case study in maintenance cost estimation
John Ahmet Erkoyuncu (Author), Bernadin Namoano (Author), Dominik Kozjek (Author), Rok Vrabič (Author)

Abstract

Cost estimation is critical for effective decision making in engineering projects. However, it is often hampered by a lack of sufficient data. For this, data imputation techniques can be used to estimate missing costs based on statistical estimates or analogies with historical data. However, these techniques are often limited because they do not consider the existing knowledge of experts. In this paper, a novel cognitive data imputation technique is proposed for cost estimation that uses explanatory interactive machine learning to integrate and improve human knowledge. Through a case study in maintenance cost estimation the effectiveness of the approach is demonstrated.

Keywords

artificial intelligence;maintenance;cost estimation;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FS - Faculty of Mechanical Engineering
UDC: 004.8:658.5
COBISS: 158961155 Link will open in a new window
ISSN: 0007-8506
Views: 2036
Downloads: 89
Average score: 0 (0 votes)
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Other data

Secondary language: Slovenian
Secondary keywords: umetna inteligenca;vzdrževanje;ocena stroškov;
Type (COBISS): Article
Pages: str. 385-388
Volume: ǂVol. ǂ72
Issue: ǂiss. ǂ1
Chronology: 2023
DOI: 10.1016/j.cirp.2023.03.036
ID: 19574776
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