To explore the impacts of various factors on the performance of concrete, the response surface methodology was adopted to optimize the concrete mix proportion. In the experiment, the water-binder ratio, the dosage of steel slag, and the content of desert sand were taken as variables, with a focus on analyzing the main performance indicators such as the slump of concrete, compressive strength, and splitting tensile strength. The experimental results indicate that the content of desert sand has the most significant influence on the slump of concrete, while the compressive strength and splitting tensile strength are mainly affected by the variation of the water-binder ratio. With the increase of the content of desert sand, the slump, compressive strength, and splitting tensile strength of concrete exhibit a trend of initially increasing and then decreasing. When the content of desert sand reaches 30%, the performance is optimal. The addition of steel slag can enhance the fluidity of desert sand concrete (DSC). As the amount of steel slag increases, the compressive strength of DSC shows a decreasing trend, and the tensile strength increases initially and then decreases. The addition of steel slag interacts with desert sand, particularly on the tensile strength of DSC. Through response analysis, the optimal mix proportion was obtained as a water-binder ratio of 0.39, a dosage of steel slag of 10%, and a content of desert sand of 30%, at which the comprehensive performance of DSC is the best. Finally, non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) was utilized for multi-objective optimization, which yielded a more complete solution set, thereby providing certain technical support for the application of DSC.

In the practice of energy renovation of existing buildings, the uncertainty of renovation parameters has a significant impact on the renovation results. To support energy renovation decision-making, a Monte Carlo method combined with Latin hypercube sampling was proposed to evaluate different renovation schemes, and a tree-based Gaussian method was used to screen key variables that affect the renovation process. The results show that uncertainty analysis can quantitatively evaluate renovation schemes at the preliminary design stage. The energy-saving rates of two typical renovation scenarios fluctuate between 32.7%~55.2% and 55.5%~108.4%, respectively, with higher uncertainty in schemes with better energy-saving effects. The cumulative probability distribution was used to assess the probability of renovation success, with the probabilities of meeting renovation targets being 58% and 96.4%, respectively. The integration of renewable energy technologies ensures the renovation results. Sensitivity analysis results show that infiltration rate and equipment power density are the most important factors in office building energy consumption, accounting for 80% of the output variance, which provides a theoretical basis and methodological reference for the selection of more building energy renovation schemes in the future.

On-line and efficient monitoring of leakage faults in district heating network can effectively increase the quality of heat transmission and reduce energy consumption. However, the data feature extraction ability of conventional leakage fault diagnosis method is limited, and it is difficult to deal with the high dimensional nonlinear pressure flow monitoring data for complex heating network, which makes its diagnostic performance weak. Therefore, a fault diagnosis model of heating network leakage based on convolutional neural network (CNN) and Transformer was proposed. The proposed CNN-Transformer diagnostic model combines CNN and Transformer network to realize joint learning of different time scales and spatial features. The CNN network was used to extract local features, and the Transformer network was used to capture global features. The validity of the model was verified by simulating the fault data set of the annular heating pipe network system. The results show that the proposed CNN-Transformer diagnosis model based on multi-stage feature extraction and fusion mechanism of fault features significantly improves the accuracy of leak diagnosis. The CNN-Transformer method has the highest accuracy on the test set, with an accuracy increase of 13.21%, 7.49%, 6.1% and 4.62%, respectively, compared to other fault diagnosis methods including long short-term memory network, gate recurrent network, CNN and Transformer.

