Liang, Y and Xu, Z (2025) Intelligent inspection of appearance quality for precast concrete components based on improved YOLO model and multi-source data. Engineering, Construction and Architectural Management, 32(3), pp. 1691-1714. ISSN 0969-9988
Abstract
Purpose: Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means. Design/methodology/approach: The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network. Findings: The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues. Originality/value: The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | multi-source data fusion; object detection; precast concrete component; quality inspection |
| Index terms: | object detection, dataset, construction project, integration, construction process, methodology, accuracy, construction site, point cloud, quality inspection, construction phase, prefabricated building, quality management, precast concrete, hardware, quality issue, data fusion, monitoring, inspection, platform, efficiency, conversion |
| Subjects: | computer hardware, control systems, work location, building materials, data science, project delivery, performance management, professional development, data management, organizational analysis, manufacturing engineering, research methods, computer vision, production management, digital design, building construction, quality assurance |
| Topics: | Quality Management, Engineering Principles, Project Management, Organizational Design, Site Management, Digital Applications, Construction Technology, Information Management, Construction Materials, Research Practice |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here