = 0.880; SVM = 0.896) are suitable for this task. Regarding the main characteristics that influence the open porosity of the mortar, the bulk density and open porosity of the substrate are significant factors. Furthermore, this study employs a straightforward methodology with a machine learning no-code platform, enhancing the replicability of its findings for future research and practical implementations.<\/jats:p>","DOI":"10.3390\/app142310780","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T04:46:11Z","timestamp":1732164371000},"page":"10780","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9059-1232","authenticated-orcid":false,"given":"Rafael","family":"Travincas","sequence":"first","affiliation":[{"name":"Department of Materials Science, Military Institute of Engineering-IME, Pra\u00e7a General Tiburcio, 80, Urca, Rio de Janeiro 22290-270, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9451-2266","authenticated-orcid":false,"given":"Maria Paula","family":"Mendes","sequence":"additional","affiliation":[{"name":"CERENA, Centre of Natural Resources and Environment, Instituto Superior T\u00e9cnico, University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon, Portugal"}]},{"given":"Isabel","family":"Torres","sequence":"additional","affiliation":[{"name":"CERIS, Department of Civil Engineering, University of Coimbra, Rua Lu\u00eds Reis Santos\u2014P\u00f3lo II, 3030-788 Coimbra, Portugal"},{"name":"Itecons\u2014Institute for Research and Technological Development in Construction, Energy, Environment and Sustainability, Rua Pedro Hispano, s\/n, 3030-289 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4038-6748","authenticated-orcid":false,"given":"In\u00eas","family":"Flores-Colen","sequence":"additional","affiliation":[{"name":"CERIS, Department of Civil Engineering, Architecture and Environment, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"ref_1","unstructured":"(2024, July 03). Buildings and Construction\u2014European Commission. Available online: https:\/\/single-market-economy.ec.europa.eu\/industry\/sustainability\/buildings-and-construction_en."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13089","DOI":"10.1007\/s00521-021-06004-8","article-title":"Prediction of cement-based mortars compressive strength using machine learning techniques","volume":"33","author":"Asteris","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"118744","DOI":"10.1016\/j.conbuildmat.2020.118744","article-title":"Use of machine learning based technique to X-ray microtomographic images of concrete for phase segmentation at meso-scale","volume":"249","author":"Saha","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Koumoulos, E.P., Paraskevoudis, K., and Charitidis, C.A. (2019). Constituents phase reconstruction through applied machine learning in nanoindentation mapping data of mortar surface. J. Compos. Sci., 3.","DOI":"10.3390\/jcs3030063"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.culher.2020.09.005","article-title":"Machine learning techniques for structural health monitoring of heritage buildings: A state-of-the- art review and case studies","volume":"47","author":"Mishra","year":"2020","journal-title":"J. Cult. Herit."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.istruc.2022.02.003","article-title":"Machine learning for structural engineering: A state-of-the-art review","volume":"38","author":"Thai","year":"2022","journal-title":"Structures"},{"key":"ref_7","first-page":"125","article-title":"Estimating compressive strength of modern concrete mixtures using computational intelligence: A systematic review","volume":"3","author":"Nunez","year":"2021","journal-title":"Constr. Build. Mater."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"100045","DOI":"10.1016\/j.dibe.2021.100045","article-title":"Machine learning in construction: From shallow to deep learning","volume":"6","author":"Xu","year":"2021","journal-title":"Dev. Built Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"119889","DOI":"10.1016\/j.conbuildmat.2020.119889","article-title":"Machine learning prediction of mechanical properties of concrete: Critical review","volume":"260","author":"Chaabene","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101816","DOI":"10.1016\/j.jobe.2020.101816","article-title":"Machine learning applications for building structural design and performance assessment: State-of-the-art review","volume":"33","author":"Sun","year":"2021","journal-title":"J. Build. Eng."},{"key":"ref_11","unstructured":"Izbicki, R. (2020). Machine Learning: A Statistical Approach, UICLAP. (In Portuguese)."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1139\/er-2020-0019","article-title":"A review of machine learning applications in wildfire. Science and Management","volume":"28","author":"Jain","year":"2020","journal-title":"Environ. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1007\/s12046-017-0652-6","article-title":"Estimation of algal colonization growth on mortar surface using a hybridization of machine learning and metaheuristic optimization","volume":"42","author":"Tran","year":"2017","journal-title":"S\u0101dhan\u0101"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1080\/14686996.2020.1814117","article-title":"Addressing the need for standardization of test methods for self-healing concrete: An inter-laboratory study on concrete with macrocapsules","volume":"21","author":"Anglani","year":"2020","journal-title":"Sci. Technol. Adv. Mater."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1038\/s43246-020-0012-7","article-title":"Designing bioinspired brick-and-mortar composites using machine learning and statistical learning","volume":"1","author":"Morsali","year":"2020","journal-title":"Commun. Mater."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"121584","DOI":"10.1016\/j.conbuildmat.2020.121584","article-title":"Compressive strength prediction models for cementitious composites with fly ash using machine learning techniques","volume":"271","author":"Sevim","year":"2021","journal-title":"Constr. Build. Mater."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1111\/jace.16706","article-title":"Machine learning can predict setting behavior and strength evolution of hydrating cement systems","volume":"103","author":"Oey","year":"2020","journal-title":"J. Am. Ceram. Soc."