Digital infrastructure management increasingly requires a persistent connection between static asset information and the changing physical state of infrastructure. This technical report develops INFRADEX, a digital infrastructure intelligence algorithm that integrates Building Information Modelling (BIM), digital-twin states, Internet of Things (IoT) measurements, field inspection records, and operational-maintenance data within a single decision-support architecture. The algorithm combines semantic harmonisation, multimodal data fusion, condition-state inference, anomaly scoring, and maintenance-priority assessment. A comparative evaluation was performed using the benchmark results supplied for conventional asset information management (AIM), BIM-based AIM, Random Forest, XGBoost, Long Short-Term Memory (LSTM), and INFRADEX. INFRADEX attained 96.1% condition-classification accuracy, a 95.1% F1 score, 97.6% anomaly-detection performance, 98.2% digital-twin synchronization accuracy, and 2.8 s update latency. Robustness testing retained 89.6% accuracy at 20% missing data. The findings indicate that the principal contribution of INFRADEX is not BIM visualisation alone, but the continuous reconciliation of asset semantics with field evidence and predictive analytics. The framework therefore provides a technically defensible route from conventional document-centred AIM toward synchronized, condition-informed and predictive infrastructure asset management.