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The annual production of medical waste from healthcare facilities in Pakistan is around 250,000 tons. An effective waste management system is essential for disposing of hazardous medical waste, and incineration is considered the most effective and accessible technology. Disposal of medical waste ash in landfills without proper treatment could lead to groundwater contamination due to leachate. This research paper aims to evaluate the feasibility of using hospital waste ash obtained from the National Cleaner Production Center (NCPC) in Rawalpindi as a partial replacement for cement. The primary variable in this study was the amount of hospital waste ash (0%, 3%, 7%, and 10% by weight of cement), while the amount of cementitious material, water-to-cement ratio, and fine and coarse aggregate content were kept constant. A total of 36 cubes were cast, with nine cubes for each replacement level for curing periods of 7, 14, and 28 days. The slump value and density of fresh concrete decreased with the increase in the proportion of hospital waste ash in the mix. The compressive strength of mixes with 3% hospital waste ash was higher than that of the control mix. The best results (20.13 MPa) were obtained from the 3% mix after 28 days of curing, while the result obtained with the 7% mix was nearly equal to that of the control mix. [ABSTRACT FROM AUTHOR]

This paper introduces a multistep damage identification process that is both straightforward and useful for identifying damage in buildings with regular plan geometries. The algorithm proposed in this study combines the utilization of a multi-damage sensitivity feature and MATLAB programming, providing a comprehensive approach for the structural health monitoring (SHM) of different structures through vibration analysis. The system utilizes accelerometers attached to the structure to capture data, which is then subjected to a classical statistical subspace-based damage detection test. This test focuses on monitoring changes in the data by analyzing modal parameters and statistically comparing them to the structure's baseline behavior. By detecting deviations from the expected behavior, the algorithm identifies potential damage in the structure. Additionally, the algorithm includes a step to localize damage at the story level, relying on the jerk energy of acceleration. To demonstrate its effectiveness, the algorithm was applied to a steel shear frame model in laboratory tests. The model utilized in this study comprised a total height of 900 mm and incorporated three lumped masses. The investigation encompassed a range of scenarios involving both single and multiple damages, and the algorithm proposed in this research demonstrated the successful detection of the induced damages. The results indicate that the proposed system is an effective solution for monitoring building structure condition and detecting damage. [ABSTRACT FROM AUTHOR]