Search

With the steadily growing of global transportation market, the traffic load has increased dramatically over the past decades, which may develop into a risk source for existing bridges. The simultaneous presence of heavy trucks that are random in nature governs the serviceability limit for large bridges. This study investigated probabilistic traffic load effects on large bridges under actual heavy traffic load. Initially, critical stochastic traffic loading scenarios were simulated based on millions of traffic monitoring data in a highway bridge in China. A methodology of extrapolating maximum traffic load effects was presented based on the level-crossing theory. The effectiveness of the proposed method was demonstrated by probabilistic deflection investigation of a suspension bridge. Influence of traffic density variation and overloading control on the maximum deflection was investigated as recommendations for designers and managers. The numerical results show that the congested traffic mostly governs the critical traffic load effects on large bridges. Traffic growth results in higher maximum deformations and probabilities of failure of the bridge in its lifetime. Since the critical loading scenario contains multi-types of overloaded trucks, an effective overloading control measure has a remarkable influence on the lifetime maximum deflection. The stochastic traffic model and corresponding computational framework is expected to be developed to more types of bridges. [ABSTRACT FROM AUTHOR]

In the global wave of digitization, digital economic agglomeration, as an emerging model, profoundly impacts the economy, environment, and society. Countries worldwide are formulating strategies and policies to promote the development of digital economic agglomeration, yet they also face challenges of widening digital divide and environmental sustainability. Existing research primarily focuses on the positive effects of the digital economy, with limited assessment of the dual effects of digital economic agglomeration on sustainable development. This study utilizes panel data from 282 Chinese cities between 2011 and 2021, employing a two-tier stochastic frontier model. It reexamines the dual impacts and intrinsic mechanisms of digital economic agglomeration, attempting to capture regional and temporal variations in the dual effects to address this research gap. The study shows that: (1) The positive effect of digital economy agglomeration is much more than the negative effect, resulting in a positive net effect that shows an overall increasing trend with significant regional disparities. (2) Digital economic agglomeration has a significant negative spatial spillover effect, promoting local inclusive green growth while inhibiting inclusive green growth in neighboring cities. (3) Regarding the mediating mechanisms, industrial structure, technological innovation and resource allocation efficiency have positive indirect effects on inclusive green growth, while environmental regulation intensity has a negative indirect effect, and it has a nonlinear effect under the threshold constraint of the mediating mechanisms. This study provides policy insights for promoting inclusive green growth, emphasizing the need to consider regional differences in resource distribution, ecological environment, and social demands. It advocates for the organic integration of the digital economy across different regions, reducing polarization effects, and enhancing diffusion effects., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)