In order to investigate the segregation characteristics of asphalt concrete with large size core walls, indoor and field tests were conducted. Firstly, the density ratio data of the indoor prepared asphalt concrete specimens were analyzed by extreme variance analysis. Secondly, the density data of the upper and lower parts of the asphalt concrete core samples with the maximum aggregate size of 37.5 mm drilled under different working conditions in the field were statistically analyzed based on the one-dimensional variance theory. Finally, the basic mechanical properties of the upper and lower parts of the asphalt concrete cores of the large-grain-size aggregate core wall were comparatively analyzed under different working conditions. The results show that the relatively large influence on the segregation of asphalt concrete in the heart wall is the largest aggregate size, followed by the test temperature, the smallest is the amount of asphalt, and the tendency of segregation of asphalt concrete in the heart wall increases with the increase of the aggregate size, the increase of asphalt dosage and the increase of the test temperature. The tendency of segregation of asphalt concrete with the largest aggregate size of 37.5 mm molded under different working conditions from large to small are: initial milling temperature of 145 ℃ with paving thickness of 30 cm, initial milling temperature of 130 ℃ with paving thickness of 30 cm, initial milling temperature of 145 ℃ with paving thickness of 40 cm and initial milling temperature of 130 ℃ with paving thickness of 40cm, respectively. The tendency of segregation of its own will be increased with the increase of the thickness, initial milling temperature and asphalt consumption, and with the increase of test temperature and asphalt consumption. The tendency of segregation will decrease with the increase of paving thickness and the decrease of initial milling temperature. The differences in Marshall stability, Marshall flow value and split tensile strength between the upper and lower parts of the core samples of asphalt concrete with the largest aggregate size of 37.5 mm molded under the above four working conditions are all within 6%, with good uniformity.

When a large number of containers arrive at a node during coal intermodal transportation, it can lead to problems such as container congestion and transfer delays during the transshipment process. Based on this, the criteria importance through intercrieria correlation (CRITIC) objective weighting method was adopted to quantify the risk of transfer delay in coal rail-road intermodal transportation, and then the risk was incorporated into the path optimization factors. Meanwhile, the improved activity-share-intensity-factor (ASIF) equation was introduced to measure the carbon emissions of transportation and transshipment node exchange processes. A carbon emission and transfer delay risk model for the entire freight transportation time was established. Based on the above model, a coal rail-road intermodal transportation path optimization model was proposed, with the objectives of minimizing carbon emissions, transportation costs, transportation time, and transfer delay risk. Through a case study, MATLAB programming was introduced, and the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) algorithm was designed to solve the example. The optimization results under different transportation decision conditions were simulated, respectively. By comparing the single-objective optimization results of minimum carbon emissions, minimum transportation costs, shortest transportation time, and minimum transfer delay risk with the multi-objective optimization results that comprehensively consider the above objectives, the advantages of multi-objective optimization in handling low-carbon path optimization for coal rail-road intermodal transportation were explored. The research results show that compared with single-objective optimization, the multi-objective optimization scheme can effectively reduce transportation costs, shorten transportation time, reduce comprehensive energy consumption, and lower the risk of transfer delay, achieving a comprehensive optimal combination of economy and safety in coal rail-road intermodal transportation. At the same time, it also provides a certain reference for the path optimization of rail-road intermodal transportation under different transportation demands and scenarios.

The construction of the pile-beam-arch-method(PBA method) exhibits a cluster-tunnel effect, which readily induces surface subsidence. To achieve safe construction and minimize surface settlement, the MatDEM discrete element software was employed to simulate the construction process of the PBA method. The potential deformation mechanism and metastable microstructure of soil were considered in the training of formation micromechanics parameters, and the variation rule of surrounding rock pressure under multi-guide tunnel construction was summarized. The research findings indicate that when adopting a construction sequence of “top-to-bottom and sides-to-center,” surface settlement is relatively small. When using the “center-to-sides” approach, the initially excavated central pilot tunnel leads to the formation of slip planes at 1.5 times the tunnel width. Conversely, with the “sides-to-center” method, the initially excavated side tunnels provide vertical support to the inner soil, reducing the surface settlement caused by the subsequent excavation of the central tunnel. During the construction of the upper pilot tunnels, the surrounding rock pressure at the vault and arch waist exhibits an increasing trend. The construction of the lower pilot tunnels results in stress concentration at the vault and inner arch waist of the upper tunnels. Approximately 72.65% of surface settlement occurs during the construction of the lower pilot tunnels, and the adoption of staggered construction can significantly mitigate soil disturbance.

In order to analyze the distortion effect of thin-walled box girder under lane load, the distribution law of stress amplification coefficient in thin-walled box girder was studied. The distortion angle and distortion generalized bending displacement were taken as node displacements. Based on the initial parameter solution of the homogeneous equation of the distortion control differential equation, the thin-walled box girder was divided into beam segment elements, and its element stiffness matrix and equivalent node load array were derived. Using Fortran language and referring to the calculation program FRAME2 for planar frame structures, a finite element program for beam segments that can be used to analyze the distortion effect of thin-walled box girders was obtained. The program was used to analyze the variation laws of the distortion internal forces and stress amplification factors of box girders under lane loads. The results show that the distortion warpage stress of three-span continuous box girder with variable cross-section under the action of lane load has a sudden change at the place of concentrated load, where the maximum and minimum values are obtained, the longitudinal distribution of stress amplification factor is similar to the longitudinal distribution of distortion warpage normal stress, both of which are symmetrically distributed along the span, the magnification factor of normal stress at the top and bottom of the section is 1.080 and 1.180 respectively. The points with smaller bending stress should not be considered when the magnification factor is taken into account.

