Usage of asphalt mixture with poor gradation will most likely lead to pavement deficiency. There is a growing need for rapid and non-destructive methods to extract pavement aggregate gradation. In this study, a deep learning-based method that utilizes point clouds data for gradation extraction was proposed. Firstly, a data enhancement algorithm along with three data format conversion methods (aligned point cloud, voxel, and depth image) were proposed to preprocess the original collected point clouds. Subsequently, different neural network models were designed for each data format to extract gradation. Finally, a multi-feature fusion network was developed, which using extraction network as the backbone and additional auxiliary information. In the case study, the MAE loss of multi-feature fusion networks with PointNet, Vox-ResNet34 and GoogLeNet-v4 as the backbone respectively achieved 0.202, 0.142 and 0.046 on the test set, which means an estimation accuracy of more than 95% for the pavement aggregate gradation.

@en

International trade and emission offshoring can reduce a country's domestic carbon dioxide emissions, helping it to reach emission reduction targets set under the prevailing territorial climate policy frameworks. We ask what is the net contribution of trade to national production-based emissions. Existing metrics (consumption-based emissions and the technology-adjusted balance of emissions embodied in trade) do not answer this question. Based on global multi-regional input-output tables and the domestic technology assumption, we calculate net emission onshoring as the difference between the emissions embodied in gross exports (onshoring) and the emissions avoided by gross imports (offshoring) for 43 countries between 2000–2014. We find that the USA offshores emissions and China onshores emissions; the aggregate trade balance explains this result while the trade composition plays a negligible role in either country. In general there is no cross-country relationship between net offshoring and per-capita income, and neither one between trade specialization in emission-intensive products and per-capita income. The developed countries’ absolute decoupling of economic growth and production-based emissions since 2000 is “genuine” in the sense that it reflects domestic economic developments and is not owed to emission offshoring.@en

The real-time energy matching between building load and PV generation is low in actual applications of photovoltaic direct-driven air conditioners (PVACs). The indoor thermal comfort temperature range (TCTR) can enhance the load flexibility of PVACs to improve the real-time zero energy probability. Therefore, a real-time zero-energy potential evaluation method with the adoption of TCTR for PVACs was proposed. The indoor temperatures are mainly determined by real-time PV generation, which minimizes the power taken from the grid and batteries. The indoor temperature, conditioned by PVACs under varying operating conditions, is predicted using machine learning models at a one-minute time resolution. The real-time energy matching performances of PVACs are evaluated by key indicators, including zero-energy probability, real-time zero-energy probability, complete overcooling rate, and complete overheating rate, for different climatic regions. With a fixed indoor setting temperature in summer, the real-time zero-energy probabilities in Shanghai, Guangzhou, and Beijing are 1.89%, 2.45%, and 2.11%, respectively. While adopting the TCTR with the similar PV capacity, the corresponding values in Shanghai, Guangzhou, and Beijing reach 53.13%, 49.48%, and 87.11%, respectively. In addition, the real-time zero-energy points frequently appear when large cooling demands are needed. Finally, an optimization of PV capacity is conducted. The optimal PV capacity was found to be at the point where seasonal PV generation is 1.3 times the dominant seasonal energy consumption. PVACs with TCTR can utilize the PV generation and load flexibility to the greatest extent and the evaluation method for PVACs is beneficial for designing a more practical and economical system.

@en

This Special Issue "Sustainable Designed Pavement Materials" has been proposed and organized as a means to present recent developments in the field of environmentally-friendly designed pavement materials. For this reason, articles included in this special issue relate to different aspects of pavement materials, from industry solid waste recycling to pavement materials recycling, from pavement materials modification to asphalt performance characterization, from pavement defect detection to pavement maintenance, and from asphalt pavement to cement concrete pavement, as highlighted in this editorial.

@en

There is a wide range of different stakeholders who may be affected by energy flexibility measures: end-users (occupants of buildings), building owners, facility managers, Energy Service Companies (ESCOs), developers, architects, contractors, and product/system suppliers. The energy flexibility is ultimately useful for aggregators, DSOs (Distribution System Operators), TSOs (Transmission System Operators), and district heating suppliers.This report establishes a comprehensive understanding of acceptance, behaviour, and motivation at different levels of involvement for the relevant stakeholders.@en

Most of previous studies optimize maintenance time window scheduling problem under a given train schedule, leading to a relatively poor quality of maintenance time window schedule, increasing the influence on traffic assignment. In order to reduce the negative effects on maintenance schedule and improve the utilization of railway resources, we consider integrating maintenance time window scheduling and train timetabling. In this way, more reasonable maintenance time window schedule can be obtained. We propose a mixed integer programming model and in particular we focus on the characteristics of the problem, including the speed limits affected by maintenance tasks on a double-track railway line. The benefits of the proposed integrated optimization model are demonstrated by numerical experiments.

@en