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Research on Detection Model of Abnormal Data in Engineering Cost List

Jingyi Dai

Jingyi Dai

School of Resources and Architectural Engineering, Gannan University of Science and TechnologyChina
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Dai, Jingyi
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Dandan Ke

Dandan Ke

School of Resources and Architectural Engineering, Gannan University of Science and TechnologyChina
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Ke, Dandan
  
15 giu 2022
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Pubblicato online: 15 giu 2022
Pagine: 2567 - 2580
Ricevuto: 01 gen 2022
Accettato: 27 mar 2022
DOI: https://doi.org/10.2478/amns.2021.2.00203
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© 2023 Jingyi Dai et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Valuation process of quantity bill
Valuation process of quantity bill

Fig. 2

Extensive unit cost distribution of cast-in-place component reinforcement
Extensive unit cost distribution of cast-in-place component reinforcement

Fig. 3

Flowchart for detecting abnormal unit price data
Flowchart for detecting abnormal unit price data

Fig. 4

Schematic diagram of K-means clustering results
Schematic diagram of K-means clustering results

Fig. 5

Experimental results of Laplace smoothing parameters K=9
Experimental results of Laplace smoothing parameters K=9

Fig. 6

Results of Laplace smoothing parameters K=13
Results of Laplace smoothing parameters K=13

Fig. 7

K-means K value and accuracy results of the two Bayesian classification methods
K-means K value and accuracy results of the two Bayesian classification methods

Fig. 8

Flow chart of detecting abnormal data in the project cost list
Flow chart of detecting abnormal data in the project cost list

Fig. 9

Abnormal data detection K value experiment diagram
Abnormal data detection K value experiment diagram

Fig. 10

Experimental diagram of the comparison between the traditional method and the method of this topic
Experimental diagram of the comparison between the traditional method and the method of this topic
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Inglese
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