IDEAS home Printed from https://ideas.repec.org/a/vrs/bjrecm/v7y2019i1p110-122n7.html
   My bibliography  Save this article

Preliminary Construction Cost Estimate in Yemen by Artificial Neural Network

Author

Listed:
  • Hakami Waled

    (University of Science and Technology, Sana’a, Yemen)

  • Hassan Awad

    (Sudan University of Science and Technology, Khartoum, Sudan)

Abstract

The construction industry in Yemen is currently facing challenges associated with rapid development of technology; thus, cost estimation is considered a key factor that should align with this technological advancement. The main problem in the area of preliminary estimate in Yemen is how to make estimate accurately. The aim of this study is to analyse a modern method of preliminary cost estimation in Yemen to prove its efficiency over the traditional method. Therefore, a wide range of literature sources regarding the preliminary estimates using Artificial Neural Network (ANN) as a modern technique is considered. Both qualitative and quantitative approaches were adopted in this study depending on the theoretical premises discussed in literature and the ANN technique, respectively. The independent variables were chosen in the course of literature review. The collected data were classified and processed regarding the ANN constraints and encoded for building and analysis of the ANN model. NeuroSolution 6 software was used to build, train, and test the network as well as to perform sensitivity analysis. In addition, the results of training, testing, and sensitivity analysis were obtained and discussed showing high effectiveness of accurate estimates with less than 1 % error. The ANN model is a more powerful technique for estimating costs in the preliminary stage that should be used in the developing countries instead of the traditional methods.

Suggested Citation

  • Hakami Waled & Hassan Awad, 2019. "Preliminary Construction Cost Estimate in Yemen by Artificial Neural Network," Baltic Journal of Real Estate Economics and Construction Management, Sciendo, vol. 7(1), pages 110-122, January.
  • Handle: RePEc:vrs:bjrecm:v:7:y:2019:i:1:p:110-122:n:7
    DOI: 10.2478/bjreecm-2019-0007
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/bjreecm-2019-0007
    Download Restriction: no

    File URL: https://libkey.io/10.2478/bjreecm-2019-0007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Margaret Emsley & David Lowe & A. Roy Duff & Anthony Harding & Adam Hickson, 2002. "Data modelling and the application of a neural network approach to the prediction of total construction costs," Construction Management and Economics, Taylor & Francis Journals, vol. 20(6), pages 465-472.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Phattara Khumprom & Nita Yodo, 2019. "A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm," Energies, MDPI, vol. 12(4), pages 1-21, February.
    2. Chou, Jui-Sheng & Tai, Yian & Chang, Lian-Ji, 2010. "Predicting the development cost of TFT-LCD manufacturing equipment with artificial intelligence models," International Journal of Production Economics, Elsevier, vol. 128(1), pages 339-350, November.
    3. Swei, Omar & Gregory, Jeremy & Kirchain, Randolph, 2017. "Construction cost estimation: A parametric approach for better estimates of expected cost and variation," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 295-305.
    4. Agnieszka Leśniak & Krzysztof Zima, 2018. "Cost Calculation of Construction Projects Including Sustainability Factors Using the Case Based Reasoning (CBR) Method," Sustainability, MDPI, vol. 10(5), pages 1-14, May.
    5. Wei Tong Chen & Ying-Hua Huang, 2006. "Approximately predicting the cost and duration of school reconstruction projects in Taiwan," Construction Management and Economics, Taylor & Francis Journals, vol. 24(12), pages 1231-1239.
    6. Ivan Damnjanovic & Xue Zhou, 2009. "Impact of crude oil market behaviour on unit bid prices: the evidence from the highway construction sector," Construction Management and Economics, Taylor & Francis Journals, vol. 27(9), pages 881-890.
    7. Zangeneh, Pouya & McCabe, Brenda, 2022. "Modelling socio-technical risks of industrial megaprojects using Bayesian Networks and reference classes," Resources Policy, Elsevier, vol. 79(C).
    8. Qiao, Yu & Fricker, Jon D. & Labi, Samuel, 2019. "Effects of bundling policy on project cost under market uncertainty: A comparison across different highway project types," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 606-625.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:bjrecm:v:7:y:2019:i:1:p:110-122:n:7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.