Investigation, modelling and planning of stochastic concrete placing operations

The concrete delivery and pumping process is a stochastic system. If analysed deterministically there is the danger that the negative effects of the random distribution of events are not taken into account, leading to poor estimates of production and cost. By representing the system as a random process the construction engineer can firstly achieve improved estimates of the overall productivity and thus schedule deliveries better, and secondly, determine the effect of non-anticipated events such as excessive delivery or pour times. This research is centred on studies of actual construction projects, which are used to study cyclic processes in general, and concreting placing operations in particular. The dataset used was gathered from over 345 concrete pours, and represents one of the largest of its type. This dataset was used as a basis for the modelling of concreting operations. These models were developed and analysed using a number of techniques, notably lean construction principles, multiple regression analysis and Monte Carlo simulation. The outcome of this research is a better understanding of how cyclic construction processes are managed and planned at a grass roots level. Firstly, by applying lean construction theories to concrete operations in the UK it was found that many are being carried out with an unacceptable amount of wasteful activities. By understanding lean construction principles some headway can be made to ensure that in future this waste is not only understood but also eliminated. Secondly, a multiple regression analysis was carried out with excellent results. Not only was it possible to identify those factors that most effect the performance of concrete placing, but also a model was developed that allows planners and engineers alike to accurately predict key responses such as pour duration and expected productivity. Finally, a simulation analysis was carried out which highlighted how the overall process reacted to changes in key times such as pump, interarrival and waiting time on site. By knowing how the process behaves and reacts to change it is possible to calculate the optimum operating conditions.

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