ABSTRACT
Overview/Challenge: AEC has been continually challenged to provide accurate and reliable approaches for predicting project success; however, consensus on what constitutes ‘success’ remains unanswered. This has stifled decision-making, making current arrangements untenable in some instances.
Design/Methodology/Approach: Sustainable Success Factors (SSF) and Sustainable Success Criteria (SSC) were aligned to a set of rules obtained from Rough Set Theory (RST) to develop a dynamic sustainable success prediction model. This model was validated through a case study.
Findings: This model was able to predict project success based on sustainability dimensions that included success factors and criteria, supported by rules and sensitivity analysis. These findings were considered particularly useful to help inform investment decisions.
Originality/Future Vision: Solution generation using artificial neural networks, AI, and ML are continually evolving. Given the high levels of subjectivity and variability often associated with AEC projects, the use and integration of dynamically driven data-centric models is strongly encouraged to improve future results.
