Abstract:

To achieve low carbon emission and reduction in the design and construction of Chengdu metro, the machine learning algorithm is employed to predict the carbon emissions in the construction phase of the Chengdu metro based on a case study of 6 stations and 7 sections of the Chengdu metro line 18. The greenhouse gas emissions from building material production, transportation, and onsite construction in metro stations and shield sections are then calculated using a life cycle assessment method, and a metro carbon emission prediction model based on deep extreme learning machine (DELM) optimized by whale optimization algorithm (WOA) is established. The prediction results of DELM algorithms optimized by the winddriven optimizer, grey wolf optimizer, particle swarm optimizer, artificial bee colony, multiverse optimizer, and atom search optimizer are compared with those of nonoptimized back propagation neural network, kernel extreme learning machine, and DELM algorithms. The research results reveal the following: (1) The correlation consistency of WOADELM algorithm prediction results is 0.757, slightly higher than other algorithm models. (2) The sensitivity analysis of the leading input indicators of metro carbon emission is conducted using the WOADELM 〖HJ〗algorithm model, obtaining the key influencing factors of station carbon emission prediction, i.e., station length and rail surface buried depth, and the relative change rates of corresponding indicators of carbon emission are 30.1% and 23.1%, respectively.

Key words: metro, life cycle assessment, carbon emission, deep extreme learning machine, prediction model