CIVILICA We Respect the Science
Publisher of Iranian Journals and Conference Proceedings

A novel deep learning model to estimate and predict residential construction cost

Credit to Download: 1 | Page Numbers 6 | Abstract Views: 80

How to Download This Paper

For Downloading the Fulltext of CIVILICA papers please visit the orginal Persian Section of website.

Authors A novel deep learning model to estimate and predict residential construction cost

  Amir Hossein Pakizeh - M.Sc. student, Department of Civil Engineering, Sharif University of Technology, Tehran , Iran
  Hamed Kashani - Assistant Professor, Department of Civil Engineering, Sharif University of Technology, Tehran, Iran


The accurate prediction of nonstationary construction costs can contribute to the enhancement of the understanding about sources and patterns of construction costs fluctuations. This understanding can facilitate informed decision making about investment in construction projects. It can help investors better manage the risks associated with construction cost fluctuations and achieve maximum profit. This paper puts forward a novel prediction model for the construction costs of residential buildings. The proposed model comprises two sub -models. A set of variables that determine the building characteristics and the market conditions are the inputs to the first sub-model. This sub-model uses unsupervised deep Boltzmann machine (DBM) learning approach to learn the complex relationships among the explained and explanatory variables. The results are then used in order to build a regression model using support vector regression (SVR) and multi -layer Perceptron (MLP) . The first sub-model estimates the current construction cost of a given residential building. The second sub -model, which is based on the adaptive multiscale ensemble-learning paradigm, incorporates ensemble empirical mode decomposition (EEMD) and autoregressive integrated moving average (ARIMA). This sub-model generates a construction cost time series based on estimated costs of the first sub-model and predicts the construction cost of the residential building under study in the following time steps. In order to evaluate the prediction performance of the proposed model, it is applied to a dataset on the construction costs of 360 residential buildings. The results show that the model is successfully able to predict construction costs of residential buildings to the accuracy performance of 98%.


nonstationary construction costs; unsupervised deep Boltzmann machine (DBM); support vector regression (SVR); ensemble empirical mode decomposition (EEMD); autoregressive integrated moving average (ARIMA);

Perma Link
COI code: SECM03_496

how to cite to this paper:

If you want to refer to this article in your research, you can easily use the following in the resources and references section:
Pakizeh, Amir Hossein & Hamed Kashani, 2019, A novel deep learning model to estimate and predict residential construction cost, rd International Conference on Applied Researches in Structural Engineering and Construction Management (secm2019), تهران, دانشگاه صنعتي شريف, the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Pakizeh, Amir Hossein & Hamed Kashani, 2019)
Second and more: (Pakizeh & Kashani, 2019)
For a complete overview of how to citation please review the following CIVILICA Guide (Citation)


The University/Research Center Information:
Type: state university
Paper No.: 12722
in University Ranking and Scientometrics the Iranian universities and research centers are evaluated based on scientific papers.

Research Info Management

Export Citation info of this paper to research management softwares

New Related Papers

Iran Scientific Advertisment Netword

Share this paper


COI is a national code dedicated to all Iranian Conference and Journal Papers. the COI of each paper can be verified online.