Addressing the limitations of existing landslide displacement prediction models in capturing the dynamic characteristics of data changes, this study introduces a novel dynamic displacement prediction model for landslides. The proposed method combines Variational Mode Decomposition (VMD) with Sparrow Search Optimization (SSO) and Long Short-Term Memory (LSTM) techniques to formulate a comprehensive VMD–SSO–LSTM model. Through the application of VMD, the method dissects cumulative displacement and rainfall data, thereby extracting distinct components such as trend, periodicity, and fluctuation components for displacement, as well as low-frequency and high-frequency components for rainfall. Furthermore, leveraging Gray Correlational Analysis, the interrelationships between the periodic component of displacement and the low-frequency component of rainfall, as well as the fluctuation component of displacement and the high-frequency component of rainfall, are established. Building upon this foundation, the SSO–LSTM model dynamically predicts the interrelated displacement components, synthesizing the predicted values of each component to generate real-time dynamic forecasts. Simulation results underscore the effectiveness of the proposed VMD–SSO–LSTM model, indicating root-mean-square error (RMSE) and mean absolute percentage error (MAPE) values of 1.2329 mm and 0.1624%, respectively, along with a goodness of fit (R²) of 0.9969. In comparison to both back propagation (BP) prediction model and LSTM prediction model, the VMD–SSO–LSTM model exhibits heightened predictive accuracy.

To promote the development of green and low-carbon expressway construction, an evaluation index system and associated evaluation method for the construction of green and low-carbon expressways suitable for multiple bridges and tunnels is proposed. The evaluation index system was created from three layers: the goal layer, the criterion layer, and the indicator layer. The criterion layer includes 4 first-level indices and the indicator layer includes 18 second-level indices. The weight of each index in the criterion layer and indicator layer is determined using the improved analytic hierarchy process (AHP) method, and green and low-carbon expressway construction is then graded using the gray fuzzy comprehensive evaluation method combined quantitative indices with qualitative indices. The method with the selected indices is then verified in a case study on the Huangling-Yan’an Expressway, with the evaluation grade being “Excellent” and the evaluation value being 9.1255. The proposed evaluation method can provide theoretical and practical guidance for effective evaluation for green and low-carbon expressway construction.

Using real-time production data of concrete to predict its 28-day compressive strength has great significance for improving the engineering structure quality and overcoming the shortage of the traditional tests long period of concrete compressive strength. The current research has the shortcomings such as insufficient prediction accuracy, inadequate matching between data characteristics and model characteristics, and redundant input parameter information. This paper proposes a BP neural network prediction model optimized by Spearman and PCA. The model first uses Spearman method to reduce the number of the input variables by eliminating variables that have a low correlation with the compressive strength and then uses PCA to eliminate the correlation between material-related variables. Following this, the new uncorrelated input variables optimized by Spearman and PCA are input to the BP neural network-based model to predict the compressive strength of concrete. The results showed that it yielded the mean absolute percentage error (MAPE) of 2.78 and the root mean-squared error (RMSE) of 1.66MPa, far lower than the error of 4.82 and 2.92MPa obtained by the nonoptimized BP neural network, respectively. The proposed model fully exploits real-time monitoring data from the concrete mixing station, and its results are close to those of traditional tests. It has great practical significance to guide the concrete production and construction, shorten the production cycle, and reduce the project cost.

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.

The Yellow River basin spans nine provinces and autonomous regions and plays an important role in China's economic and social development and ecological security. However few studies have integrated the concept of carbon neutrality into research to evaluate the carbon-neutral development level. This paper calculates the comprehensive evaluation value of the provincial carbon-neutral capacities comprehensive evaluation index in the Yellow River basin through the driving-force-pressure-state-impact-response (DFPSIR) index system and the global entropy method based on provinces data between 2008 to 2019. The final results indicated that from 2008 to 2019, the carbon-neutral capacities of the provinces in the Yellow River basin were in a state of rapid development and had achieved a grade leap. However, seven provinces had carbon-neutral capacity levels at the Grade III standard in 2019, thereby leaving scope for substantial improvement. Through the above research, we identified the changes in the trend and driving mechanisms of the carbon-neutral capacity of the Yellow River basin and provide a theoretical reference value for a comprehensive realization of carbon neutrality in China in 2060.

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.