The detection of crack development in a masonry wall forms an important study for investigating the earthquake resistance capability of the masonry structures. Traditionally, inspecting the structure and documenting the findings were done manually. The procedures are time-consuming, and the results are sometimes inaccurate. Therefore, the digital image correlation (DIC) technique is developed to identify the strain and crack variations. This technique is non-destructive for inspecting the whole displacement and strain field. Tests on two masonry wall samples were performed to verify the performance of the digital image correlation method. The phenomena of micro cracks, strain concentration situation and nonuniform deformation distribution which could not have been observed preciously by manual inspection are successfully identified using DIC. The crack formation tendencies on masonry wall can be observed at an earlier stage by this proposed method. These results show a great application potential of the DIC technique for various situations such as inspecting shrinkage-induced cracks in fresh concrete, masonry and reinforced concrete structures, and safety of bridges.
In the implementation of active or semi-active control systems, it is necessary to process the measured signals because they are not perfect in reality. At present, the current energy-dissipating method for controlling semi-active dampers is flawed because of some restrictions on processing and measuring the signals. Thus, a detection methodology of signal control is proposed in this research based on the direction of structural motion; a velocity estimating calculator is developed by using the least-square polynomial regression. Comparison of the analytical results and experimental data confirms that the proposed calculator is effective in predicting when to switch the moving direction of a semi-active damper. It can detect when the direction of the structure motion reverses as well as when to compensate the poor influence on the performance of a semi-active damper caused by the delayed response. Additionally, the noise of displacement signal will not affect the phase difference of predictive signals.
Indoor air quality has become a critical issue because people spend most of their time in the indoor environment. The factors that influence indoor air quality are very important to environmental sanitation and air quality improvement. This study focuses on monitoring air quality, colony counts, and bacteria species of the indoor air of a nursing care institution. The regular colony counts in two different wards range from 55 to 600 cfu m(-3) Regression analysis results indicate that the bacterial colony counts have close correlation with relative humidity or carbon dioxide (CO2) but not with carbon monoxide (CO) or ozone (O3). Real-time PCR was used to quantify the bacterial pathogens of nosocomial infection, including Acinetobacter baumannii, Citrobacter freundii, Escherichia coli, Klebsiella pneumoniae, and methicillin-sensitive Staphylococcus aureus. The most abundant bacteria species in the air of the nursing care institution is E. coli.
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