Bespoke precast productivity estimation with neural network model

Bespoke precast-concrete components are custom made for construction projects. The variety of product designs results in requiring different manufacturing time. To estimate the productivity of four precast routines, this study identifies twenty influential factors based on the difficulty in product designs and manpower. These influential factors are such as nominal height, length, and width, tiling area, the number of curves, the number of embedded parts, concrete strength, slump, reinforcement weight, and the number of different bar shapes, etc. Productivity estimation models are formulated using two techniques: neural network (NN) and multivariable linear regression (MLR). The estimation performance from both techniques is measured with three statistical values, namely absolute percentage error, mean square error, and correlation coefficient. The experimentation results show that MLR gives insignificantly better performance than NN. However, standardised residuals from the NN are distributed in the narrower range than the ones from the MLR.