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Symbiotic organisms search-optimized deep learning technique for mapping construction cash flow considering complexity of project

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  • Cheng, Min-Yuan
  • Cao, Minh-Tu
  • Herianto, Jason Ghorman

Abstract

Accurate construction cash flow forecasting is very important in successfully managing cost during execution of building projects. Despite many research efforts, it still remains a difficult issue in attaining an accurate forecast model of cash flows due to the risk factors and characteristic of the project. Additionally, cash flow of the construction projects is strongly impacted by sequence and non-sequence factors. Hence, this study proposed a novel artificial intelligence(AI)-based inference model, named symbiotic organisms search-optimized neural network-long short-term memory (SOS-NN-LSTM), which employs symbiotic organisms search (SOS) algorithm to obtain the suitable hyperparameters of the neural network (NN) and long short-term memory (LSTM) for establishing a robust hybridization model. In the proposed model, the LSTM technique addresses time series problem with considering the complexity of projects while the NN technique aims at tackling non-sequence factors. The experimental results on 13 construction projects have supported the SOS-NN-LSTM as the best model in forecasting the cash flow by achieving the greatest values of (2.55%), MAPE (5.71%), MAE (2.07%), and R2 (0.983). The statistical result further reveals that accuracy of cash flow forecasting can be improved at least 13.4% and 12.0% in terms of RMSE and MAE, respectively, in comparison with other comparative AI-based inference models. The SOS-NN-LSTM model is thus a useful tool to help managers forecast and control cash flow of construction projects.

Suggested Citation

  • Cheng, Min-Yuan & Cao, Minh-Tu & Herianto, Jason Ghorman, 2020. "Symbiotic organisms search-optimized deep learning technique for mapping construction cash flow considering complexity of project," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    • Handle: RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920302691
    DOI: 10.1016/j.chaos.2020.109869
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    References listed on IDEAS

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    1. Lv, Ya-jun & Wang, Jun-wei & Wang, Julian & Xiong, Cheng & Zou, Liang & Li, Ly & Li, Da-wang, 2020. "Steel corrosion prediction based on support vector machines," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    2. Min-Yuan Cheng & Yu-Wei Wu & Le Trung Dan & Andreas F. Van Roy, 2013. "Enhanced Time-Dependent Evolutionary Fuzzy Support Vector Machines Inference Model For Cash Flow Prediction And Estimate At Completion," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(04), pages 679-710.
    3. A. H. Boussabaine & A. P. Kaka, 1998. "A neural networks approach for cost flow forecasting," Construction Management and Economics, Taylor & Francis Journals, vol. 16(4), pages 471-479.
    4. Lahmiri, Salim & Bekiros, Stelios, 2019. "Cryptocurrency forecasting with deep learning chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 35-40.
    5. Henry A. Odeyinka & John Lowe & Ammar P. Kaka, 2013. "Artificial neural network cost flow risk assessment model," Construction Management and Economics, Taylor & Francis Journals, vol. 31(5), pages 423-439, May.
    6. A. H. Boussabaine & Taha Elhag, 1999. "Applying fuzzy techniques to cash flow analysis," Construction Management and Economics, Taylor & Francis Journals, vol. 17(6), pages 745-755.
    7. Qingbin Cui & Makarand Hastak & Daniel Halpin, 2010. "Systems analysis of project cash flow management strategies," Construction Management and Economics, Taylor & Francis Journals, vol. 28(4), pages 361-376.
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    Cited by:

    1. Mahir Msawil & Faris Elghaish & Krisanthi Seneviratne & Stephen McIlwaine, 2021. "Developing a Parametric Cash Flow Forecasting Model for Complex Infrastructure Projects: A Comparative Study," Sustainability, MDPI, vol. 13(20), pages 1-26, October.

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