Advancing Data-Driven Scheduling Frameworks for Infrastructure Modernization: A Comprehensive Review of United States Construction Efficiency Models

Infrastructure modernization in the United States continues to face recurring schedule overruns that undermine national competitiveness and reduce public value. In recent years data driven scheduling frameworks have emerged as potential solutions that leverage Building Information Modeling real-time sensor data, historical performance archives, and advanced analytics to enhance schedule accuracy and resilience. This literature review examines peer-reviewed studies from the last decade that investigate data-centered scheduling models, including predictive algorithms, hybrid machine learning decision systems, out-of-order scheduling, and Building Information Modeling (BIM)- enabled workflow integration. The review analyzes methodological advances, evaluates empirical evidence from United States-based studies, and identifies persistent constraints such as limited interoperability, weak data governance, and small-scale validation. Findings demonstrate that hybrid analytics and sensor-enhanced scheduling approaches consistently outperform traditional critical path methods in delay prediction of resource alignment and progress monitoring. The review also highlights substantial research gaps related to transparency procurement effects and cross-agency data integration. Recommendations for future research include establishing United States-focused longitudinal datasets, improving interpretability and standardization, and conducting multi-site field evaluations in public infrastructure programs.