^ Selection of construction equipment – excavators and dump trucks in terms of minimizing the emission of CO2 by using forecasting methods | Structurae
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Selection of construction equipment – excavators and dump trucks in terms of minimizing the emission of CO2 by using forecasting methods

Author(s):
Medium: journal article
Language(s): English
Published in: Budownictwo i Architektura, , n. 1, v. 15
Page(s): 133-142
DOI: 10.24358/bud-arch_16_151_14
Abstract:

The article predicted CO₂ emission by a set of machines: excavator and dump trucks. The emissivity of carbon dioxide during the execution of a specific work task depends on the performance of the machines. In the first stage, work performance of excavators was projected. The following technical and organisational data having a hypothetical influence on the performance of excavators were collected: bucket capacity, type of working tool, category of land, load capacity of a mean of transport, type of access road, work experience of an operator, humidity of the soil, distance of the soil disposal, air temperature, failure frequency. The linguistic variables were coded, the data was transformed in a way that ensures that the best results were obtained. The method of multiple regression were used for forecasting. Analysis of the autocorrelation and partial autocorrelation residues and sensitivity analysis was done. MAPE errors forecasts were calculated. On the basis of a predictive model, an example of calculation of selection of machines in terms of carbon dioxide emission was made. The calculation formula to quantify the number of kilograms of carbon dioxide produced during earthworks was formulated. Analyses showed that the criterion of minimizing carbon dioxide emissions are directly proportional to the excavator’s bucket capacity and capacity of means of transport.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.24358/bud-arch_16_151_14.
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    data sheet
  • Reference-ID
    10475768
  • Published on:
    25/11/2020
  • Last updated on:
    25/11/2020