Tao Yan

The electricity-saving behavior of construction workers is helpful in reducing construction costs, protecting the ecological environment, and preventing global climate change. However, there is insufficient research on the electricity-saving behavioral mechanisms of tunnel-construction workers, and their behavior is influenced by their surrounding people including supervisors and co-workers because they are nested in various construction sites and isolated from other acquaintances. This study aims to develop a hierarchical linear model that explores the interrelationships between tunnel-construction workers’ electricity-saving behavior and four influencing factors theoretically and empirically. An organizational-level factor, electricity-saving climate, and three individual-level factors, including attitude, perceived behavioral control, and moral norms, are considered, and 1567 tunnel-construction workers from 41 construction sites mainly located in the southwest of China participated in this study. A six-step procedure for statistical analyses is adopted to test eight hypotheses using questionnaire survey data. The results supported all the hypotheses within the multi-level model and showed that the organizational-level factor played a leading role in predicting workers’ electricity-saving intentions with three individual-level factors positively associated with workers’ electricity-saving intentions. Further, the organizational electricity-saving climate also indirectly affects workers’ electricity-saving intentions through three mediators (individual-level factors), and electricity-saving intention is positively associated with electricity-saving behavior. Consequently, cultivating an electricity-saving climate within an organization is of great benefit to electricity conservation and environmental protection, and several recommendations are provided to improve the practical operability of results. The findings enable a better understanding of electricity-saving behavioral mechanisms and promote a low-carbon lifestyle among tunnel-construction workers.

The rational use of natural wind in extra-long tunnels for feedforward operation ventilation control can dramatically reduce tunnel operation costs. However, traditional tunnel natural wind calculation theory lacks a prediction function. This paper proposes a three-stage tunnel natural wind prediction method relying on the Yanglin Tunnel in Yunnan, China based on the massive meteorological parameters provided by the open-source national meteorological stations around the tunnel, which make up for the partial deficiency of the meteorological parameters of the tunnel portal. The multi-layer perceptron model (MLP) was used to predict the real-time meteorological parameters of the tunnel portal using the data from four national meteorological stations. The nonlinear autoregressive network model (NARX) was used to predict the meteorological parameters of the tunnel portal in the next period based on the predicted and measured real-time data. The natural wind speed in the tunnel was obtained by a theoretical calculation method using the predicted meteorological parameters. The final tunnel natural wind prediction results are in good agreement with the field measured data, which indicates that the research results of this paper can play a guiding role in the feedforward regulation of tunnel operation fans.