In order to minimize the influence of construction for airport operation, the construction method of closing part of time or some pavement without stopping is usually used by the airport pavement maintenance office. Therefore, it is necessary to consider the key objectives of construction management, such as quality, duration and safety, and carry out a research on construction organization and management in the maintenance and repair of airport pavement. A multi-objective optimization model was proposed for the non-stop construction organization of airport pavements, focusing on the construction period within airport pavement maintenance management. The construction process was emphasized and functional relationships among cost, quality, and construction time were established to quantify each objective scientifically. Moreover, an optimization model was developed, incorporating the construction window period as a constraint and leveraging the closure of nighttime hours to facilitate uninterrupted construction without disrupting navigation. The approach ensures efficient non-stop construction of airport pavements. In the model, a triple objective optimization strategy was employed, aiming to minimize duration and cost while maximizing quality level. To solve the model, the performance of multiple objective particle swarm optimization (MOPSO), nondominated sorting genetic algorithm-Ⅱ(NSGA-II), and NSGA-III algorithms was compared under the 3D objectives. The results demonstrate that all three algorithms show commendable performance in solving the concave optimization problem DTLZ2, with NSGA-III notably outperforming the others in tackling the three-dimensional optimization problem. The capacity of the model to further optimize construction management objectives, while fully adhering to construction constraints, was verified, which provided a decision-making framework for construction organization and management in airport pavement maintenance, catering to diverse requirements.

In order to study the effect of low temperature on mechanical properties, fatigue properties and frost resistance of cement stabilized crushed stone mixture in cold area. Using 5 ℃, 10 ℃, 15 ℃ to simulate the field low temperature curing condition, based on the mechanical properties test, three-point loading fatigue test and freeze-thaw cycle test, the differences of unconfined compressive strength, flexural tensile strength, dynamic resilience modulus, fatigue resistance and anti-freezing durability of cement stabilized gravel mixture under low temperature curing condition, 20 ℃ standard curing condition and 30 ℃ curing condition were compared. The effect of curing temperature on the attenuation law of microvoid structure of cement-stabilized crushed stone mixture during freeze-thaw cycle test was studied. The results show that, compared with the standard curing temperature (20 ℃), the mechanical properties of cement stabilized macadam mixture decrease by 20%~40% and the fatigue life decrease by 40%~50% under the condition of 5~10 ℃. Low temperature curing has a significant deterioration effect on the mechanical properties, fatigue properties and anti-freezing properties of cement stabilized macadam mixture. The greater the decrease of mechanical properties and fatigue characteristics of cement stabilized crushed stone mixture. Increasing the curing temperature is helpful to reduce the decay rate of micro void diameter after freeze-thaw cycle, increase the stability of the void structure of cement stabilized gravel mixture and refine the micro void structure during the freeze-thaw cycle test, and thus increase the mechanical properties, fatigue durability and frost resistance of cement stabilized gravel mixture.

Rock mass classification is a fundamental component in tunnel engineering construction. With the rapid advancement of mechanized and intelligent construction technologies in China, drilling-parameter-based intelligent rock mass classification methods have become crucial in facilitating smart mechanized tunneling. This need is especially pronounced in the mountainous regions of Western China, where complex terrain and challenging construction, combined with limited experience in mechanized tunneling and the restricted applicability of current intelligent rock mass classification methods, make mechanized construction crucial for improving project quality and effectively controlling construction risks. A predictive method was proposed for intelligent rock mass classification using drilling measurement parameters. Focusing on multiple long tunnels as research subjects, on-site drilling parameters were collected and rock mass mechanical tests was conducted to construct a drilling parameter database, then intelligent algorithms was applied, such as support vector regression (SVR) and particle swarm optimization-back propagation (PSO-BP), to develop a predictive model for rock mass classification. The result indicates that the absolute value of correlation coefficient |r_s| between drilling parameters and rock mass classification indices is greater than 0.6, demonstrating a significant correlation, where torque and rotational speed show the strongest correlation with rock mass classification indices. A standardized parameter index database with 574 ideal samples was established through data-cleaning tools. Comparative analysis of predictive accuracy across intelligent algorithms indicated that the PSO-BP model demonstrated the best performance. The PSO-BP neural network-based prediction model was validated by transient electromagnetic (TEM) and tunnel seismic prediction (TSP) advanced geological forecasting, confirming its accuracy in predicting rock mass classification and providing reliable support for mechanized tunnel excavation.

