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Marko Grebović

Bio: Marko Grebović is an academic researcher from University of Montenegro. The author has contributed to research in topics: Computer science & Ultimate tensile strength. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Papers
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Journal ArticleDOI
TL;DR: In this paper, the correlation between tensile strength of concrete and compressive strength, based on experimental results, is shown and regression analysis that use, is based on the results of the experimental researches of compressive strengths and splitting tensile strengths, at specimens of high strength concrete.
Abstract: Use of high strength concrete require reliable predictions of correlations of compressive strength with tensile strength or splitting strength. Analysis of correlation between tensile strength of concrete and compressive strength, based on experimental results, is shown in this paper. It is proposed new simple correlation. Regression analysis that use, is based on the results of the experimental researches of compressive strength and splitting tensile strength, at specimens of high strength concrete. Comparative analysis of test results, gained for high strength concrete and normal strength concrete is performed. Analysis comprises the results of the experimental research of deep beams subjected to shear. Stresses measured on surfaces of high strength concrete beams subjected to shear and level of stresses that induce inclined cracks in concrete are applied. Relationship between concrete compressive strength and shear cracking stresses is studied, too. Experimental research was done at pairs of the beams made of concrete with high compressive strength and normal compressive strength. Estimation of validation of relations prescribed by the design codes for high strength concrete has performed.

2 citations

Proceedings ArticleDOI
22 Nov 2022
TL;DR: In this paper , several artificial neural networks and traditional statistical methods are evaluated and analyzed through accuracy measures for prediction purposes in various fields of applications, based on gained results, couple of techniques for improving Artificial Neural Networks are proposed to get better accuracy results than statistical predictive methods.
Abstract: Traditional statistical models as tools for summarizing patterns and regularities in observed data can be used for making predictions. However, statistical prediction models contain small number of important predictors, which means limited informative capability. Also, predictive statistical models that provide some type of pseudo-correct regular statistical patterns, are used without previous understanding of the used data causality. Machine Learning (ML) algorithms as area in Artificial Intelligence (AI) provide the ability to interpret and understand data in more sophisticated way. Artificial Neural Networks as kind of ML methods use non-linear algorithms, considering links and associations between parameters, while statistical use one-step-ahead linear processes to improve only short-term prediction's accuracy by minimizing cost function. Disregarding that designing an optimal artificial neural network is very complex process, they are considered as potential solution for overcoming main flaws of statistical prediction models. However, they will not automatically improve predictions accuracy, so several artificial neural networks and traditional statistical methods are evaluated and analyzed through accuracy measures for prediction purposes in various fields of applications. Based on gained results, couple of techniques for improving artificial neural networks are proposed to get better accuracy results than statistical predictive methods.
Journal ArticleDOI
TL;DR: In this article , the authors evaluate and analyze several statistical and ML methods, including some artificial neural networks, through accuracy measures for prediction purposes in various fields of applications, and propose a couple of techniques for improving suggested ML methods and Artificial Neural Networks are proposed to get better accuracy results.
Abstract: Compared to traditional statistical models, Machine Learning (ML) algorithms provide the ability to interpret, understand and summarize patterns and regularities in observed data for making predictions in an advanced and more sophisticated way. The main reasons for the advantage of ML methods in making predictions are a small number of significant predictors of the statistical models, which means limited informative capability, and pseudo-correct regular statistical patterns, used without previous understanding of the used data causality. Also, some ML methods, like Artificial Neural Networks, use non-linear algorithms, considering links and associations between parameters. On the other hand, statistical models use one-step-ahead linear processes to improve only short-term prediction accuracy by minimizing a cost function. Although designing an optimal ML model can be a very complex process, it can be used as a potential solution for making improved prediction models compared to statistical ones. However, ML models will not automatically improve prediction accuracy, so it is necessary to evaluate and analyze several statistical and ML methods, including some artificial neural networks, through accuracy measures for prediction purposes in various fields of applications. A couple of techniques for improving suggested ML methods and artificial neural networks are proposed to get better accuracy results
DOI
15 Mar 2023
TL;DR: In this paper , the authors proposed a transparent, immutable, accessible, and monetizable distributed weather data system based on blockchain, containing: governance, oracle, data storage, and marketplace layer.
Abstract: Obtaining and distributing real-time and localized primary and secondary weather datasets is very challenging. Primary datasets are collected, aggregated, and released from government-controlled weather stations in a complicated manual bureaucratic process. They are prone to maintenance and breakdowns, which lead to missing large data sections, requiring accessible, transparent, and affordable data cleaning. Also, there is no easy way for an independent researcher or a small company to set up a weather station and aggregate it into a broader system. Secondary datasets obtained from primary ones can lead to a heterogeneous and unstandardized marketplace caused by various data formats from different providers. It is necessary to provide a transparent, immutable, accessible, and monetizable distributed weather data system based on blockchain, containing: governance, oracle, data storage, and marketplace layer.

