🤖 AI Summary
This study addresses the limitations of traditional construction quality control, which relies on lagging inspections that hinder timely intervention and often lead to rework and schedule delays. The authors propose the first component-level digital twin framework tailored for the construction phase, integrating inspection records, material production and concrete placement data, early-age sensor measurements, and strength prediction models to enable dynamic representation of quality status and readiness-driven decision support. By shifting quality assessment from passive document review to real-time, data-driven proactive management, the framework facilitates structured decisions—such as releasing or halting components—well before standard strength tests are completed. This approach significantly enhances the timeliness of interventions, traceability, and overall management efficiency in construction quality control.
📝 Abstract
Quality assurance (QA) during construction often relies on inspection records and laboratory test results that become available days or weeks after work is completed. On large highway and bridge projects, this delay limits early intervention and increases the risk of rework, schedule impacts, and fragmented documentation. This study presents a construction-phase digital twin framework designed to support element-level QA and readiness-based decision making during active construction. The framework links inspection records, material production and placement data, early-age sensing, and predictive strength models to individual construction elements. By integrating these data streams, the system represents the evolving quality state of each element and supports structured release or hold decisions before standard-age test results are available. The approach does not replace established inspection and testing procedures. Instead, it supplements existing workflows by improving traceability and enabling earlier, data-informed quality assessments. Practical considerations related to data integration, contractual constraints, and implementation challenges are also discussed. The proposed framework provides a structured pathway for transitioning construction QA from delayed, document-driven review toward proactive, element-level decision support during construction.