To explore the effects of typical uncertainty factors on the seismic performance of small-and medium-span bridges during design, construction, and service, a four-span continuous small box girder bridge was taken as the engineering background, and a nonlinear dynamic model was built based on OpenSees. The influence mechanisms of seismic uncertainty, modeling parameter uncertainty, and capacity uncertainty on the seismic demand of key components were analyzed. On this basis, combined with the theory of fragility analysis, the linkage effect of the above uncertainty factors in the establishment of component fragility curves was explored, and then the degree of influence of each type of uncertainty factors on the analysis of structural seismic performance was quantified. The results show that the differences in the seismic hysteresis curves of the components are mainly caused by the uncertainties of the modeling parameters, while the differences in the peak seismic response are mainly caused by the combined uncertainties of ground shaking and modeling parameters. The modeling parameter uncertainty and component seismic capacity uncertainty can lead to an increase in the probability of structural damage, which consequently makes some components unable to meet the required damage state. The consideration of the uncertainty factor increases the probability of failure of the bridge system susceptibility curve, and the effect of uncertainty increases as the degree of damage deepens, biasing the analysis results by more than 30 percent.

The rapid increase in highway tunnel mileage also signifies a gradual rise in operational costs, and the pressing issue of high electricity operation costs for highway tunnels urgently needs to be addressed. To reduce the electricity operation costs of highway tunnels and achieve energy conservation and emission reduction, it is essential to consider optimizing the energy structure under the “dual carbon” background. This involves exploring the application prospects of renewable energy supply systems in highway tunnels and establishing a wind-solar-storage complementary power generation system. Taking a 498 m long highway tunnel load as an example, an optimization based on an improved PSO (particle swarm optimization) algorithm was conducted. The goal was to minimize the full lifecycle costs of equipment construction and maintenance, with constraints on the power shortage load rate and storage capacity, specifically for the wind-solar-storage complementary system. The results show as follows. The improved discrete adaptive particle swarm algorithm obtained the optimal solution after the 20th iteration, while the standard particle swarm algorithm reached the optimal solution near the 300th iteration, indicating a stronger optimization capability of the discrete adaptive particle swarm algorithm. Compared to the standard particle swarm algorithm, the improved discrete adaptive particle swarm algorithm reduced the investment and usage costs of the wind, solar, and storage equipment by 578 300 yuan, approximately 17.37%.Compared to the annual electricity cost of the example tunnel, which is 515 000 yuan, the full lifecycle cost of the wind-solar-storage complementary system is 3 328 800 yuan. The investment cost will be recouped within 7 years, and the investment return rate of this wind-solar complementary system is 10.47%. Over the 20-year lifespan of the equipment, the wind-solar-storage complementary power generation system will save 6 971 200 yuan in electricity expenses.

To analyze the hydrodynamic characteristics of light beacons in wind-wave environments and propose an optimization scheme based on stress concentration points, the VOF (volume of fluid) method was used to calculate the pressure distribution on the surface of the light beacon under wave and wind conditions and the resulting deformations. The results indicate that stress concentration occurs at the base of the central support of the light beacon, with the maximum displacement at the top of the light beacon. Two optimization schemes were investigated based on these findings. In the slanted support scheme, stress concentration shifted to the top of the support, reducing by 32.2% compared to before optimization. The maximum deformation was at the base in the middle, a 40.1% reduction compared to before optimization. In the end-strengthened scheme, stress concentrated at the bottom, with a reduction of 0.08% compared to before optimization, while the maximum deformation was at the top of the light beacon, which was reduced by 23.1% compared to before optimization. It is evident that the light beacon structure with slanted supports can effectively reduce stress intensity and disperse the impact of wind and waves on other parts of the light beacon, representing a better structural optimization scheme.

In response to the problem of insufficient analysis of the risk impact on system components and inadequate classification management in civil aircraft maintenance, which leads to sudden failures during component operation, a comprehensive risk impact factor was introduced to accurately evaluate the importance and potential hazards of civil aircraft system components, and a component preventive maintenance strategy considering the comprehensive risk impact of multiple factors was established. A chance maintenance decision model for civil aircraft systems was established with the objective of optimizing the maintenance cost of components, taking into account the comprehensive risk impact of multiple factors on components. The model comprehensively considers the time correlation between system components and the cost generated by the comprehensive risk impact of multiple factors on components. Example verification shows that compared to preventive maintenance decisions that do not consider the impact of component risks, the maintenance decision proposed in this article reduces the total maintenance cost of civil aircraft systems by about 20.20%, and the reduction rate of preventive maintenance and replacement frequency is about 41.23%.

The problem of scheduling and collaborative decision-making in airport metroplex or terminal areas can be significantly approached by obtaining accurate ETA (estimated time of arrival). Traditional methods are short of the ability to fine-tune the arrival metering nodes. The accurate quantitative estimation of large-volume and complex flight traffic situations is hard to achieved especially under the influence of highly dynamic environments in a medium to long term. An ETA correction method based on error feedback was proposed. Based on the aircraft performance parameters, an aircraft kinematics model was firstly constructed combined with route planning and meteorological data, which was used to give a preliminary ETA prediction through the calculation of 4D trajectory then. After that an error sequence would be constructed by comparing the difference between ATA (actual time of arrival) and the predicted results, with it the next error could be predicted using the error feedback model and the results obtained previously would be corrected. Finally, the arrival flights to a large hub airport were taken as examples to conduct a simulation, in which the rate of error within ±5 minutes that predicted 30 minutes in advance was chosen as the evaluation criteria. The simulation results show that the accuracy of ETA prediction can be improved by more than 25% in bad weather after corrected by the proposed method when compared with traditional means.

