ArticleSensors (Basel, Switzerland)2025
Implementation of Integrated Smart Construction Monitoring System Based on Point Cloud Data and IoT Technique.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Integration of AR and deep learning-based image classification using CNN for construction project monitoring.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
This study presents an integrated smart construction monitoring system that combines point cloud data (PCD) from a 3D laser scanner with real-time IoT sensors and ultra-wideband (UWB) indoor positioning technology to enhance construction site safety and quality management. The system addresses the limitations of traditional BIM-based methods by leveraging high-precision PCD that accurately reflects actual site conditions. Field validation was conducted over 17 days at a residential construction site, focusing on two floors during concrete pouring. The concrete strength prediction model, based on the ASTM C1074 maturity method, achieved prediction accuracy within 1-2 MPa of measured values (e.g., predicted: 26.2 MPa vs. actual: 25.3 MPa at 14 days). The UWB-based worker localization system demonstrated a maximum positioning error of 1.44 m with 1 s update intervals, enabling real-time tracking of worker movements. Static accuracy tests showed localization errors of 0.80-0.94 m under clear line-of-sight and 1.14-1.26 m under partial non-line-of-sight. The integrated platform successfully combined PCD visualization with real-time sensor data, allowing construction managers to monitor concrete curing progress and worker safety simultaneously.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.