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Gledson, B J (2016) Hybrid project delivery processes observed in constructor BIM innovation adoption. Construction Innovation, 16(02), 229-46.

Maghrebi, M, Shamsoddini, A and Waller, S T (2016) Fusion-based learning approach for predicting concrete pouring productivity based on construction and supply parameters. Construction Innovation, 16(02), 185-202.

  • Type: Journal Article
  • Keywords: construction scheduling,artificial intelligence,construction management,construction estimating,computer systems,construction engineering management
  • ISBN/ISSN:
  • URL: https://doi.org/10.1108/CI-05-2015-0025
  • Abstract:
    Purpose The purpose of this paper is to predict the concrete pouring production rate by considering both construction and supply parameters, and by using a more stable learning method. Design/methodology/approach Unlike similar approaches, this paper considers not only construction site parameters, but also supply chain parameters. Machine learner fusion-regression (MLF-R) is used to predict the production rate of concrete pouring tasks. Findings MLF-R is used on a field database including 2,600 deliveries to 507 different locations. The proposed data set and the results are compared with ANN-Gaussian, ANN-Sigmoid and Adaboost.R2 (ANN-Gaussian). The results show better performance of MLF-R obtaining the least root mean square error (RMSE) compared with other methods. Moreover, the RMSEs derived from the predictions by MLF-R in some trials had the least standard deviation, indicating the stability of this approach among similar used approaches. Practical implications The size of the database used in this study is much larger than the size of databases used in previous studies. It helps authors draw their conclusions more confidently and introduce more generalised models that can be used in the ready-mixed concrete industry. Originality/value Introducing a more stable learning method for predicting the concrete pouring production rate helps not only construction parameters, but also traffic and supply chain parameters.

Shokri-Ghasabeh, M and Chileshe, N (2016) Critical factors influencing the bid/no bid decision in the Australian construction industry. Construction Innovation, 16(02), 127-57.

Tsehayae, A A and Fayek, A R (2016) System model for analysing construction labour productivity. Construction Innovation, 16(02), 203-28.

Walker, D H T (2016) Reflecting on 10 years of focus on innovation, organisational learning and knowledge management literature in a construction project management context. Construction Innovation, 16(02), 114-26.

Walker, D H T and Rahmani, F (2016) Delivering a water treatment plant project using a collaborative project procurement approach. Construction Innovation, 16(02), 158-84.