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Addy, M N, Akunyumu, S and Simons, B (2019) Key risk factors affecting renewable energy independent power producer (IPP) set-up projects in developing countries. Journal of Engineering, Design and Technology , 17(06), 1340–55.

Aghimien, D O, Aigbavboa, C O and Thwala, W D (2019) Microscoping the challenges of sustainable construction in developing countries. Journal of Engineering, Design and Technology , 17(06), 1110–28.

Asa, E, Anna, A S and Baffoe-Twum, E (2019) An investigation of mechanical behavior of concrete containing crushed waste glass. Journal of Engineering, Design and Technology , 17(06), 1285–303.

Bendak, S and Alhammadi, A A (2019) A multi-criteria decision-making approach to minimising fire risk in detached house designs. Journal of Engineering, Design and Technology , 17(06), 1146–60.

Bhaurkar, V P and Thakur, A G (2019) Investigation of crack in beams using anti-resonance technique and FEA approach. Journal of Engineering, Design and Technology , 17(06), 1266–84.

Entner, D, Prante, T, Vosgien, T, Z─âvoianu, A, Saminger-Platz, S, Schwarz, M and Fink, K (2019) Potential identification and industrial evaluation of an integrated design automation workflow. Journal of Engineering, Design and Technology , 17(06), 1085–109.

Katoch, V and Mohan, S (2019) Design and fabrication of a safety frame for workers carrying out head lifting at construction sites. Journal of Engineering, Design and Technology , 17(06), 1250–65.

Matarneh, S, Danso-Amoako, M, Al-Bizri, S, Gaterell, M and Matarneh, R (2019) BIM-based facilities information: streamlining the information exchange process. Journal of Engineering, Design and Technology , 17(06), 1304–22.

Oke, A E, Ogunsemi, D R and Adeyelu, M (2019) Quadrant and gap analysis of required and exhibited quantity surveyors’ competencies. Journal of Engineering, Design and Technology , 17(06), 1161–73.

Othman, A A E and Hafez, M G (2019) A framework integrating corporate social responsibility for marketing architectural design firms in developing countries. Journal of Engineering, Design and Technology , 17(06), 1174–91.

Owusu-Manu, D, Edwards, D J, Mohammed, A, Thwala, W D and Birch, T (2019) Short run causal relationship between foreign direct investment (FDI) and infrastructure development. Journal of Engineering, Design and Technology , 17(06), 1202–21.

Rajabion, L, Sataei Mokhtari, A, Khordehbinan, M W, Zare, M and Hassani, A (2019) The role of knowledge sharing in supply chain success. Journal of Engineering, Design and Technology , 17(06), 1222–49.

Srivastava, A, Srivastava, D K and Misra, A K (2019) Spatial variability modeling and reliability analysis of flexible pavement through mechanistic–empirical model. Journal of Engineering, Design and Technology , 17(06), 1129–45.

Tengan, C, Aigbavboa, C O, Guribie, F and Annor-Asubonteng, J (2019) Analysis of the outcome features of effective monitoring and evaluation in construction project delivery. Journal of Engineering, Design and Technology , 17(06), 1192–201.

Titirla, M and Aretoulis, G (2019) Neural network models for actual duration of Greek highway projects. Journal of Engineering, Design and Technology , 17(06), 1323–39.

  • Type: Journal Article
  • Keywords: Neural networks; Attribute selection; Highway construction; Predicting models; Project actual duration; WEKA;
  • ISBN/ISSN: 1726-0531
  • URL: https://doi.org/10.1108/JEDT-01-2019-0027
  • Abstract:
    This paper aims to examine selected similar Greek highway projects to create artificial neural network-based models to predict their actual construction duration based on data available at the bidding stage. Design/methodology/approach Relevant literature review is presented that highlights similar research approaches. Thirty-seven highway projects, constructed in Greece, with similar type of available data, were examined. Considering each project’s characteristics and the actual construction duration, correlation analysis is implemented, with the aid of SPSS. Correlation analysis identified the most significant project variables toward predicting actual duration. Furthermore, the WEKA application, through its attribute selection function, highlighted the most important subset of variables. The selected variables through correlation analysis and/or WEKA and appropriate combinations of these are used as input neurons for a neural network. Fast Artificial Neural Network (FANN) Tool is used to construct neural network models in an effort to predict projects’ actual duration. Findings Variables that significantly correlate with actual time at completion include initial cost, initial duration, length, lanes, technical projects, bridges, tunnels, geotechnical projects, embankment, landfill, land requirement (expropriation) and tender offer. Neural networks’ models succeeded in predicting actual completion time with significant accuracy. The optimum neural network model produced a mean squared error with a value of 6.96E-06 and was based on initial cost, initial duration, length, lanes, technical projects, tender offer, embankment, existence of bridges, geotechnical projects and landfills. Research limitations/implications The sample size is limited to 37 projects. These are extensive highway projects with similar work packages, constructed in Greece. Practical implications The proposed models could early in the planning stage predict the actual project duration. Originality/value The originality of the current study focuses both on the methodology applied (combination of Correlation Analysis, WEKA, FannTool) and on the resulting models and their potential application for future projects.