To improve sustainable development, increasingly more attention has been paid to the evaluation of the resilience to waterlogging disasters. This paper proposed a projection pursuit model (PPM) improved by quantum particle swarm optimization (QPSO) for the evaluation of the resilience of subway station projects to waterlogging disasters. In view of the lack of research results related to the evaluation of the resilience of subway station projects to waterlogging disasters, 16 secondary indicators that affected the ability of subway station projects to recover from waterlogging disasters were identified from defense, recovery, and adaptability, for the first time. A PPM improved by QPSO was then proposed to effectively deal with the high-dimensional data about the resilience of subway station projects to waterlogging disasters. The QPSO was used to solve the best projection vector of the PPM, and interpolation algorithm was used to construct the mathematical model of evaluation. Finally, four station projects of Chengdu Metro Line 11 in China were selected for a case study analysis. The case study revealed that, among the secondary indicators, the emergency plan of construction order, the exercise frequency of emergency plans, and relief supplies had the greatest weights. The recovery was found to be the most important in the primary indicators. The values of the resilience of Lushan Avenue Station, Miaoeryan Station, Shenyang Road Station, and Tianfu CBD North Station to waterlogging disasters were found to be 2, 1.6571, 2.8318, and 3 respectively. This resilience ranking was consistent with the actual disaster situation in the flood season of 2019. In addition, the case study results showed that QPSO had the advantages of fewer parameter settings and a faster convergence speed as compared with PSO and the genetic algorithm.

Environmental risks have a significant impact on the sustainability of subway station construction projects. This paper proposes an environmental risk assessment model based on the intuitionistic fuzzy analytic hierarchy process (IFAHP) and set pair analysis (SPA) to deal with the ambiguity and uncertainty in the assessment. An index system for environmental risk assessment is established based on a literature review and the rough set method. Subsequently, the IFAHP is used to calculate the indicator weights to describe the certainty, uncertainty, and hesitation degree of expert decisions in the weighting calculation by means of affiliation, nonaffiliation, and hesitation. Finally, SPA, which can deal with the randomness, uncertainty, and ambiguity of the indicators, is used to assess environmental risk. A case study of two typical stations (Lushan Avenue Station and Huilong Road West Station) of Metro Line 11 in Chengdu, China, is conducted. The case study results are consistent with field surveys. The evaluation results of the proposed model are more objective and reasonable than those of the traditional analytic hierarchy process, the entropy weight method, fuzzy comprehensive evaluation, grey correlation analysis, and technique for order of preference by similarity to an ideal solution (TOPSIS). The research results prove the scientific validity and superiority of the proposed model.

The scientific and effective prediction of the water consumption of construction engineering is of great significance to the management of construction costs. To address the large water consumption and high uncertainty of water demand in project construction, a prediction model based on the back propagation (BP) neural network improved by particle swarm optimization (PSO) was proposed in the present work. To reduce the complexity of redundant input variables, this model determined the main influencing factors of water demand by grey relational analysis. The BP neural network optimized by PSO was used to obtain the predicted value of the output interval, which effectively solved the shortcomings of the BP neural network model, including its slow convergence speed and easy to fall into local optimum problems. In addition, the water consumption interval data of the Taiyangchen Project located in Xinyang, Henan Province, China, were simulated. According to the results of the case study, there were four main factors that affected the construction water consumption of the Taiyangchen Project, namely, the intraday amount of pouring concrete, the intraday weather, the number of workers, and the intraday amount of wood used. The predicted data were basically consistent with the actual data, the relative error was less than 5%, and the average error was only 2.66%. However, the errors of the BP neural network model, the BP neural network improved by genetic algorithm, and the pluralistic return were larger. Three conventional error analysis tools in machine learning (the coefficient of determination, the root mean squared error, and the mean absolute error) also highlight the feasibility and advancement of the proposed method.

Targeted at emergency plans for rainstorm and waterlogging disasters in subway station projects, this work proposes a group decision-making method that uses linguistic intuitionistic fuzzy sets, structural entropy weights, and TOPSIS. An evaluation index system of emergency plans was constructed based on four aspects, namely a scientific basis, completeness, operability, and flexibility. A linguistic interval intuitionistic fuzzy set approach was then used to qualitatively present the decision-makers' understanding of, attitudes about, and preferences for emergency plans. The uncertainty was comprehensively and intuitively represented by the dimensions of the degrees of membership and non-membership. The structural entropy weight method was applied and improved to fully reflect the influences of experts with different characteristics on the index weights. Finally, the TOPSIS method, with a background context of linguistic interval intuitionistic fuzzy sets, was applied. The calculation results of benchmark and verification case highlight the rationality and scientificity of the method proposed in this paper. The emergency decisions regarding waterlogging in 2018 for the Huilong Road West Station Project of Chengdu Metro Line 11 in China were selected as a case study. The case study demonstrates that operability is the most critical of the four primary indicators, and that flexible response to changes in the emergency response level is the most important of the secondary indicators. The uncertainty analysis of data revealed that with the increase of uncertainty, the difference between each scheme and the ideal solution decreased. Compared with the classical TOPSIS method, the new model proposed in this paper is robust and effective, and can be used for similar projects in the future.

In order to effectively solve the problems of low prediction accuracy and calculation efficiency of existing methods for estimating economic loss in a subway station engineering project due to rainstorm flooding, a new intelligent prediction model is developed using the sparrow search algorithm (SSA), the least-squares support vector machine (LSSVM) and the mean impact value (MIV) method. First, in this study, 11 input variables are determined from the disaster loss rate and asset value, and a complete method is provided for acquiring and processing data of all variables. Then, the SSA method, with strong optimization ability, fast convergence and few parameters, is used to optimize the kernel function and the penalty factor parameters of the LSSVM. Finally, the MIV is used to identify the important input variables, so as to reduce the predicted input variables and achieve higher calculation accuracy. In addition, 45 station projects in China were selected for empirical analysis. The empirical results revealed that the linear correlation between the 11 input variables and output variables was weak, which demonstrated the necessity of adopting nonlinear analysis methods such as the LSSVM. Compared with other forecasting methods, such as the multiple regression analysis, the backpropagation neural network (BPNN), the BPNN optimized by the particle swarm optimization, the BPNN optimized by the SSA, the LSSVM, the LSSVM optimized by the genetic algorithm, the PSO-LSSVM and the LSSVM optimized by the Grey Wolf Optimizer, the model proposed in this paper had higher accuracy and stability and was effectively used for forecasting economic loss in subway station engineering projects due to rainstorms.

As a result of global climate change and urbanization, waterlogging disasters have occurred frequently around the world, and deep foundation pit projects with lower terrain suffer even more. This study puts forward a method for the waterlogging risk assessment of deep foundation pit projects via the combination of a projection pursuit model, particle swarm optimization, and an interpolation algorithm. First, through a comprehensive analysis of the water circulation process in waterlogging and the characteristics of deep foundation pit projects, a risk index system with 11 indicators is identified and constructed. Then, a projection pursuit model optimized by particle swarm optimization is leveraged to determine the weights of the indicators and the best projection values of evaluation objects, and the mathematical function between the best projection values and the risk levels is constructed by an interpolation algorithm. Finally, three deep foundation pit projects of the Chengdu Metro Line 11 in China are selected as case studies. The results demonstrate that the frequency of storms, intensity of rainfall, preparation of emergency rescue plans, and proportion of older workers have the greatest impacts on waterlogging risk in deep foundation pits. The risk ranking of the case studies is found to be consistent with the actual situations, which proves the objectivity and effectiveness of the proposed method.