Estimating labor productivity using probability inference neural network

This paper discusses the derivation of a probabilistic neural network class ification model and its application in the construction industry. The proba bility inference neural network (PINN) model is based on the same concepts as those of the learning vector quantization method combined with a probabi listic approach. The classification and prediction networks are combined in an integrated network, which required the development of a different train ing and recall algorithm. The topology and algorithm of the developed model was presented and explained in detail. Portable computer software was deve loped to implement the training, testing, and recall for PINN. The PINN was tested on real historical productivity data at a local construction compan y and compared to the classic feedforward back-propagation neural network m odel. This showed marked improvement in performance and accuracy. In additi on, the effectiveness of PINN for estimating labor production rates in the context of the application domain was validated through sensitivity analysi s.