Keywords: neural networks, selection optimal equipment combinations, heavy earth moving.

Previous methods have been developed to predict or select earth moving equipment fleets and associated costs of production, but these fail to provide a complete solution to the plant productivity problem. That is, when hiring or purchasing machines plant managers are not normally provided with sufficient detail to optimize the plant selection decision process. The crux of this problem is to choose an appropriate plant item from the vast range available. This paper contributes to resolving this selection process through the application of an optimization technique, based on artificial neural networks. Specifically, a decision tool for selecting the most economic solution to haul any type of soil according to productivity requirements, the hauling distance, type of loader, size of its bucket, and required number of hauling units taking in consideration the size effect of available hauling units, bucket size, job conditions, different combinations of loaders and trucks, type, condition and maintenance of hauling road, inclination of the grade, motional direction of hauling units for loaded and empty cases, operator skill, and wheel pressure condition for truck tires.

The procedure presented facilitates the selection of the best matched combinations for certain problems, which makes decision making easier and faster than that of classical procedures.

In this paper the employment of construction equipment with emphasis on heavy earth moving equipment in constructions have been studied to produce the most efficient and cost effective construction possible.

The work presented shows that optimization tool based on neural networks could be effective in selecting the best plant team composition to satisfy certain production rate for excavation and earth moving processes in construction of irrigation canals and drains. The results also demonstrate that great care needs to be taken in building the network construction that provides the highest accuracy.

The present paper paves the way for future development of practical decision tools according to the field assessment, for each company according to its fleet, and actual rental values, taking the decision in one step instead of several steps.

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