As industry and agriculture continue to evolve, the threat of organic contamination in groundwater to human health is gaining attention. Following the health risk assessment method recommended by the USEPA, and considering local natural geographical and hydrogeological conditions, organic pollutants in Zhuzhou City’s groundwater were assessed. Using a health risk assessment model, non-carcinogenic and carcinogenic risks from three exposure pathways-drinking water, skin, and inhalation-were evaluated. Results show that key organic pollutants in Zhuzhou City’s groundwater include dichloromethane, 1,2-dichloroethylene, trichloroethylene, tetrachloroethylene, p,p'-DDE, and p,p'-DDD. Non-carcinogenic risks from these pollutants are below specified limits, but every sampling points exceed carcinogenic risk limits, with ZZS119 surpassing the maximum acceptable carcinogenic risk. Inhalation is the primary exposure pathway, contributing to approximately 81% of total risk. Tetrachloroethylene poses the highest carcinogenic risk at 81.08%, followed by trichloroethylene at 11.65%. Urban discharges, volatile organic compounds from chemical plants, insecticide use in forestry, and domestic wastewater discharge have severely contaminated Zhuzhou City’s groundwater, resulting in unacceptable carcinogenic risks for local residents.

Land ecological security is the core of sustainable use of land resources, and land use changes caused by human activities change the structure and function of ecosystems, which have a serious impact on the regional ecological security system. In order to explore the changes in ecological security in Chongqing in recent years and in 2030, Chongqing was taken as the research object and PLUS (patch-level land use simulation) model was employed to simulate land use changes under the scenarios of natural development, ecological priority and development priority in 2030. Based on the ecological perspective, an ecological security evaluation index system was constructed, and the mutation model was combined to quantitatively evaluate the level of land ecological security. The results show that the spatial distribution of land use types in Chongqing is quite different, the cultivated land area decreased by 3 995.14 km², and the construction land area increased by 1 147.36 km², realizing rapid urban development. At the same time, 64.53%, 67.31% and 55.97% were relatively safe or above under the three scenarios. The spatial pattern of ecological security in Chongqing is opposite to the spatial pattern of population density and GDP, and consistent with the spatial pattern of natural data such as vegetation cover and slope. Through the ecological assessment of land use changes in previous years and under different scenarios, it provides a basis for high-quality coordinated development of ecology and economy.

Particle profile control and blockage is recognized as an important method for enhancing oil recovery. The migration and deposition characteristics of particles in porous media are understood to facilitate the optimization of particle preparation, thereby improving compatibility with reservoir pore throats and blocking efficiency. Factors such as particle concentration, particle size, porous medium structure, particle size ratio, and fluid parameters within the medium were reviewed for their effects on migration and deposition. Research results from various simulation methods, including simplified geometry, mesoscopic simulation, lattice Boltzmann method-discrete element method (LB-DEM) and computational fluid dynamics-discrete element method(CFD-DEM) were summarized. It is indicated that the critical value of the particle size ratio influences the deposition location and blockage degree in porous media. Different particle sizes are subjected to significant differences in forces, with larger particles being notably affected by hydrodynamics, gravity, and fluid flow rates. The fluid flow model within porous media is not yet fully unified. However, the Brinkman-Forchheimer-Darcy model is noted for its strong applicability. The CFD-DEM method, approached from a microscopic perspective, has validated the flow-solid coupling of migration and deposition within the medium, providing a basis for profile control schemes in heterogeneous reservoirs.

Under the guidance of the “14th Five-Year Plan” and the “Dual Carbon” goals, construction materials face significant challenges, particularly as the adaptability and accuracy of traditional concrete performance prediction models are questioned. Recently, machine learning (ML) has demonstrated high accuracy and efficiency in predicting concrete performance. The research progress of ML in this field was systematically reviewed, focusing on its applications in mechanical properties, mix design, and durability, while identifying its limitations and proposing improvement strategies. CiteSpace software was used to analyze the current state of ML research in construction engineering, examining publication volume, research hotspots, and trends. This analysis offers valuable reference for future researchers, aiding in the effective application of ML technology to drive innovation in construction materials and support environmental sustainability goals.

In rock image recognition, achieving rapid and accurate identification of rocks is crucial for the digitalization of rocks. Among the challenges faced in intelligent rock recognition is the issue of image blurring caused by environmental factors such as lighting and humidity. In light of this, a novel deep learning approach (MobileNetV3-small-RegNetX) was proposed for rock image recognition, which is suitable for scenarios with limited resources such as mobile devices. Building upon the RegNet network, transfer learning methods, combining the advantages of the MobileNetV3 residual structure with squeeze-and-excitation (SE)modules was employed to effectively optimize feature extraction and network structure, leading to a significant improvement in detection speed. To validate the accuracy of this approach, comparative experiments were conducted between the new model and current mainstream lightweight models (DenseNet and ShuffleNet). The results demonstrate that the new model proposed exhibits high precision (82.15%) and fast processing (0.06 GFLOPs). Additionally, the model demonstrates good adaptability to environmental factors such as lighting and humidity-induced image blurring.