A Construction-Phase Digital Twin Framework for Quality Assurance and Decision Support in Civil Infrastructure Projects

TL;DR

This paper introduces a digital twin framework that enhances real-time quality assurance and decision-making during construction by integrating inspection, sensing, and predictive data at the element level.

Contribution

It presents a novel construction-phase digital twin system that supports early, data-driven quality assessments and decision-making during active construction projects.

Findings

Supports early detection of quality issues

Enables proactive decision-making before standard tests

Integrates multiple data streams for comprehensive quality tracking

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.