Due to the various dynamic conditions of construction sites, quality failures have become part and parcel of the industry. Many studies have identified the causal factors of construction site quality failures and their cost impacts. However, limited studies have been made evaluating the domino effects of these on one another and the correlation between the cost impact and frequency of each attribute. In this study, made in the context of ongoing research into related artificial intelligence (AI)-based predictive models, a total of 2,527 nonconformance reports (NCRs) collected from 59 construction projects within the scope of a previous study were analyzed using the Delphi method and logistic regression analysis. According to the Delphi results, 25 critical cost impact factors were refined and categorized into five main groups: Materials, Design, Installation, Operation, and Process. Then, five main hypotheses were developed to test each attribute's cost impact and interaction by logistic regression. The results showed that although some attributes (from the Materials and Operation groups) have a significant impact on the cost of quality if observed in a failure report individually, others may become a critical cost-impact factor when interacting with other attributes. No significant correlation was observed between the frequency and cost impact of the attributes. Finally, a holistically based quality control system that considers the domino effects of causal factors from planning to operation was proposed for construction practitioners to reduce quality failures causing cost and time overruns.