The construction industry is embracing transformation through the integration of digitization, artificial intelligence (AI), and immersive technologies. On a construction site, continuous assessment is vital for ensuring both the reliability of assets and safety of workers. Scaffolding is a key structural support asset that requires regular inspections for detection and identification of alterations from the design rules that could compromise integrity and stability. At present, such inspections to identify deviations are primarily visual and conducted by the site managers or accredited personnel. However, visual inspection is time-intensive and susceptible to human errors, which can lead to unsafe conditions. This study explores the use of AI and digital technologies to automate and enhance scaffolding inspections process to contribute toward safety improvement. A cloud-based AI platform is developed to process and analyze 3D point-cloud data of scaffolding structures to detect modifications through comparisons as well as evaluate the certified reference scan with a recent scan. The proposed workflow incorporates prognostics and health management concepts with continuous monitoring to identify structural modifications and further assist with decision-making. The results indicate that the proposed approach can limit reliance on manual visual inspections. By enabling automated monitoring of scaffolding, the proposed approach reduces the time and effort required for inspection process, while enhancing the safety on a construction site.

In iron mining the processing phase broadly consists of sorting, concentrating, and pelletizing of the iron ore, this is to increase the iron content in the final product. In pelletizing, the filtering stage which controls the moisture content in the iron cake plays a crucial role. A Rotatory Vacuum Drum Filter (RVDF) is one of the mining equipment for removing excessive moisture by separating solid iron cake from slurry. A supporting wire which holds the cloth mounted on the frame of the RVDF is one of the critical components. During operation, recursive compression and stretching due to variation in pressure may lead to wire failure. This failure significantly impacts the integrity and efficiency of filter cloth that affects the filtration performance. If the wire failure is not detected promptly, it can lead to prolonged maintenance time, substantial maintenance cost and unplanned downtime, consequently affecting system availability. This work demonstrates health monitoring of filtering system in mining, designed to alert the operators about the emerging failure, to take appropriate maintenance action and minimize further damage, and unplanned downtime.

This paper introduces a computer-vision based monitoring approach that leverages image data of the drum filter during operation. The proposed approach identifies wire-induced degradation pattern on the filter cloth. Extracted video frames from the drum filter are processed to isolate the region of interest. Using Hough transform horizontal sections of the drum are detected followed by a sliding window analysis to evaluate the variations in pixel intensity. For normal surface, the average intensity variations remain low, typically ranging from 5 to 10. However, it spikes up to around 40 when irregular patterns are detected. The focus of this work is on detection and diagnostics, a transition towards prognostics is envisioned by incorporating pressure sensor data. Integrating multi-modal data may enhance the capability of predicting failure and improve the system availability.