},{"key":"ref_18","unstructured":"Carasek, H. (1996). Mechanisms and Factors Involved in the Adhesion of Portland Cement-Based Mortars Applied to Porous Substrates. [Ph.D. Thesis, Escola Polit\u00e9cnica, Universidade de S\u00e3o Paulo]. (In Portuguese)."},{"key":"ref_19","first-page":"47","article-title":"Influence of base characteristics on tensile strength and pore distribution of a mortar","volume":"3","author":"Kazmierczak","year":"2007","journal-title":"Estud. Tecnol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"04018254","DOI":"10.1061\/(ASCE)MT.1943-5533.0002339","article-title":"Influence of substrate characteristics on behavior of applied mortar","volume":"30","author":"Torres","year":"2018","journal-title":"J. Mater. Civ. Eng."},{"key":"ref_21","unstructured":"Matias, G., Torres, I., Bellei, P., Flores-Colen, I., Silveira, D., and Travincas, R. (2021, January 12\u201315). Influence of the substrate in the porosimetry of hardened mortars. Proceedings of the International Conference Construction, Energy, Environment e Sustainability, Coimbra, Portugal."},{"key":"ref_22","unstructured":"Bellei, P., Torres, I., Flores-Colen, I., Travincas, R., and Silveira, D. (2021, January 12\u201315). Study of cement mortars with different particle size sands after application to ceramic brick substrate. Proceedings of the International Conference Construction, Energy, Environment e Sustainability, Coimbra, Portugal."},{"key":"ref_23","unstructured":"Carvalho, A.N. (2002). Evaluation of the Adhesion of Mortared Coatings: A Contribution to the Identification of the Mechanical Adhesion System. [Doctoral Thesis, Graduate Course in Metallurgical and Mining Engineering, Federal University of Minas Ge Rais]. (In Portuguese)."},{"key":"ref_24","first-page":"2349","article-title":"Orange: Data Mining Toolbox in Python","volume":"14","author":"Demsar","year":"2013","journal-title":"J. Mach. Learn. Res."},{"key":"ref_25","unstructured":"(2010). Specification for Mortar for Masonry. Part 1: Rendering and Plastering Mortar (Standard No. EN 998-1)."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Travincas, R., Bellei, P., Torres, I., Flores-Colen, I., Matias, G., and Silveira, D. (2022). The use of fibreglass mesh in the study of applied coating mortars. Coatings, 12.","DOI":"10.3390\/coatings12081091"},{"key":"ref_27","unstructured":"(2008). Natural Stone Test Methods\u2014Determination of Real Density and Apparent Density and of Total and Open Porosity (Standard No. EN 1936:2008)."},{"key":"ref_28","unstructured":"(2002). Hygrothermal Performance of Building Materials and Products\u2014Determination of Water Absorption Coefficient by Partial Immersion (Standard No. ISO 15148)."},{"key":"ref_29","unstructured":"(2013). Conservation of Cultural Heritage: Test Methods: Determination of Drying Properties (Standard No. EN 16322)."},{"key":"ref_30","unstructured":"(1980). RILEM Test No. II.5\u2014Evaporation Curve. RILEM 25-PEM, Provisional recommendations. These recommand\u00e9s pour mesurer l\u2019alt\u00e9ration des pierres et \u00e9valuer l\u2019efficacit\u00e9 des m\u00e9thodes de traitement. Mat\u00e9r. Constr., 75, 205\u2013207."},{"key":"ref_31","unstructured":"(2019). Methods of Test for Mortar for Masonry\u2014Part 11: Determination of Flexural and Compressive Strength of Hardened Mortar (Standard No. EN 1015-11)."},{"key":"ref_32","unstructured":"Gouveia, J.P.M. (2021). Analysis of the Behavior of Mortars after Application on Substrates. [Master\u2019s Thesis, University of Coimbra]. (In Portuguese)."},{"key":"ref_33","unstructured":"Soares, K.M.P. (2021). The Influence of Traditional Supports on the Behavior of Lime Mortars. [Master\u2019s Thesis, Universidade de Coimbra]. (In portuguese)."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Travincas, R., Silveira, D., Bellei, P., Gouveia, J., Matias, G., Torres, I., and Flores-Colen, I. (2024). Performance and Accelerated Ageing of an Industrial Hydraulic Lime Mortar Applied on Different Substrates. Coatings, 14.","DOI":"10.3390\/coatings14070819"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"106784","DOI":"10.1016\/j.jobe.2023.106784","article-title":"The influence of the substrate type on the performance of an industrial cement mortar for general use","volume":"73","author":"Travincas","year":"2023","journal-title":"J. Build. Eng."},{"key":"ref_36","first-page":"38","article-title":"Peirce\u2019s criterion for the elimination of suspect experimental data","volume":"20","author":"Ross","year":"2003","journal-title":"J. Eng. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hastie, T., Friedman, J., and Tibshirani, R. (2001). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ray, S. (2019, January 14\u201316). A quick review of machine learning algorithms. Proceedings of the 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India.","DOI":"10.1109\/COMITCon.2019.8862451"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1007\/s42452-019-0590-5","article-title":"Support vector machine for determining the compressive strength of brick-mortar masonry using NDT data fusion (case study: Kharagpur, India)","volume":"1","author":"Mishra","year":"2019","journal-title":"SN Appl. Sci."},{"key":"ref_42","first-page":"1411","article-title":"Analyzing the risk factors and predicting the learning ability of students during pandemic and comparing machine learning algorithms using Orange tool","volume":"32","author":"Gladshiya","year":"2021","journal-title":"Turk. J. Physiother. Rehabil."}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/14\/23\/10780\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:36:35Z","timestamp":1760114195000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/14\/23\/10780"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,21]]},"references-count":42,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["app142310780"],"URL":"https:\/\/doi.org\/10.3390\/app142310780","relation":{},"ISSN":["2076-3417"],"issn-type":[{"value":"2076-3417","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,21]]}}}