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An, X, Li, H, Zuo, J, Ojuri, O, Wang, Z and Ding, J (2018) Identification and Prevention of Unbalanced Bids Using the Unascertained Model. Journal of Construction Engineering and Management, 144(11).

Charkhakan, M H and Heravi, G (2018) Risk Manageability Assessment to Improve Risk Response Plan: Case Study of Construction Projects in Iran. Journal of Construction Engineering and Management, 144(11).

Devine, R D, Barbachyn, S M, Thrall, A P and Kurama, Y C (2018) Effect of Tripping Prefabricated Rebar Assemblies on Bar Spacing. Journal of Construction Engineering and Management, 144(11).

ElMousalami, H H, Elyamany, A H and Ibrahim, A H (2018) Predicting Conceptual Cost for Field Canal Improvement Projects. Journal of Construction Engineering and Management, 144(11).

  • Type: Journal Article
  • Keywords: Conceptual construction cost; Parametric model; Key parameters selection; Machine learning; Multiple regression analysis; Artificial neural networks; Field canal improvement projects;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001561
  • Abstract:
    A conceptual cost estimation is prepared to assess the feasibility of a project or establish the project’s initial budget at the early stages of the project. The main objective of the paper is automating the cost estimate at the conceptual stage with the highest accuracy. The key contribution of this paper is developing a quadratic regression model with a prediction accuracy of 9.12% and 7.82% for training and validation, respectively. This research has identified the model’s key parameters to establish a reliable conceptual cost estimate model for field canal improvement projects (FCIPs). Two machine learning models were developed utilizing multiple regression analysis (MRA) and artificial neural networks (ANNs). Searching for a better model, several data transformations have been conducted to improve the model performance. The quadratic regression model has shown the highest performance based on the correlation and the mean absolute percentage error (MAPE) criteria. A parametric model has been presented in this paper to predict the conceptual cost of FCIPs. This research maintains the importance of identifying key parameters and conducting data transformation and sensitivity analysis for developing a reliable parametric cost prediction model.

Gupta, M, Hasan, A, Jain, A K and Jha, K N (2018) Site Amenities and Workers’ Welfare Factors Affecting Workforce Productivity in Indian Construction Projects. Journal of Construction Engineering and Management, 144(11).

Orgut, R E, Zhu, J, Batouli, M, Mostafavi, A and Jaselskis, E J (2018) Metrics That Matter: Core Predictive and Diagnostic Metrics for Improved Project Controls and Analytics. Journal of Construction Engineering and Management, 144(11).

Sackey, S and Kim, B (2018) Environmental and Economic Performance of Asphalt Shingle and Clay Tile Roofing Sheets Using Life Cycle Assessment Approach and TOPSIS. Journal of Construction Engineering and Management, 144(11).

Wang, Z, Hu, H and Gong, J (2018) Modeling Worker Competence to Advance Precast Production Scheduling Optimization. Journal of Construction Engineering and Management, 144(11).

Xu, J, Jin, R, Piroozfar, P, Wang, Y, Kang, B, Ma, L, Wanatowski, D and Yang, T (2018) Constructing a BIM Climate–Based Framework: Regional Case Study in China. Journal of Construction Engineering and Management, 144(11).

Yang, F, Li, X, Song, Z, Li, Y and Zhu, Y (2018) Job Burnout of Construction Project Managers: Considering the Role of Organizational Justice. Journal of Construction Engineering and Management, 144(11).