Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 197-209 |
| Number of pages | 13 |
| Journal | International Journal of Construction Management |
| Volume | 17 |
| Issue number | 3 |
| Early online date | 23 Jun 2016 |
| DOIs | |
| Publication status | Published - 2017 |
| Externally published | Yes |
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Automated estimation of completion time for dam projects. / Golizadeh, Hamed; BANIHASHEMI, Saeed; Sadeghifam, Aidan; Preece, Christopher.
In: International Journal of Construction Management, Vol. 17, No. 3, 2017, p. 197-209.Research output: Contribution to journal › Article
TY - JOUR
T1 - Automated estimation of completion time for dam projects
AU - Golizadeh, Hamed
AU - BANIHASHEMI, Saeed
AU - Sadeghifam, Aidan
AU - Preece, Christopher
PY - 2017
Y1 - 2017
N2 - An accurate estimation of construction duration for the successful completion of infrastructure projects is as important a task as keeping a project within the stipulated budget and quality. This research aims at developing an automated approach for estimating the duration of construction for dam projects by virtue of artificial neural network (ANN). Through literature searches and expert interviews, variables that critically influence the construction of dam projects were identified. A wide range of data on Iranian dam projects was used to develop seven ANN models. Different datasets were used to achieve the best performance by assessing the RMSE and R2 values as the reliability and validity indicators. Finally, a web-based automated prototype was developed and validated in order for stakeholders to estimate the duration of dam projects based on the ANN method and benefit from better infrastructure project management practices
AB - An accurate estimation of construction duration for the successful completion of infrastructure projects is as important a task as keeping a project within the stipulated budget and quality. This research aims at developing an automated approach for estimating the duration of construction for dam projects by virtue of artificial neural network (ANN). Through literature searches and expert interviews, variables that critically influence the construction of dam projects were identified. A wide range of data on Iranian dam projects was used to develop seven ANN models. Different datasets were used to achieve the best performance by assessing the RMSE and R2 values as the reliability and validity indicators. Finally, a web-based automated prototype was developed and validated in order for stakeholders to estimate the duration of dam projects based on the ANN method and benefit from better infrastructure project management practices
KW - construction duration
KW - dam
KW - ANN
KW - prediction
KW - infrastructure
UR - http://www.scopus.com/inward/record.url?scp=84976270837&partnerID=8YFLogxK
UR - http://www.mendeley.com/research/automated-estimation-completion-time-dam-projects
U2 - 10.1080/15623599.2016.1192249
DO - 10.1080/15623599.2016.1192249
M3 - Article
VL - 17
SP - 197
EP - 209
JO - International Journal of Construction Management
JF - International Journal of Construction Management
SN - 1562-3599
IS - 3
ER -