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Dunlop, P G and Smith, S D (2002) Simulation analysis of the UK concrete delivery and placement process: a tool for planners. In: Greenwood, D (Ed.), Proceedings 18th Annual ARCOM Conference, 2-4 September 2002, Northumbria, UK. Association of Researchers in Construction Management, Vol. 2, 781–90.
- Type: Conference Proceedings
- Keywords: concrete operations; cyclic construction processes; Monte Carlo simulation; probability distribution functions
- ISBN/ISSN: 0 9534161 7 8
- URL: http://www.arcom.ac.uk/-docs/proceedings/ar2002-781-790_Dunlop_and_Smith.pdf
- Abstract:
The UK construction industry continually strives to improve previous performance and increase financial efficiency in terms of labour, plant and materials. Construction projects are very rarely made up of one activity or process and in most cases projects involve a multitude of task specific and intricate processes. In order to achieve desired goals, such as better productivity rates, it is fundamental that an improvement is witnessed in the performance of each and every process. One such process is the delivery and placement of ready-mixed concrete. Evident in the vast majority of major civil engineering projects in the UK, concrete is a very valuable material, and one that requires meticulous planning in order to successfully get it to site and into the required formwork. The successful completion of many construction projects, on time and within budget, can be decided by the effectiveness of the concreting phase. So, therefore, why in the UK are very few tools available for the efficient planning and completion of concrete operations? It is proposed that by using simulation to model the concrete process it will be possible to plan and manage productivity rates of concrete operations. The factors that influence the concrete system can be summarized as: truck mixer interarrival time, truck mixer position time, concrete load pump time and truck mixer volume. This paper will look at a model of the above factors, based on over 300 ‘real’ concrete pours. The random variability of these factors can be incorporated into a model by using the gamma probability distribution for the interarrival time, the exponential probability function for the position time and finally the inverse Gaussian probability distribution for the pump time. The development of the model and the simulation runs carried out will be described. The main results of the experimental process will aid planners in optimizing the concrete process by maximizing productivity and minimizing cost.