Estimating Labor Productivity Using Probability Inference Neural Network

Abstract

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