Combined Use of Design of Experiment and Computer Simulation for Resources Level Determination in Concrete Pouring Process

Abstract

Construction managers and planners are always involved in answering questions regarding the effects of changing the level of resources involved in construction activities on project performance. The planners strive to determine the best resource level combination that optimizes the performance measures such as productivity. In this study, a unique approach involving the combined use of a powerful Quality Engineering tool, Design of Experiment (DOE) and Simulation for determining the best combination of resources level for a real-world construction process, viz. concrete pouring process. DOE enabled the experimental plan to be designed in the form of a twice replicated, 24 full factorial designs with 5 center points. This experimental plan involved 37 experiments. Simulation has enabled the construction process investigated to be realistically modelled. Therefore, instead of performing field trials involving 37 experiments, these experiments are simulated in order to obtain the response investigated, which is productivity. A model, for predicting concrete pouring process productivity, was successfully developed and the optimum resources level was also determined.

Predicting the Duration of Concrete Operations Via Artificial Neural Network and by Focusing on Supply Chain Parameters

“…Finally, the achieved results are discussed and compared with relevant research. It is worthy to note that authors do not intend to estimate the size of crew or machinery for concrete pouring which has been discussed in literature extensively (Thomas and Sakarcan, 1994, Thomas et al, 1984, Thomas and Daily, 1983, Thomas, 1991, Sonmez and Rowings, 1998, Crawford and Vogl, 2006, Borcherding and Alarcon, 1991, Zahraee et al, 2013, Dunlop and Smith, 2004. Beyond the construction sites there are important variables that have not been carefully taken into account for predicting the productivity of concrete pouring.…”

Fusion based learning approach for predicting concrete pouring productivity based on construction and supply parameters

“…These variables will be integrated into the proposed model to predict the duration of concrete pouring more effectively. It is worth to mention that it is not intended to estimate the size of the crew or machinery for concrete pouring, matters which have been discussed in literature extensively (Borcherding and Alarcon, 1991; Crawford and Vogl, 2006; Dunlop and Smith, 2004; Sonmez and Rowings, 1998; Thomas, 1991; Thomas and Daily, 1983; Thomas et al , 1984; Thomas and Sakarcan, 1994; Zahraee et al , 2013), but rather the focus is on predicting the duration of the process, which has not been sufficiently investigated to date. In this section, the features of the available database are examined.…”

Análise da aplicabilidade do Design of Experiments no Mercado Financeiro

“…Para Zahraee, S. M. et al (2013), DoE pode ser visto como sendo composto por uma série de etapas: o planejamento, a execução do experimento e a análise dos dados experimentais coletados usando vários métodos estatísticos para tirar conclusões válidas e objetivas.…”