During tunnel construction, the deformation of surrounding rock and the mechanical response of the supporting structures are significantly influenced by the lateral pressure coefficient λ. Accurate determination of the on-site lateral pressure coefficient is essential for guiding tunnel design and construction. Firstly, the impact of the lateral pressure coefficient on settlement displacement of the tunnel vault and horizontal displacement of the side walls was analyzed theoretically. Secondly, the ratio between horizontal displacement of the side walls and settlement displacement of the vault was monitored, and a numerical simulation was employed to establish a mathematical relationship between the horizontal-vertical displacement coefficient K and the lateral pressure coefficient λ, enabling the inversion of the lateral pressure coefficient. Finally, the inverted lateral pressure coefficient was applied to optimize tunnel cross-section design. The results indicate that, under the same geological conditions, an approximately linear relationship exists between K and λ. Regardless of changes in tunnel depth or surrounding rock conditions, a proportional relationship between horizontal and settlement displacements is maintained, which can be used to invert the lateral pressure coefficient at the tunnel site. By adjusting the tunnel axis ratio m to gradually approach λ^-1, deformation is effectively controlled and the proportion of lining damage is reduced.

The complex nonlinear characteristics and dynamic friction properties of the integrated brake-by-wire system were recognized as challenges for precise hydraulic pressure control. To address these issues, a precise hydraulic force control strategy was proposed for the integrated brake-by-wire system. Firstly, the structure and control framework of the integrated brake-by-wire system were analyzed, and equivalent simplified models were established for each component to facilitate controller design. Secondly, a three-layer cascade pressure control method was introduced for active braking in the integrated brake-by-wire system. Specifically, the pressure control layer was designed based on the active disturbance rejection control method, which mitigates the effects of hysteresis nonlinearity in the hydraulic system. The position control layer employs a robust sliding mode variable structure control method and addresses the dynamic and static friction issues in the transmission mechanism. The current control layer was designed using the linear matrix inequality method to enhance the braking motor's dynamic following performance. Joint simulation tests using AMESim-Simulink demonstrate that the integrated brake-by-wire system achieves good pressure control performance across various operating conditions, maintaining the steady-state pressure tracking error within 0.1 MPa, compared to the traditional proportion-integration(PI) control method, the mean transient pressure tracking error is reduced by 0.14 MPa using the proposed method, the mean steady-state pressure tracking error decreases by 0.8 MPa, and the response lag time is lowered by 0.04 s, which verifies the effectiveness of the control strategy proposed.

In order to address the supply-demand imbalance in the increasingly complex and changing market environment, improving the accuracy of air cargo volume forecasting is of great significance for route planning and supply chain optimization. Firstly, based on monthly air cargo data from January 2000 to December 2022 as the training set, seasonal fluctuations and long-term trends were captured using seasonal and trend decomposition using loess (STL). Secondly, a deep learning time series prediction model (LSTM-SVR) was used to fit the nonlinear changes in cargo volume due to emergencies. Finally, the prediction model was tested based on monthly data for the entire year of 2023. The results indicate that the seasonal and combination prediction model (STL-SVR-LSTM) is more accurate in predicting air cargo volume during emergencies compared to traditional methods such as ARIMA, SVR, or LSTM. The data validation in 2023 shows that the root mean square error and average absolute percentage error of the seasonal and combination prediction models are 3.53 and 3.53%, respectively, with a goodness of fit score of 0.79. The LSTM model has the second best prediction results, with root mean square error and average absolute percentage error of 5.66 and 7.73%, respectively, and a goodness of fit score of 0.58, significantly better than the other two traditional models. It can be seen that this prediction model can adapt to the prediction of air cargo volume in complex environments, which is helpful in providing reference suggestions for enterprise operation and enhancing supply chain stability in case of emergencies.