Construction of Green Ecological Corridor and Vertical Greening of Building Facades in Zhengzhou City

Development of a city depends largely on its transportation, and road construction reveals development skeletons of the city. With the aggravation of city pollution, construction of ecological corridor has been put on the agenda of urban ecological construction, against this background, "2 rings and 17 radial lines" of Zhengzhou City were planned. This paper analyzed the plan from the perspectives of role of urban green corridor, planning principle, characteristic building and greening of building facade, and exploration of green corridors integrating pathways, bikeways, bus harbors and green island gas station.

Deep learning for estimating energy savings of early-stage facade design decisions

The selection of high-performance building facade systems is essential to promote building energy efficiency.However,this selection is highly dependent on early-stage design decisions,which are extremely challenging considering numerous design parameters with early-stage uncertainties.This paper aims to evaluate the appli-cability of deep learning networks in estimating the energy savings of different facade alternatives in the early-stage design of buildings.The energy performance of two competing façade systems(i.e.,Ultra-High-Performance Fiber-Reinforced-Concrete and conventional panels)was estimated for different scenarios through building en-ergy simulations using EnergyPlusTM.Three deep learning networks were trained using the collected data from the simulation of fourteen buildings in fourteen different locations to estimate the heating,cooling,and total site energy savings.The accuracy of trained deep networks was compared with the accuracy of three common data-driven prediction models including,Gradient Boosting Machines,Random Forest,and Generalized Linear Regression.The results showed that the deep learning network trained to predict building total site energy savings had the highest accuracy among other models with a mean absolute error of 1.59 and a root mean square error of 3.48,followed by Gradient Boosting Machines,Random Forest,and last Generalized Linear Regression.Similarly,deep networks trained to predict building cooling and heating energy savings had the lowest mean average error of 0.20 and 1.17,respectively,compared to other predictive models.It is expected the decision support system developed based on this methodology helps architects and designers to quantify the energy savings of different facade systems in early stages of design decisions.

Sub-Atmospheric Pressure Coupled with Width Effect on Downward Flame Spread over Energy Conservation Material Polyurethane Foam

Rigid polyurethane(PUR)foam,a sustainable thermosetting building facade porous polymer material,has been widely applied in the construction industry for energy conservation.Additional knowledge of the fire safety performance of PUR foam at different altitudes and sample widths is required.Comparative lab-scale experiments were conducted in the Lhasa plateau(66.5 kPa)and the Hefei plain(99.8 kPa)in China.Flame propagation characteristics(average flame spread rate and flame height)were measured at different widths and atmospheric pressures of the test locations.Experimental results show that the dependence of dimensionless flame heights on sample width shows negative power law relationships with index of−w/5.4 to−w/5.8.Both flame height and flame spread rate were lower under low ambient pressure conditions as H f P0.26~0.33 and Vf P0.057~0.568.Flame spread rate decreased with increasing sample width in the convection regime before a critical width of 4 cm–8 cm,after which the flame spread rate increased in the radiation regime.Results of this study contribute to the science of combustion,fire safety and energy conservation,and provide a basis for fire safety protocols for historical heritage buildings in the Lhasa plateau.