Digital Transformation of Construction Quality Management: Extraction Dataset
Description
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.
Files
Steps to reproduce
The dataset was compiled through a PRISMA-guided systematic review of peer-reviewed literature on digital and Quality 4.0 technologies applied to construction quality management. Searches were conducted in Scopus and Web of Science for the period 2006–2026, using combined terms for technologies (artificial intelligence, machine learning, computer vision, robotics and automation, UAV/drones, IoT and sensing, blockchain, augmented/virtual reality, BIM/digital twin, cloud/platform monitoring, and text mining/NLP) and quality (construction AND quality management/control/assurance/inspection, QA/QC, defects, non-conformance, traceability, auditability, compliance). Records were screened using predefined criteria. Studies were included if they reported empirical applications of digital technologies in construction with a clear link to quality management activities (inspection, control, assurance, or management). Non-English publications, abstracts without accessible full texts, non-empirical papers without application evidence (unless explicitly retained as contextual evidence and coded accordingly), and studies outside the built environment were excluded. Data from eligible studies were extracted into a structured template covering bibliographic information, country/region as reported, research method and sample, technology investigated, application level, any referenced frameworks, reported benefits and drawbacks, reception/implementation context, KPI and metric reporting, and accuracy/performance measures where available. Terminology was normalised by mapping application levels to the four-tier quality hierarchy (inspection, control, assurance, and management) and by grouping technologies into consistent families (e.g., AI/ML, IoT/sensing, robotics/UAV-enabled inspection, blockchain-based traceability/e-inspection, AR/VR, BIM/digital twin, cloud/platform, and text mining/NLP). KPI reporting was coded into algorithm-level metrics, process/outcome indicators, and linked reporting (algorithm metrics connected to at least one process/outcome indicator), with unclear or insufficiently specified KPI reporting coded separately. The final cleaned entries were consolidated into an Excel-based extraction dataset and accompanied by a complete Mixed Methods Appraisal Tool (MMAT) assessment for all included studies (Q1–Q5 plus overall ratings), providing a reproducible evidence base on digital technology applications for construction quality management.
Institutions
- Nottingham Trent UniversityNottinghamshire, Nottingham