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Abstract: The extreme traffic measures during the COVID-19 lockdown provided a unique opportunity to gain better insight into the relationship between traffic characteris-tics and NO2 concentrations in Maribor, a small Slove-nian city. NO2, traffic and meteorological data were sta-tistically processed in detail for March and April 2018, 2019 and 2020 to get a historical insight and to exclude the specifics of the lockdown period. The extreme event resulted in an average reduction of road traffic of 42%. The decrease in the number of passenger cars ranged from 33.9 to 60.3% per day with the largest decrease on the motorway. Daily averages of heavy goods traffic de-clined on the motorway and the expressway by 24.6% and 7%, respectively. Traffic characteristics were reflect-ed in a 24–27% decrease in NO2 concentrations at the urban station. The change is smaller than the change in traffic volume, which could be explained by the change in the composition of the vehicle fleet due to the increase in NO2-dominant traffic sources, e.g. diesel heavy goods vehicles. The presented results are relevant for improv-ing air quality and sustainable mobility management in small cities. They highlight the important role of reor-ganisation of heavy goods traffic in urban logistics.
Keywords: road traffic, extreme event, COVID-19 lockdown, NO2 emissions, meteorological conditions, air pollution
Published in DKUM: 13.03.2025; Views: 0; Downloads: 1
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Abstract: Knowledge discovery from databases (KDD) and data mining (DM) belong to the field of artificial intelligence (AI). The integration of artificial intelligence into various segments of the construction industry is still in its infancy, but it is expected to be used more widely soon, driven by the development of databases and data warehouses. By using BIM (Building Information Modelling) technologies in the planning of new buildings, we will be able to obtain valuable data. The situation is different for old, existing buildings and the building engineering associated with these properties. Civil engineers, renovation planners and architects need knowledge of the building before renovation. This knowledge is much less than the possibilities that exist. Information about the building can be found in provincial archives. For historic buildings, 10- 15% of the plans, drawings, descriptions, or projects are available. The remaining 85% must be researched on site, which is a lengthy and costly process and hinders the construction process. The question arose as to how the findings from the study of buildings based on written and preserved sources can be applied to the 85% of buildings for which no data is available. This paper presents the use of the collected data as an idea for an initiative to develop a database and modelling using artificial intelligence algorithms. The research study investigates the feature “load-bearing wall” for residential buildings with basements and floors built between 1857 and 1948 in the former Austro-Hungarian Empire. The aim of this study is to create a model to predict the characteristics of a building for which no archival material is available. The study is based on the use of artificial intelligence in the creation of decision trees to help engineers improve their knowledge of historic buildings in the former Austro-Hungarian Empire and building engineering for historic objects.
Keywords: knowledge discovery from data, machine learning, Austro-Hungarian Monarchy buildings
Published in DKUM: 03.03.2025; Views: 0; Downloads: 3
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Abstract: Diplomsko delo se osredotoča na analizo krožnega gospodarstva gradbene industrije v Sloveniji s posebnim poudarkom na primeru javnega podjetja Nigrad, d. o. o. V teoretičnem okviru so najprej definirani osnovni koncepti krožnega gospodarstva in gradbenih odpadkov. Poudarjeni so pomen ponovne uporabe in recikliranja odpadkov ter zakonske zahteve, ki urejajo to področje v Sloveniji. V teoretičnem delu sta najprej predstavljena zgodovina zbiranja in predelave gradbenih odpadkov ter trenutno stanje nastajanja teh odpadkov. V nadaljevanju je povzet zakonodajni okvir odpadkov in krožnega gospodarstva v Sloveniji. Kot študija primera je v praktičnem delu naloge predstavljen princip krožnega gospodarstva na primeru javnega podjetja Nigrad, d. o. o. V praktičnem delu naloge so najprej predstavljene rešitve podjetja za predelavo in ponovno uporabo gradbenih odpadkov. Nato so analizirani materialni vhodi in izhodi, ki so bili realizirani v zadnjih dveh letih delovanja podjetja. V zaključku naloge so podani ključna spoznanja in predlogi za izboljšanje ravnanja z gradbenimi odpadki v podjetju ter spodbujanje krožnega gospodarstva v praksi.
Keywords: gradbeništvo, krožno gospodarstvo, gradbeni odpadki, ponovna uporaba odpadkov
Published in DKUM: 05.09.2024; Views: 60; Downloads: 2
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Abstract: Practical example of signal processing from strain gauge, inductive transducer and total station measurements are used to illustrate the features of the bridge load testing measurements. FFT provides accurate representation of physical behavior for static and dynamic signals obtained when loading the bridge. As a reference measurement the signal from inductive transducer was taken. A static part of the load test was also geodetically measured and theoretically calculated. The results are comparable.
Keywords: bridges, measurements, strain gauge, load test, deformation, strain, signal processing
Published in DKUM: 12.07.2023; Views: 445; Downloads: 33
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Keywords: arheologija, domoznanstvo, turške šance
Published in DKUM: 19.06.2020; Views: 1322; Downloads: 159
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