Neural networks for estimating the ceramic productivity of walls

Abstract EN

Productivity estimating of a construction operation is an essential tool for the successful completion of the construction process.

Productivity of a construction operation is defined as output of the system per unit of time.

In this research Artificial Neural Networks approaches are presented.

The main reason for using neural networks for construction productivity estimation is the requirement of performing complex mapping of environment and management factors to productivity.

A generic description of the artificial neural networks model is provided, followed by summarized factors that affect ceramic labor productivity, then neural-network model are developed for Estimating ceramic walls productivity, the input data for the model based on experienced superintendents employed by a leading construction general contractor, test results show that the ANN approach can produce a sufficiently accurate estimate with a limited data-collection effort, and thus has the potential to provide an efficient tool for construction productivity estimation.