Economics at your fingertips  

Using ANN to Predict the Impact of Communication Factors on the Rework Cost in Construction Projects

Roman Trach (), Yuliia Trach () and Marzena Lendo-Siwicka ()
Additional contact information
Roman Trach: Institute of Civil Engineering, Warsaw University of Life Sciences, 02787 Warsaw, Poland
Yuliia Trach: Institute of Civil Engineering, Warsaw University of Life Sciences, 02787 Warsaw, Poland
Marzena Lendo-Siwicka: Institute of Civil Engineering, Warsaw University of Life Sciences, 02787 Warsaw, Poland

Energies, 2021, vol. 14, issue 14, 1-15

Abstract: The construction sector has a large impact on the environment and available resources. Natural resources and energy consumption occurs not only during the operation of the facility, but also during its construction. In addition, this situation often occurs when work already completed requires rework. In such cases, not only the reuse of resources and energy occurs but also generation of waste. Many studies support the relationship between communication and project efficiency, which is expressed in the cost of rework. At present there is no available tool to quantify the evaluation of this relationship. This study aims to fill this knowledge gap. The article purpose was to create ANNs (artificial neural networks) for assessing and predicting the impact of communication factors on rework costs in construction projects. During the data collection phase, 12 factors that influence communication were identified and assessed. The level of rework costs in 18 construction projects was also calculated. We used ANN, which is a two-layer feedforward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer. The network input layer consists of 12 neurons while the hidden layer consists of 10 neurons and one output neuron. The optimal results of the mean square error and correlation were shown by the Levenberg–Marquardt algorithm. The proposed model can be used by project management as the integration decision support tool aimed at decreasing the number of reworks and reducing energy and resource consumption in construction projects.

Keywords: artificial neural networks (ANNs); communication; rework cost; energy and resource consumption; construction projects (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed

Downloads: (external link) (application/pdf) (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Energies is currently edited by Ms. Estelle Chen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

Page updated 2022-01-01
Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4376-:d:597746