Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks

Chien-Ho Ko

doi:10.1155/2013/729525

Abstract

Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.

Highlights

A construction project involves various work items that need to be accomplished by subcontractors, including earthwork, formwork, concrete pouring, plastering, rebar, and mechanical and electrical tasks
Project success cannot be achieved without appropriate performance on the part of the subcontractors [4]
Subcontractor performance is considered an important indicator for general contractors to select subcontractors

Summary

Introduction

A construction project involves various work items that need to be accomplished by subcontractors, including earthwork, formwork, concrete pouring, plastering, rebar, and mechanical and electrical tasks. Their research found that a subcontractor’s previous performance is the most critical criterion for selecting high-performing subcontractors at the prequalification stage and for assessing their performance at the construction stage. The Scientific World Journal assessment, placing the identified factors into a criteria matrix to aid decision making for selecting subcontractors at the bid stage. Another investigation conducted by Elazouni and Metwally [16] developed a decision support system that assigns work items to subcontractors under constraints and predicts the project’s final profit. The objective of this research is to develop Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. The paper concludes with suggestions for future research directions

Performance Prediction Practice

Evolutionary Fuzzy Neural Networks

Application

Conclusions