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Comparative Representation of Two Models for Predicting the Productivity of Column and Wall Concreting Process

Autor(en): ORCID

Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Buildings, , n. 11, v. 12
Seite(n): 1809
DOI: 10.3390/buildings12111809
Abstrakt:

One of the most important tasks of managing the construction process is to achieve the highest possible productivity. The productivity that can be achieved on a construction site depends on a number of influencing factors and on the type of work that is executed. Concrete works are a crucial activity when constructing high-rise buildings built in the RC frame structural system. Therefore, it is very important to adequately manage the concreting process in order to meet the set deadlines and reduce costs. This paper presents an approach for predicting the productivity of the concreting process based on the conducted quantitative research, by recording the concreting process on construction sites of buildings in Niš, Serbia. The concreting of reinforced concrete columns and walls on seven construction sites was recorded for 20 months. The total amount of fresh concrete that is built into the elements is 848 m³ and the total duration is 114 h of work. Factors that can affect productivity have been identified and, by applying the multiple linear regression and simulation methods and techniques and using the discrete event method and the agent-based method, models have been developed to predict the productivity of the concreting of reinforced concrete columns and walls. An analysis of the developed models was performed, and a comparative presentation was provided.

Copyright: © 2022 by the authors; licensee MDPI, Basel, Switzerland.
Lizenz:

Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden.

  • Über diese
    Datenseite
  • Reference-ID
    10700012
  • Veröffentlicht am:
    10.12.2022
  • Geändert am:
    15.02.2023
 
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