Digital Transformation of Construction Quality Management: Extraction Dataset

This repository provides the full extraction dataset and quality appraisal outputs underpinning a PRISMA-guided systematic review of digital and Quality 4.0 technologies in construction quality management. The final dataset synthesises 51 included studies published between 2006 and 2026 and captures how technologies are being applied across the quality hierarchy (Inspection, Control, Assurance, and Management), with particular attention to adoption and governance conditions.
For each included study, the dataset records bibliographic metadata (title, authors, year, and country/region as reported), research method and sample characteristics, and the primary “Application Level” within quality management. It documents the “Topic/technology investigated” and associated enabling infrastructures (e.g., AI/ML and computer vision, IoT/sensing, robotics and UAV-enabled inspection, blockchain-based traceability and e-inspection, BIM/digital twin and cloud/platform monitoring, and text mining/NLP of defect records). In addition, it captures reported benefits and drawbacks, reception/implementation context, referenced frameworks, and all KPI/metric reporting (algorithm-level metrics, process/outcome indicators, and whether tool metrics are linked to at least one process/outcome measure), including any reported accuracy/performance values where available.
To support interpretation, the repository also includes Supplementary Table 2 reporting study quality appraisal using the Mixed Methods Appraisal Tool (MMAT), including Q1–Q5 criterion coding and overall ratings for all 51 studies. The dataset is intended to be reusable for secondary synthesis, benchmarking, and routine-centred evaluation of digital QA/QC workflows. It enables readers to trace where evidence is concentrated (inspection/control), where assurance/management implementations remain thinner, how KPI practices vary in clarity and comparability, and where governance-oriented technologies (e.g., traceability/auditability) are being operationalised.

本数据集仓库提供完整的提取数据集与质量评价成果，支撑一项遵循PRISMA指南（PRISMA）的建筑质量管理领域数字技术与质量4.0（Quality 4.0）技术系统综述。本最终数据集整合了2006年至2026年间发表的51项纳入研究，梳理了各类技术在质量管理层级（检测、控制、保证与管理）中的应用场景，并重点关注其采纳情况与治理条件。针对每一项纳入研究，本数据集记录了文献元数据（含标题、作者、发表年份及报告中提及的国家/地区信息）、研究方法与样本特征，以及质量管理中的核心“应用层级”。数据集涵盖了“研究主题/技术”及相关支撑基础设施，例如人工智能/机器学习（AI/ML）与计算机视觉、物联网/传感技术、机器人技术与无人机（UAV）辅助检测、基于区块链的可追溯性与电子检测、建筑信息模型/数字孪生（BIM/digital twin）与云/平台监控、缺陷记录的文本挖掘/自然语言处理（NLP）等。此外，数据集还收录了报告中提及的技术利弊、采纳/实施背景、参考框架，以及所有关键绩效指标（KPI）/度量指标记录（含算法级指标、过程/结果指标，以及工具指标是否与至少一项过程/结果度量相关联），包括所有可获取的准确率/性能数值。为便于研究解读，本仓库还包含补充表2，其采用混合方法评价工具（MMAT）对所有51项研究的质量进行评价，涵盖Q1-Q5标准编码与整体评级。本数据集旨在可复用，可用于数字质量保证/质量控制（QA/QC）工作流的二次综合、基准测试与常规导向评估。通过本数据集，读者可追溯证据集中的领域（检测/控制环节）、质量保证与管理实施相对薄弱的环节、关键绩效指标实践在清晰度与可比性上的差异，以及以治理为导向的技术（如可追溯性/可审计性技术）的落地应用情况。