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Akanmu, A and Anumba, C J (2015) Cyber-physical systems integration of building information models and the physical construction. Engineering, Construction and Architectural Management, 22(05), 516-35.

Albogamy, A and Dawood, N (2015) Development of a client-based risk management methodology for the early design stage of construction processes: Applied to the KSA. Engineering, Construction and Architectural Management, 22(05), 493-515.

Das, M, Cheng, J C P and Law, K H (2015) An ontology-based web service framework for construction supply chain collaboration and management. Engineering, Construction and Architectural Management, 22(05), 551-72.

Khosrowshahi, F (2015) Enhanced project brief: Structured approach to client-designer interface. Engineering, Construction and Architectural Management, 22(05), 474-92.

Li, H, Chan, G, Skitmore, M and Huang, T (2015) A 4D automatic simulation tool for construction resource planning: A case study. Engineering, Construction and Architectural Management, 22(05), 536-50.

Maghrebi, M, Sammut, C and Waller, S T (2015) Feasibility study of automatically performing the concrete delivery dispatching through machine learning techniques. Engineering, Construction and Architectural Management, 22(05), 573-90.

  • Type: Journal Article
  • Keywords: Australia; Information technology; Computer-aided design; Automation; Knowledge management; Modelling
  • ISBN/ISSN:
  • URL: https://doi.org/10.1108/ECAM-06-2014-0081
  • Abstract:
    Purpose - The purpose of this paper is to study the implementation of machine learning (ML) techniques in order to automatically measure the feasibility of performing ready mixed concrete (RMC) dispatching jobs. Design/methodology/approach - Six ML techniques were selected and tested on data that was extracted from a developed simulation model and answered by a human expert. Findings - The results show that the performance of most of selected algorithms were the same and achieved an accuracy of around 80 per cent in terms of accuracy for the examined cases. Practical implications - This approach can be applied in practice to match experts’ decisions. Originality/value - In this paper the feasibility of handling complex concrete delivery problems by ML techniques is studied. Currently, most of the concrete mixing process is done by machines. However, RMC dispatching still relies on human resources to complete many tasks. In this paper the authors are addressing to reconstruct experts’ decisions as only practical solution.