In order to meet the transfer and transportation requirements of passengers and to significantly mitigate the loss of passenger flow that exceeds the waiting tolerance threshold, the optimization method of feeder bus scheduling considering the arrival time and passenger flow loss of rail transit trains was studied. The distribution of passenger flow demand was characterized by the passenger transfer demand and the arrival times of rail transit within the study period. The transfer time was described by the alignment between the time passengers arrive at the station of the bus and the bus departure schedule, as well as the operational capacity of the buses. The constraints of bus departure intervals, passenger flow loss and transfer demands were considered, and the multi-objective optimization with the minimum passenger flow loss, bus number and passenger transfer waiting time was realized under the limited number of buses that can be scheduled. Due to the contradictions among the optimization objectives, the model was solved with Non-dominated Sorting Genetic Algorithm II (NSGA-II). Finally, taking the actual bus routes as an example, the results show that the optimized model takes into account the bus operation cost and the passenger transfer time cost, and can obtain the scheduling that meets the passenger flow demand and represents different priorities. When the number of feeder buses is the same, the total transfer waiting time of the optimized method is reduced by 8.0% compared with the uniform headway. The average factor under the uneven is 59.3%, which is better than the average factor of 50.2% under the uniform. The calculation results validate the effectiveness and rationality of the model and algorithm, effectively enhancing the match between the time and capacity of buses and urban rail transit.

With the rapid advancement of air transport aviation and general aviation, the frequency assignment of air navigation stations has become increasingly crucial. A comprehensive algorithm was presented for assigning frequencies to individual air navigation stations. Subsequently, in order to address the issue of frequency assignment for multiple aviation navigation stations, a model was established specifically for civil aviation navigation stations. Finally, considering the limitations of traditional multi-objective genetic algorithms such as slow convergence speed and susceptibility to local optimal solutions, a multi-objective genetic algorithm along with a multi-objective genetic local search algorithm based on optimal weight allocation was proposed to effectively solve the frequency assignment problem faced by aviation navigation stations. The problem encompasses several objectives including minimizing frequency interference and utilizing the minimum number of frequencies required. Simulation results demonstrate that our proposed multi-objective genetic local search algorithm successfully resolves the frequency assignment problem encountered by air navigation stations. In comparison with traditional multi-objective genetic algorithms and those incorporating optimized weight allocation, our proposed algorithm significantly enhances solution quality, convergence speed, and stability.

Aiming at the current aviation typical parts with complex structure and high precision requirements, increased difficulty in quality inspection but backward inspection methods, an inspection plan automatic preparation method was proposed. Firstly, from the information reuse and information integration, the inspection number model was constructed on the basis of the design model, which layed a foundation of data source for the generation of the inspection plan. Secondly, the production method of the inspection plan was formulated, and the rules of the inspection sequence generation were designed, to achieve the automatic generation of inspection sequence number by using the improved Dynamic planning algorithm. Finally, combined with the CAA(component application architecture) secondary development interface and Excel component development, the corresponding function module was developed under the CATIA environment, to achieve the output of the inspection element information and process view, to complete the automatic preparation and export of the inspection plan. Through examples, it is proved that the method can reduce the labour intensity of inspectors and improve the efficiency of the preparation of inspection plans.

In view of the position control accuracy and body chattering of small unmanned helicopter under large disturbance, an improved sliding mode controller (SMC) was proposed. Firstly, for the unknown parameters in the dynamic model of small unmanned helicopter named Align T-REX 300, three flight experiments were designed for the data acquisition, determining the comprehensive aerodynamic parameters. Then, the dual-channels control strategy with an improved SMC was designed to realized the helicopter hovering. The parameters ranges of SMC were presented by combining stability analysis, which can effectively suppress chattering. The model validation experiment shows that the aerodynamic parameters determined by the flight experiments have high fidelity. Moreover, the control simulations show that the steady state error of the step responses is less than 0.02 under the continuous large disturbance. Under the improved SMC with high control accuracy and weak tremor, the servos' control signal curves are smooth, which is more conducive for achieving the flight control of unmanned helicopter under the disturbance in actual engineering.

