Innovations in health institutions of the federation of Bosnia and Herzegovina (FBH), innovation management, innovation management models and support method

Bušatlić, Senad; Mamuti, Agim; Riđić, Ognjen; Riđić, Goran

doi:10.5937/ekoizazov1712112b

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The share of MgO in the slag is increased compared to the allowed 5%. A higher quantity of MgO can affect in addition to volume instability, the appearance of cracks [56] in mixtures with cement, which demands special attention in experimental work. The measured slag pH value is 8.…”

Section: Physical-chemical Characteristics Of Slagmentioning

confidence: 99%

Bio-Induced Healing of Cement Mortars in Demineralized and Danube Water: CNN Model for Image Classification

Nešković¹,

Jovanović

Markov

et al. 2023

Buildings

View full text Add to dashboard Cite

Reducing the costs of repairing concrete structures damaged due to the appearance of cracks and reducing the number of people involved in the process of their repair is the subject of a multitude of experimental studies. Special emphasis should be placed on research involving industrial by-products, the disposal of which has a negative environmental impact, as is the case in the research presented in this paper. The basic idea was to prepare a mortar with added granulated blast furnace slag from Smederevo Steel Mill and then treat artificially produced cracks with a Sporosarcina pasteurii DSM 33 suspension under the conditions of both sterile demineralized water and water from the Danube river in order to simulate natural conditions. The results show a bio-stimulated healing efficiency of 32.02% in sterile demineralized water and 42.74% in Danube river water already after 14 days. The SEM images clearly show calcium carbonate crystals as the main compound that has started to fill the crack, and the crystals are much more developed under the Danube river water conditions. As a special type of research, microscopic images of cracks were classified into those with and without the presence of bacterial culture. By applying convolutional neural networks (ResNet 50), the classification success rate was 91.55%.

show abstract

Section: Physical-chemical Characteristics Of Slagmentioning

confidence: 99%