Digital technology for quality management in construction:A review and future research directions

Significant developments in digital technologies can potentially provide managers and engineers with the ability to improve the quality of the construction industry. Acknowledging the current and future use of digital technologies in construction quality management (CQM), we address the following research question: What developments in digital technologies can be used to improve quality in the construction industry? In addressing this research question, a systematic review approach is used to examine the studies that have been used for the management of quality in the construction industry. This review indicates that there is a need for digital technology-based quality management to be: (1) enhance defect management for concealed work, (2) enhance pre-construction defects prevention as well as post-completion product function testing, and (3) research on construction compliance inspection as a direction. We suggest that future research focus on quality culture development, advanced data analytics, and behavioral quality assessment

A High Precision Quality Inspection System for Steel Bars Based on Machine Vision

Steel bars play an important role in modern construction projects and their quality enormously affects the safety of buildings. It is urgent to detect whether steel bars meet the specifications or not. However, the existing manual detection methods are costly, slow and offer poor precision. In order to solve these problems, a high precision quality inspection system for steel bars based on machine vision is developed. We propose two algorithms: the sub-pixel boundary location method (SPBLM) and fast stitch method (FSM). A total of five sensors, including a CMOS, a level sensor, a proximity switch, a voltage sensor, and a current sensor have been used to detect the device conditions and capture image or video. The device could capture abundant and high-definition images and video taken by a uniform and stable smartphone at the construction site. Then data could be processed in real-time on a smartphone. Furthermore, the detection results, including steel bar diameter, spacing, and quantity would be given by a practical APP. The system has a rather high accuracy (as low as 0.04 mm (absolute error) and 0.002% (relative error) of calculating diameter and spacing; zero error in counting numbers of steel bars) when doing inspection tasks, and three parameters can be detected at the same time. None of these features are available in existing systems and the device and method can be widely used to steel bar quality inspection at the construction site