The karst region in Northern Guangxi is one of the most typical and severely rocky desertification areas in China. Research on ecological restoration in this region is of significant importance. The study focused on the moss experimental research area in Pingfeng Mountain, Qixing District, Guilin City. The dominant moss species in four typical habitats were selected using the five-point sampling method. Field cultivation methods for these species were studied. Different slope treatments' effects on moss growth patterns were revealed. Mosses were explored as innovative methods for ecological restoration in rocky desertification areas. Hyophila involuta, Barbula unguiculata, and Bryum paradoxum were identified as dominant species, serving as references for ecological restoration in rocky desertification areas. In the field cultivation experiment, the effects of slope on moss growth indices were considered. The indices analyzed included moss coverage, plant density, plant height, moss crust thickness, and dry weight. Barbula unguiculata and Bryum paradoxum are considered as dominant moss species for ecological restoration of rocky surfaces in desertification areas. They effectively solve the problem of large bare rock surfaces in Northern Guangxi and improve the ecological benefits of karst areas.

In order to estimate carbon storage and investigate its influencing factors in typical karst rocky desertification regions, Jianshui County, located in the upper reaches of the Yangtze River and within the Nanpan River Basin, was selected for study due to its significant ecological position. Data from Landsat 5 Collection 1 Tier 1 Surface Reflectance, Landsat 8 Collection 1 Tier 1 Surface Reflectance, and digital elevation model (DEM) were used to conduct the analysis. The google earth engine (GEE) platform and the integrated valuation of ecosystem services and trade-offs (InVEST) model was used to estimate and predict carbon storage, while the geodetector tool was employed to analyze influencing factors. The results show that, between 2000 and 2020, Jianshui County was predominantly classified as potential rocky desertification and non-desertification, with the rocky desertification area found to have decreased by 115 km² overall, and the non-desertification area increased by 21 km², reflecting an improvement in desertification conditions. Carbon storage displayed a spatial distribution pattern of “higher in the north and south, lower in the center,” with recorded values of 116.293 t, 113.310 t, and 118.363 t in 2000, 2010, and 2020, respectively, demonstrating an initial decrease followed by an increase. Projections suggest that carbon storage may reach 129.981 t by 2030. Temperature (q=0.672) was identified as the primary factor affecting changes in carbon storage, with the interactions between factors shown to have a stronger effect on carbon storage than individual factors, especially the interaction between slope and temperature (q=0.998), indicating a non-linear enhancement. It is concluded that carbon storage in Jianshui County exhibits a trend of initial decrease followed by increase, influenced by both natural and social factors, with a spatial pattern of “higher in the north and south, lower in the center.”

In order to evaluate the characteristics of fire smoke dispersion in large-space buildings, FDS software was used to investigate the effects of air-inlet position, smoke-proofing depth and smoke exhausting rate to the working efficiency of fire isolation belt. The results show that, by opening the air-inlet in the non-fire zone and closing that in the fire zone, a reverse airflow can be induced below the fire isolation belt, which resists the smoke overflow and reduces the disturbances to the smoke layer. The increase of the smoke-proofing depth can increase the space for smoke storage and reduce the height of the smoke layer, thus delaying the instant of smoke overflow by decreasing the generation rate of fire smoke. In the scenario without mechanical smoke exhausting in the fire isolation belt, the instant of smoke overflow can be delayed by about 50% when the smoke-proofing depth increases from 20% to 40%. By adopting mechanical smoke exhausting in the fire isolation belt, the accumulated smoke can be timely extracted, which can effectively prevent the smoke spilling into other non-fire zone. It is concluded that, through an overall consideration to the smoke isolation efficiency, building functions and construction costs, the combination of “40% smoke-proofing depth + adopting the mechanical smoke exhausting rate with reference to the Smoke Standard + opening the air-inlet in the adjacent non-fire zone” is recommended as an advantageous design principle for fire isolation belts.