Concrete Subsurface Crack Detection Using Thermal Imaging in a Deep Neural Network

The article discusses how impact actions, such as conflict and warfare, can negatively impact the structural integrity of concrete structures and how detecting hidden defects in concrete structures is difficult without expert knowledge. The paper presents a new technique that combines thermal imaging and artificial intelligence to detect hidden defects in concrete structures. The authors trained an AI model on simulated data and achieved a validation accuracy of 99.93%. They then conducted a laboratory experiment to create a dataset of concrete blocks with and without subsurface cracks and trained a new model, which achieved a validation accuracy of 100%. The article concludes that AI can detect hidden defects and subsurface cracks in concrete structures by classifying thermal images of concrete surfaces

Impact of the position of the window in the reveal of a cavity wall on the heat loss and the internal surface temperature of the head of an opening with a steel lintel

© 2017 Elsevier B.V. The interface between the head of the window and the wall represents one of the largest thermal bridges of a building and one of the areas with the highest risk of surface condensation. This paper confirmed the importance, and investigated the impact, of the location of the window in the reveal of a cavity wall on the Ψlintel and surface temperature of the area. Additionally, it studied the reliability and accuracy of assessing this thermal bridge using an adiabatic surface instead the actual window. Two possible construction details that meet PARTL 2013 were modelled and assessed with HEAT2D software, following two different methods: the standard and commonly used (adiabatic surface) method and the detailed one (including the actual window). The outputs revealed that the adiabatic surface prevents the software to account the heat transfer that in reality occurs between the window frame and the highly conductive steel lintel. Therefore, the current simplified method could underestimates the heat losses up to 33% and the surface temperature by over 4°C for certain locations. Additionally, it locates the optimal area for the frame between overlapping 70mm the cavity to align with the insulation layer of the cavity. Finally, it concluded that under current trends of extremely low Ψlintel the adiabatic surface has a greater impact than before, producing less accurate outputs, enough to start to think on the necessity of including the actual window during the assessment of the thermal performance of top hat lintels without base plate in low/zero carbon projects

Consistency of fly ash and Metakaolin concrete

As high‐performance Portland cement (PC), fly ash (FA) and metakaolin (MK) concrete have been developed in wide applications, it has growing interest in optimizing and predicting consistency of fresh PC‐FA‐MK concrete for efficient and practical design and construction. This paper presents statistical models for predicting the consistency of concrete incorporating PC, FA and MK from the experimental results of standard consistency tests. They reflect the effect of variations of pozzolanic replacement materials including FA and MK at graduated replacement levels of up to 40% and 15%, respectively. The predictions produced are compared with the experimental results of consistency of concrete blends. Models show that they can be used to predict the consistency parameters including slump, compacting factor and Vebe time with a good degree of accuracy in a wide range of FA‐MK blends. Design guidelines for evaluating consistency parameters are tentatively recommended along with their confidence intervals for prediction limits at 5% significance levels. Santrauka Straipsnyje aprašyti cementbetonio mišinio su lakiaisiais pelenais ir metakaolinu konsistencijos (slankumo, sutankinamumo, Vebe rodiklio) tyrimai. Parenkant betono mišinių sudėtis buvo naudojami lakieji pelenai, kurie pakeisdavo iki 40 % portlandcemenčio ir metakaolinas, kurio buvo dedama iki 15 % cemento masės. Atitinkamai buvo keičiami ir portlandcemenčio kiekiai. Remiantis tyrimų rezultatais, pasiūlyti statistiniai modeliai įvairių sudėčių betono mišinio konsistencijai prognozuoti. Palyginus prognozuojamus ir eksperimentinių tyrimų betono mišinio konsistencijos rodiklius nustatyta, kad jie labai gerai koreliuoja. Todėl pasiūlytus statistinius prognozavimo modelius galima taikyti betonų technologijos praktikoje. First Published Online: 14 Oct 2010 Reikšminiai žodžiai: betono mišinio konsistencija, regresijos modelis, prognozavimas, portlandcementis, lakieji pelenai, metakaolinas