Dynamic prediction of project success using evolutionary support vector machine inference model

Abstract

The purpose of construction management is to successfully accomplish projects, which requires a continuous monitoring and control procedure. To dynamically predict project success, this research proposes an Evolutionary Support Vector Machine Inference Model (ESIM). ESIM is developed based on a hybrid approach that fuses support vector machine (SVM) and fast messy genetic algorithm (fmGA). SVM is primarily concerned with learning and curve fitting; and fmGA with optimization. Furthermore, the model integrates the process of continuous assessment of project performance (CAPP) to dynamically select factors that influence project success. CAPP was developed to identify continuous variables that have the ability for predicting project outcome. Training and test patterns are collected from CAPP database that contains 46 construction projects. These projects are real data collected by Russell from the 16 representative Construction Industry Institute (CII) member companies. K-means clustering was employed to conduct an unsupervised clustering to extract similar cases for comparison. Results show that ESIM can successfully predict the project success.