Cited by
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01 Aug 1993
TL;DR: High-performance concretes are made with carefully selected high-quality ingredients and optimized mixture designs; these are batched, mixed, placed, compacted and cured to the highest industry standards.
Abstract: High-performance concretes are made with carefully selected high-quality ingredients and optimized mixture designs; these are batched, mixed, placed, compacted and cured to the highest industry standards. Typically, such concretes will have a low water-cementing materials ratio of 0.20 to 0.45. Plasticizers are usually used to make these concretes fluid and workable. High-performance concrete almost always has a higher strength than normal concrete. However, strength is not always the primary required property. For example, a normal strength concrete with very high durability and very low permeability is considered to have highperformance properties. Bickley and Fung (2001) demonstrated that 40 MPa (6,000 psi) highperformance concrete for bridges could be economically made while Fig. 17-1. High-performance concrete is often used in bridges (left) and tall buildings (right). (70017, 70023) High-performance concrete (HPC) exceeds the properties and constructability of normal concrete. Normal and special materials are used to make these specially designed concretes that must meet a combination of performance requirements. Special mixing, placing, and curing practices may be needed to produce and handle high-performance concrete. Extensive performance tests are usually required to demonstrate compliance with specific project needs (ASCE 1993, Russell 1999, and Bickley and Mitchell 2001). High-performance concrete has been primarily used in tunnels, bridges, and tall buildings for its strength, durability, and high modulus of elasticity (Fig. 17-1). It has also been used in shotcrete repair, poles, parking garages, and agricultural applications. High-performance concrete characteristics are developed for particular applications and environments; some of the properties that may be required include:

311 citations

Journal Article
TL;DR: The behavior of high-strength concrete (HSC) subjected to uniaxial tension was investigated in this article, where an analytical expression was proposed for the relationship between axial stress and crack width in HSC.
Abstract: The behavior of high-strength concrete (HSC) subjected to uniaxial tension was investigated in this paper. The complete tensile stress-deformation response of HSC was acquired through an extensive experimental program. The experimental program comprised of testing concrete with compressive strengths equal to 6 ksi (41 MPa), 12 ksi (83 MPa), and 15 ksi (103 MPa). An analytical expression was proposed for the relationship between the axial stress and crack width in HSC. This relationship was developed for the determination of fracture parameters such as fracture energy and the characteristic length for the three compressive strengths. The higher compressive strength concretes exhibited larger fracture energies and lower characteristic lengths. A relationship between the tensile and compressive strengths of HSC was established. Although HSCs exhibit larger tensile strengths, there is a decrease in the ratio of tensile-to-compressive strength at higher compressive strengths. Accordingly, the uniaxial tensile strength of HSC can be predicted by the expression f't = 6.1 times the square root of f'c, where f't and f'c are the tensile and compressive strengths, respectively. In comparison with the expression developed for normal strength concrete (f't = 6.5 times the square root of f'c), this expression predicts lower tensile strengths at higher strength levels.

9 citations