This paper aims to develop an artificial neural network (ANN) to predict the energy consumption and cost of cross laminated timber (CLT) office buildings in severe cold regions during the early stage of architectural design. Eleven variables were selected as input variables including building form and construction variables, and the values of input variables were determined by local building standards and surveys. ANNs were trained by the simulation data and Latin hypercube sampling (LHS) method was used to select training datasets for the ANN training. The best ANN was obtained by analyzing the output variables and the number of hidden layer neurons. The results showed that the ANN with multiple outputs presented better prediction performance than the ANN with single output. Moreover, the number of hidden layer neurons in ANN should be greater than five and preferably 10, and the best mean square error (MSE) value was 1.957 × 103. In addition, it was found that the time of predicting building energy consumption and cost by ANN was 80% shorter than that of traditional building energy consumption simulation and cost calculation method
Timber building has gained more and more attention worldwide due to it being a generic renewable material and having low environmental impact. It is widely accepted that the use of timber may be able to reduce the embodied energy of a building. However, the development of timber buildings in China is not as rapid as in some other countries. This may be because of the limitations of building regulations and technological development. Several new policies have been or are being implemented in China in order to encourage the use of timber in building construction and this could lead to a revolutionary change in the building industry in China. This paper is the first one to examine the feasibility of using Cross Laminated Timber (CLT) as an alternative solution to concrete by means of a cradle-to-grave life-cycle assessment in China. A seven-storey reference concrete building in Xi’an was selected as a case study in comparison with a redesigned CLT building. Two cities in China, in cold and severe cold regions (Xi’an and Harbin), were selected for this research. The assessment includes three different stages of the life span of a building: materialisation, operation, and end-of-life. The inventory data used in the materialisation stage was mostly local, in order to ensure that the assessment appropriately reflects the situation in China. Energy consumption in the operation stage was obtained from simulation by commercialised software IESTM, and different scenarios for recycling of timber material in the end-of-life are discussed in this paper. The results from this paper show that using CLT to replace conventional carbon intensive material would reduce energy consumption by more than 30% and reduce CO2 emission by more than 40% in both cities. This paper supports, and has shown the potential of, CLT being used in cold regions with proper detailing to minimise environmental impact.
This paper aims to investigate the energy saving and carbon reduction performance of cross-laminated timber residential buildings in the severe cold region of China through a computational simulation approach. The authors selected Harbin as the simulation environment, designed reference residential buildings with different storeys which were constructed using reinforced concrete (RC) and cross-laminated timber (CLT) systems, then simulated the energy performance using the commercial software IESTM and finally made comparisions between the RC and CLT buildings. The results show that the estimated energy consumption and carbon emissions for CLT buildings are 9.9% and 13.2% lower than those of RC buildings in view of life-cycle assessment. This indicates that the CLT construction system has good potential for energy saving when compared to RC in the severe cold region of China. The energy efficiency of residential buildings is closely related to the height for both RC and CLT buildings. In spite of the higher cost of materials for high-rise buildings, both RC and CLT tall residential buildings have better energy efficiency than low-rise and mid-rise buildings in the severe cold region of China.