ENHANCED TIME-DEPENDENT EVOLUTIONARY FUZZY SUPPORT VECTOR MACHINES INFERENCE MODEL FOR CASH FLOW PREDICTION AND ESTIMATE AT COMPLETION

Abstract

This study conducts a mechanism enhancing the time series data treatment of the time-dependent evolutionary fuzzy support vector machine inference model (EFSIMT). The enhanced model, EFSIMET, was developed particularly to treat construction management problems that contain time series data. EFSIMET is an artificial intelligent hybrid system in which fuzzy logic (FL) deal with vagueness and approximate reasoning; support vector machine (SVM) acts as supervise learning tool; and fast messy genetic algorithm (fmGA) works to optimize FL and SVMs parameters simultaneously. Moreover, to capture the time series data characteristics, the inference model develops fmGA-based searching mechanism to seek suitable weight values to weight the training data points. This random-based searching mechanism has capacity to address the complex and dynamic nature of time series data; thus, it could improve the model's performance significantly. Nowadays, construction managementis facing complex and difficult problems due to the increasing uncertainties during project implementation. Therefore, the second objective of this study is proposed for the application of EFSIMET to treat two typical problems in construction: forecasting cash flow and estimate at completion. Through performance's comparison with previous works, the effectiveness and reliability of EFSIMET are proven. Hence, this model may be used as an intelligent decision support tool to assist the decision-making process to solve the construction management's difficulties.