A Duration Prediction Using a Material-Based Progress ManagementMethodology for Construction Operation Plans

Collection with item attached
2017
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/473574.do
DOI
10.3390/su9040635
Title
A Duration Prediction Using a Material-Based Progress ManagementMethodology for Construction Operation Plans
Description
This work was supported by the Basic Science Research Program throughthe National Research Foundation of Korea (NRF), with funding from theMinistry of Education (NRF-2015R1D1A1A01058221). The authors gratefullyacknowledge this support.
abstract
Precise and accurate prediction models for duration and cost enable contractors to improve their decision making for effective resource management in terms of sustainability in construction. Previous studies have been limited to cost-based estimations, but this study focuses on a material-based progress management method. Cost-based estimations typically used in construction, such as the earned value method, rely on comparing the planned budget with the actual cost. However, accurately planning budgets requires analysis of many factors, such as the financial status of the sectors involved. Furthermore, there is a higher possibility of changes in the budget than in the total amount of material used during construction, which is deduced from the quantity take-off from drawings and specifications. Accordingly, this study proposes a material-based progress management methodology, which was developed using different predictive analysis models (regression, neural network, and auto-regressive moving average) as well as datasets on material and labor, which can be extracted from daily work reports from contractors. A case study on actual datasets was conducted, and the results show that the proposed methodology can be efficiently used for progress management in construction.
provenance
Made available in Cube on 2018-09-28T10:23:16Z (GMT). No. of bitstreams: 0
language
English
author
Ko, Yongho
Han, Seungwoo
orcid
Han, Seungwoo/0000-0001-5739-9487
accessioned
2018-09-28T10:23:16Z
available
2018-09-28T10:23:16Z
issued
2017
citation
SUSTAINABILITY(9): 4
issn
2071-1050
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/473574.do
Funder
교육부
Funding Program
개인기초연구(교육부)
Project ID
1345271310
Jurisdiction
Rep.of Korea
Project Name
Big data based Construction Operation REcognition model (Big-CORE) development
rights
openAccess
subject
progress management
daily work report
regression
neural network
auto-regressive moving average
type
article


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