[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121387-en":3,"doc-seo-121387-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121387,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning models applied to moisture assessment in building materials - Article","Moisture-related defects undermine the long-term durability of buildings and must be prevented through reliable diagnosis. Non-destructive measurements based on surface temperature offer strong potential for moisture analysis. This work introduces machine learning models that predict moisture content from surface temperature, using both material properties and environmental conditions as inputs. Neural networks achieve R² above 0.96 with errors below 5%. Performance is excellent for brick and granite, while limestone and concrete enable moisture identification mainly in the near-surface zone.","Construction and Building Materials 405 (2023) 133330  \nContents lists available at ScienceDirect  \nConstruction and Building Materials  \njournal [homepage: www.elsevier.com/locate/conbuildmat](homepage: www.elsevier.com/locate/conbuildmat)  \n| Machine learning models applied to moisture assessment in building materials\u003Cbr>Leticia C.M. Dafico a, *, Eva Barreiraa, Ricardo M.S.F. Almeida a, b, Romeu Vicente c\u003Cbr>a CONSTRUCT-LFC, University of Porto, Faculty of Engineering (FEUP), Civil Engineering Department, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal b Department of Civil Engineering, Polytechnic Institute of Viseu, Campus Polit´ecnico, 3504-510 Viseu, Portugal\u003Cbr>c RISCO, Risks and Sustainability in Construction, Department of Civil Engineering, University of Aveiro, Campus Universit´ario de Santiago, 3810-193 Aveiro, Portugal |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Moisture\u003Cbr>Quantitative diagnosis\u003Cbr>Machine learning models\u003Cbr>Hygrothermal behaviour |  | Moisture-related defects hinder long-term building durability and must be prevented. Non-destructive techniques that measure the surface temperature of building materials have good potential for moisture analysis. This article presents machine learning models to predict the moisture content of materials according to their surface temperature using as input the material and the environmental conditions. Results showed that neural network models present a coefficient of determination higher than 0.96 and an error of less than 5%. The application of the models demonstrated that in brick and granite the model presented excellent results, but in limestone and concrete it enabled the identification of moisture only in the near-surface zone. |\n\n1. Introduction  \nMoisture is one of the main causes of defects of external and internal building fabric, frequently leading to the degradation and progressive decay of the materials. The complexity of moisture phenomena makes it more challenging to interpret the results during building inspections [1]. However, proper diagnosis and prevention of moisture damage are of paramount importance to guarantee the durability of the building and its components, the comfort of the users, and the quality of the indoor environment [2] since high levels of moisture can lead to mould growth [3], reduced thermal resistance [1], salt crystallisation [4,5], and even reduced mechanical strength [6].  \nInfrared Thermography (IRT) is a non-destructive technique that does not require contact with the surface and allows the assessment of the thermal behaviour of building materials, enabling the identification of potential problems. Hence, it has a good potential for moisture analysis, making it possible the identification of moisture-related anomalies [1,7–10]. IRT can also be used for the detection of other irregularities, such as detachments of ceramic tiles [11] and plaster [12]; sub-pavement voids in roadways [13]; defects in insulating panels in facades [14]; energy loss [15]; cracks [16], and for the analysis of the thermal performance [17], so IRT results can be integrated to assess several problems. However, despite its potential to detect moisture, there is a gap in the literature regarding the identification of quantitative  \ncriteria for the interpretation of results due to the complexity and variability of the phenomena involved in the humidification and drying of materials [7,8,18]. Therefore, obtaining surface temperature criteria indicative of moisture is a significant challenge that must be addressed.  \nIn recent years, the construction industry is experiencing a technological revolution driven by digitalisation and automation [19]. This trend is also visible in several studies that have been conducted to integrate the results of building appraisal and inspection technologies with machine learning techniques [20], including the development of algorithms that make it possible to automate the i","cbCaisjFdKpvMYEQ","https://ap.wps.com/l/cbCaisjFdKpvMYEQ","pdf",6591619,1,17,"English","en",105,"# Introduction\n## Moisture as a cause of building defects\n## Infrared thermography for moisture analysis\n## Machine learning in building inspection and diagnosis\n## Research gap and study objective","[{\"question\":\"Why is moisture diagnosis critical for building durability?\",\"answer\":\"Moisture drives defects and progressive decay, reducing thermal resistance and mechanical strength and increasing risks such as mould growth and salt crystallisation. Accurate diagnosis and prevention are essential to protect building components, occupant comfort, and indoor air quality.\"},{\"question\":\"How does the study estimate moisture content?\",\"answer\":\"The models predict moisture content using surface temperature measured by infrared thermography, with inputs including material properties and environmental conditions. The approach links thermal responses to moisture levels.\"},{\"question\":\"What performance do the proposed machine learning models achieve?\",\"answer\":\"Neural network models reach a coefficient of determination higher than 0.96 and errors under 5%. Results are excellent for brick and granite, while limestone and concrete mainly allow moisture identification in the near-surface zone.\"}]","Machine learning models applied to moisture assessment in building materials - Article | PDF",1785735418,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-models-applied-to-moisture-assessment-in-building-materials-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-models-applied-to-moisture-assessment-in-building-materials-article/121387/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is moisture diagnosis critical for building durability?","Question",{"text":75,"@type":76},"Moisture drives defects and progressive decay, reducing thermal resistance and mechanical strength and increasing risks such as mould growth and salt crystallisation. Accurate diagnosis and prevention are essential to protect building components, occupant comfort, and indoor air quality.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study estimate moisture content?",{"text":80,"@type":76},"The models predict moisture content using surface temperature measured by infrared thermography, with inputs including material properties and environmental conditions. The approach links thermal responses to moisture levels.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance do the proposed machine learning models achieve?",{"text":84,"@type":76},"Neural network models reach a coefficient of determination higher than 0.96 and errors under 5%. Results are excellent for brick and granite, while limestone and concrete mainly allow moisture identification in the near-surface zone.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]