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(5), pp. 573-590. ISSN 0969-9988
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Australia; Information technology; Computer-aided design; Automation; Knowledge management; Modelling |
| Index terms: | computer aided design, methodology, human resource, information technology, knowledge management, machine learning, concrete mixing, feasibility study, accuracy, ready mixed concrete, implementation, modelling, automation, Australia |
| Subjects: | computing systems, materials science, computational design, analytical methods, artificial intelligence, contractual arrangements, design stages, professional development, automation and robotics, Geography, research methods, management |
| Topics: | Human Resources, Digital Applications, Design Practice, Information Management, Engineering Principles, Research Practice, Geographical Context, Procurement |
| Descriptive scope: | 3 PCT |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here