To reduce the frequency of bridge-erecting machine accidents and ensure construction safety, it is necessary to correctly understand the factors affecting the construction safety of bridge-erecting machines and their relative importance. Through a literature review and a field investigation, 15 factors were identified affecting the construction safety of bridge-erecting machines. Then, questionnaire survey items are established. Structural equation modeling (SEM) was used to build the relationship model among the factors. The model included four aspects—human, mechanical equipment, and environmental safety and management—to discuss the influence of each factor on the safety of bridge-erecting machine construction. Additionally, the relationships between the influencing factors is studied and analyzed. The results show that human, mechanical equipment, and environmental safety and management capacity have significant normal influences on the construction safety of bridge-erecting machines. Human safety has the greatest influence on the construction safety of bridge-erecting machines, followed by management capacity, mechanical equipment safety, and environmental safety. In addition, the four influencing factors have a certain degree of mutual influence.

Using construction and demolition waste (CDW) as road subgrade filling materials is an excellent way to solve the disparity between increased demand and road construction aggregate shortages. However, a key quality control problem is predicting the subgrade settlement, primarily because the CDW subgrade settlement prediction methods are not yet mature. To go some way in overcoming this problem, in this paper we developed a three-point hyperbolic combination model to predict CDW subgrade settlement, in which three appropriate points for the measured settlement curve were selected in the prediction samples to improve the hyperbolic model. Then, common prediction models—namely, the hyperbolic model, the three-point model, and the Hushino model—were compared with the proposed combination model to assess its viability. Finally, the three-point hyperbolic combination prediction accuracy was analyzed for different start points t₀ and time intervals Δt. The analyses found that the proposed model was in good agreement with the measured data, had a high correlation coefficient, and had only small errors. However, the time interval $\Delta t$ needed to be greater than 80 days and the start point t₀ needed to be selected at the beginning of the stable post-filling period, that is, t₀ = 90–100 days. The application parameters were also determined to provide a reference for the large-scale application and settlement predictions of CDW subgrade.

In 2013, China imported 282 million tons of crude oil with an external dependence of 58.1%, surpassing the USA as the world’s largest net oil importer. An import source diversification strategy has been adopted by China to ensure oil supply security and to prevent oil supply disruption. However, the strategy is restricted by the imbalance of oil reserves. What is the reasonable and clear objective of the diversification strategy under an imbalanced environment? How do we assess the natural imbalance? This paper analyzes the oil import diversification of China and the USA, as well as the oil production of oil export countries by the oil import source diversification index (OISDI). Our results are as follows: the distribution of oil import sources for China tends to coincide with the oil production distribution of oil exporters in the world. Compared with the USA, China has more diversified import sources. The Chinese government paid much attention to import sources in the past. In the future, China will adjust the distributions of regional sources rather than focus on the number of sources to further optimize the structure of imported regions in the course of implementing the import source diversification strategy.

Using a dynamic model from 2005 to 2015, this study assessed China’s energy supply security in the provinces along “the Belt and Road”. Energy supply security was conceptualized across four dimensions: availability, affordability, acceptability, and energy technology and efficiency. A dynamic model was used to calculate the dynamic weights for each indicator and to examine the variation laws. It was found that the acceptability dimension indicator weights had rapidly increased, indicating that energy supply security had been widely accepted. Smooth weights were found for the availability dimension indicators, indicating that the resources needed to protect basic energy supplies were available. A synthetical energy supply security evaluation index was also analyzed for each “Belt and Road” province using a “quadratic weighting” method, which showed that the index was different in the same provinces in different years and different for different provinces in the same year. For 14 “Belt and Road” provinces, the overall energy supply security was found to have a “V” shape and a general dynamic upward trend, with the trends in the northwest and southeast provinces and along the whole “Belt and Road” found to be consistent. The energy supply security differences between these provinces were analyzed deeply in this paper; it will help us to understand the strengths and weaknesses of energy supply security levels in these provinces. The result will make it easier to cooperate with other provinces or countries along the “Belt and Road” in energy supply.

Firstly, a genetic algorithm (GA) and simulated annealing (SA) optimized fuzzy c‐means clustering algorithm (FCM) was proposed in this paper, which was developed to allow for a clustering analysis of the massive concrete cube specimen compression test data. Then, using an optimized error correction time series estimation method based on the wavelet neural network (WNN), a concrete cube specimen compressive strength test data estimation model was constructed. Taking the results of cluster analysis as data samples, the short‐term accurate estimation of concrete quality was carried out. It was found that the mean absolute percentage error, e₁, and the root mean square error, e₂, for the samples were 6.03385% and 3.3682KN, indicating that the proposed method had higher estimation accuracy and was suitable for concrete compressive test data short‐term quality estimations.

In the process of promoting economic development, carbon peaks and carbon neutrality have gradually received more attention. The question of how to steadily and rapidly improve the carbon neutrality capacity of each province and excavate the key factors hindering the carbon neutrality capacity has become particularly important. In this study, the DPSIR (driving, pressure, state, influence, response) framework was used to construct an index system of the provincial carbon neutrality capacity of the middle and lower Yellow River Basin, which included 37 indices. Based on the entropy weight matter-element model, the time evolution, regional differences, and restriction indicators of the carbon neutrality capacity of four provinces in the middle and lower Yellow River Basin from 2008 to 2021 were analyzed. The results showed that the carbon neutrality capacity of the four provinces in the middle and lower Yellow River Basin has significantly improved over time and has gradually reached a grade of “good”. Differences in carbon neutrality capacity among the provinces still exist but are gradually shrinking. the per capita car ownership, urban population density, and other factors have hindered the improvement of the carbon neutrality capacity in each province, but with the reduction in restriction indicators and increase in positive indicators such as urbanization rate, forest grass coverage, and others, the overall development direction tended to be promising.