A neural network based system for predicting earthmoving production

Abstract

An artificial neural network based system (NN earth) is developed for construction practitioners as a simple tool for predicting earthmoving operations, which are modelled by back propagation neural networks with four expected parameters and seven affecting factors. These networks are then trained using the data patterns obtained from simulation because there are insufficient data available from industrial sources. The trained network is then incorporated as the computation engine of NN earth. To engender confidence in the results of neural computation, a validation function is implemented in NN earth to allow the user to apply the engine to historic cases prior to applying it to a new project. An equipment database is also implemented in NN earth to provide default information, such as internal cost rate, fuel cost, and operator's cost. User interfaces are developed to facilitate inputting project information and manipulating the system. The major functions and use of NN earth are illustrated in a sample application. In practice, NN earth can assist the user either in selecting a crew to minimize the unit cost of a project or in predicting the performance of a given crew.

Keywords:

• Artificial Neural Networks
• Back Propagation
• Earthmoving
• Prediction
• Simulation
• Site Operations