施工阶段数字孪生框架用于基础设施项目质量保证与决策支持
A Construction-Phase Digital Twin Framework for Quality Assurance and Decision Support in Civil Infrastructure Projects
AI总结：
本文提出一种施工阶段数字孪生框架，通过整合多种数据流实现元素级质量保证与决策支持，提升施工期间的质量管理效率。
摘要：
施工阶段的质量保证往往依赖于 inspection 记录和实验室测试结果，这些结果在工作完成后几天或几周才可用。在大型高速公路和桥梁项目中，这种延迟限制了早期干预，增加了重新工作的风险、进度影响和文档碎片化的问题。本文提出了一种施工阶段的数字孪生框架，旨在支持元素级的质量保证和基于准备状态的决策制定。该框架将 inspection 记录、材料生产和放置数据、早期年龄传感和预测强度模型与个别施工元素联系起来。通过整合这些数据流，系统代表了每个元素的演变质量状态，并在标准年龄测试结果可用之前支持结构化的释放或暂停决策。该方法不取代已建立的 inspection 和测试程序。相反，它通过提高可追溯性和使早期、数据驱动的质量评估成为可能来补充现有流程。讨论了与数据整合、合同限制和实施挑战相关的实际考虑。所提出的框架为将施工质量保证从延迟的、文档驱动的审查转向施工期间的主动、元素级决策支持提供了结构化的路径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
