S
Sławomir Czarnecki
Researcher at Wrocław University of Technology
Publications - 22
Citations - 480
Sławomir Czarnecki is an academic researcher from Wrocław University of Technology. The author has contributed to research in topics: Layer (electronics) & Artificial neural network. The author has an hindex of 9, co-authored 22 publications receiving 203 citations.
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Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms
Hongwei Song,Ayaz Ahmad,Furqan Farooq,Krzysztof Ostrowski,Mariusz Maślak,Sławomir Czarnecki,Fahid Aslam +6 more
TL;DR: This study includes the collection of data from the experimental work and the application of ML techniques to predict the CS of concrete containing fly ash, and shows high accuracy towards the prediction of outcome as indicated by its high coefficient correlation (R2) value.
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Evaluation of the height 3D roughness parameters of concrete substrate and the adhesion to epoxy resin
TL;DR: In this article, a 3D scanner was used to evaluate and describe the morphology of selected concrete substrate surfaces, and a special focus was placed on the advantages and disadvantages of the latter over the other scanners.
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Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material.
TL;DR: In this paper, Gene Expression Programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected.
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A Comparative Study for the Prediction of the Compressive Strength of Self-Compacting Concrete Modified with Fly Ash
Furqan Farooq,Sławomir Czarnecki,Paweł Niewiadomski,Fahid Aslam,Hisham Alabduljabbar,Krzysztof Ostrowski,Klaudia Śliwa-Wieczorek,Tomasz Nowobilski,Seweryn Malazdrewicz +8 more
TL;DR: In this article, Artificial Neural Network (ANN) support vector machine (SVM) and gene expression programming (GEP) consisting of 300 datasets have been utilized in the model to foresee the mechanical property of SCC.
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An intelligent model for the prediction of the compressive strength of cementitious composites with ground granulated blast furnace slag based on ultrasonic pulse velocity measurements
TL;DR: In this paper, the compressive strength of green cementitious composites containing ground granulated blast furnace slag (GGBFS) was predicted using self-organizing feature map (SOFM) and ANN.