Optimisation of Construction Process Inspection Rates using a Learning Approach

Swapan Saha, Colin Greville, Trevor Mullins

Abstract


In the construction industry, the determination of the number of inspections/tests to be performed for a repetitive task is an important issue. The quality of a completed task depends on a number of factors including the cost of inspection, the cost of failure, the average proportion defective (error rate) and the inspection rate. To achieve a higher quality level, 100% inspection can be performed, however, this is unlikely to be cost effective. To determine the optimum inspection rate, this paper suggests a probabilistic approach incorporating the acceptance-sampling plan and minimum cost method. The limitation of various sampling plans including the attribute proportional sampling plan and the double sampling plan are outlined. An example is presented in this paper to demonstrate the calculation of the optimum number of inspections/tests per lot.

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