Classifying Construction Contractors Using Unsupervised-Learning Neural Networks

Abstract

Contractor prequalification involves the screening of contractors by a project owner to determine their competence to complete the project on time, within budget, and to expected quality standards. The process of prequalification involves a large number of contractors, each being represented by many attributes. A neural network model was applied to aid in the prequalification process by classifying contractors into groups based on similarity in performance using the financial ratios of liquidity, activity, profitability, and leverage. Contractors are represented in this model by patterns in four-dimensional space. Patterns of similar performance tend to form clusters intercepting regions of low pattern density in between. A neuron with weights is used as a classifier to set a decision boundary between clusters. The method basically iterates the neuron weights to move the decision boundary to a place of low pattern density. Then, the statistical hypothesis testing of the mean difference of two independent samples was used to validate the classification of the parent class to the two child classes considering the four ratios separately. The method was used hierarchically to classify a group of 245 contractors into classes of small numbers. Finally, the inferred procedure of classification proves that the neural network model classified the four-dimension pattern representing contractors efficiently.