A cost estimation recommendation system for improved contingency management in construction projects

Management of contingency reserves involves identifying and prioritizing potential high-cost impact events, serving as a cushion for absorbing the financial risks of projects. Machine learning (ML) models exist for estimating rework costs; however, they cannot recommend related activities that influence contingency costs. This research proposes a novel approach that integrates a construction contingency network, advanced node2vec algorithms, and cosine similarity measures to identify construction activities with similar contingency costs, facilitating the management and planning of rework costs. The proposed system offers tailored recommendations and aids in project management by reducing guesswork using the design science research (DSR) methodology that combines advanced ML techniques with practical construction management strategies to provide a robust tool for navigating the complexities of rework costs. The configured recommendation system achieved an 82% accuracy in its suggestions for critical construction activities with a high-cost impact, along with a 4% loss, demonstrating good generalization. Novelty of this research lies in its first-time development of a recommendation model capable of generating dynamic recommendations of the activities that impact the contingency budget, supporting the existing cost forecasting model.