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Benchmarking energy performance of existing residential buildings’ envelopes remains a challenge due to the complex physical and non-physical interacting factors of buildings. Regression analysis with sufficient data samples can be attractive for benchmarking application due to its capability in neutralizing the effects of noise variables. However, multicollinearity effects among explanatory variables often lead to unreliable regression models, especially in cases of high-dimensional variables. Principal Component Regression can transform co-linear variables via principal component analysis to orthogonal components and simultaneously has the neutralization function of linear regression analysis of high dimensional dataset. A new benchmarking method is developed using multivariate linear regression analysis with principal component analysis to address the multicollinearity risk with high dimensional dataset. The method was applied to datasets of a real project. The results indicate that Principal Component Regression is able to address multicollinearity risk, through using fewer orthogonal principal components that are linear combinations of original variables. The benchmarking outcome using this method is validated through infrared thermography validation. The benchmarking result is superior to that of the traditional statistical rating method using average energy consumption of buildings.

Non-English speaking workers constitute a disproportionately high number of workers involved in postdisaster reconstruction. Additionally, the rate of fatality among these workers is higher than the industry average. Research shows this population is more prone to unsafe behaviors in the working environment, conceivably because many of these workers are sent into the field prior to any formalized training. Recent studies show that the native culture of construction workers can impact risk-taking behavior. While numerous researchers have attempted to develop training materials for Hispanic workers, the number of studies that consider the impact of native culture on safety behavior is minimal. To answer this emerging knowledge gap, this paper develops a framework that will help to discern the influence of native culture, as well as other socioeconomic characteristics, on the effectiveness of safety trainings for non-English speaking workers. The formulation of this framework will pave the way for an enhanced understanding of the impact native culture plays on unsafe behaviors within a diverse workforce. Foreseeably, this understanding will play a significant role in developing culturally sensitive training materials in the future.

Hospitals are among the most energy intensive buildings in industrialized countries, using more than two-thirds of total energy consumption to maintain climate control and indoor air quality (IAQ). Yet, hospital acquired infections (HAIs) afflict approximately 8% of the in-patient population and claim more than 115,000 lives each year in Europe and the U.S. alone.

Recent studies have shown that higher air change rates may have the unintended consequence of creating turbulent airflows that entrain high concentrations of infectious particles within the breathing zone, and possibly, breakdown pressure relationships necessary to contain the spread of infectious particles to other clinical spaces.