In order to comprehend the scale and connectivity of sandbodies in late oilfield development, it is imperative to conduct a detailed characterization of fluvial sandbodies in the upper Guantao Formation of Gudong oilfield. Through comprehensive analysis of core, well logging, and seismic data, the sedimentary characteristics of braided river and meandering river were determined under the control of dense well array. The results indicate that the upper Guantao Formation has undergone a sedimentary evolution process from braided river to meandering river. The lower Guantao Formation exhibits braided fluvial reservoir with braid bar, braid channel, and overbank sand microfacies, while the upper Guantao Formation features meandering fluvial reservoir with microfacies types such as point bar, abandoned channel, crevasse splay, and floodplain. By conducting core-log calibration analysis, the logging response characteristics and sandbody development laws of braided and meandering rivers were determined. The channel sandbody of a braided river is cut and overlapped in multiple stages while being distributed across multiple branches on a plane. On the other hand, the meandering fluvial channel sandbody develops in isolation but forms strips on a plane. Under the control of dense well array, we have quantitatively determined the scale of sandbody development for both braided and meandering rivers. Additionally, the relationship between sandbody thickness and width for different river types were established which holds significant guiding significance for identifying sandbody connectivity and 3D geological modeling.

Due to the similarity in site selection between underground pipelines and power facilities such as high-voltage power lines and urban rail transit power supply systems, underground pipelines are increasingly affected by stray currents generated by power facilities. The impact of AC stray currents on the corrosion rate of pipeline coating defects were simulated and analyzed. The effects of parameters such as AC current magnitude, soil conductivity, internal pressure of the pipeline, stress, distance to grounding electrode, and pipeline radius on corrosion were investigated in the study. The results indicate that when the stress on the pipeline is less than the yield strength, the effect of stress on the corrosion caused by AC stray currents is relatively small. However, after the deformation of the pipeline increases, serious corrosion will be induced by stress. The greater the AC current, the greater the corrosion at the defects of the pipeline coating. The lower the AC frequency, the greater the corrosion at the defects of the pipeline coating. The closer the distance to the grounding electrode, the greater the corrosion at the defects of the pipeline coating. The higher the soil conductivity, the greater the corrosion at the defects of the pipeline coating. The smaller the pipeline radius, the greater the corrosion at the defects of the pipeline coating. These research findings are of significant importance for ensuring the safe operation of oil and gas transportation pipelines under the influence of AC stray currents.

The dynamic modulus of asphalt mixture is an important parameter in the design of asphalt pavement. Extracting material characteristics, dynamic modulus, and phase angle information from a large amount of asphalt concrete datasets using integrated methods is of great significance for optimizing the performance of asphalt pavement. The extreme gradient boost (XGBoost) model aggregated a series of decision tree models through weighted summation to construct a powerful prediction model, while optimizing the loss function to minimize prediction errors. In order to further improve the accuracy of dynamic modulus and phase angle prediction, heuristic algorithms were used to optimize the model. Initially, the basic model was initialized based on samples and the gradient of the loss function of the training data was calculated. Subsequently, XGBoost utilized gradient details to construct a decision tree model, optimized leaf node weights, and updated the model’s predictions through weighted summation. During this process, heuristic algorithms are used to optimize the optimal parameters of the entire XGBoost model. The experimental results show that the improved XGBoost model outperforms the original model in all performance evaluation indicators, improving the accuracy of predicting the dynamic modulus and phase angle of asphalt mixtures.

The creep of wood under the action of long-term load will increase the deformation of wooden beams or wooden columns, and bring hidden safety risks to the building structure. In order to improve the creep performance of wood components, an aluminum wood composite columns (AWC) was designed to make AWC with 0,2%, 3%, 3% and 4% respectively. The creep test of 0.25 stress ratio of the lower column lasted 30 days. The creep strain-time curve and creep coefficient-time curve were obtained and the AWC creep pattern was analyzed. Burgers model was used to fit the creep strain-time curve to explore the influence of aluminum alloy content on AWC creep, and analyze the causes of AWC int creep inhibition from AWC material characteristics and load transfer. The results show that, compared with AWC 1 with aluminum alloy content of 0, the creep deformation of AWC 2 ~ AWC 4 decreases to different degrees, that is, aluminum alloy can effectively enhance the creep deformation ability of wood; The four AWC creep strain-time curve correlation coefficient based on burgers model are greater than 0.95, and the creep prediction model constructed by this model can predict the AWC creep.

The accuracy and confidence level of the unmanned aerial vehicle(UAV) landing range are of great significance for objectively assessing the UAV’s ground risk. The uncertain wind field and complex electromagnetic environment are the main causes of uncertainty regarding UAV failure and landing range. Given the particle assumption in the case of complete failure of the UAV, firstly, a dynamic model of the UAV trajectory descent with the initial position and velocity as the boundary value and the wind speed vector and the initial position as random variables was constructed, and the failure and landing range of the UAV were determined by Monte Carlo simulation. Secondly, a geometric method for determining the envelope of the UAV ground risk buffer was proposed, and the quantitative determination of the ground risk buffer of the entire UAV track was realized. Finally, the method proposed was verified by taking an aerial inspection route as example and compared with the buffer protection area of the UAV in different wind fields and under various operating conditions, the effect of uncertain wind field and its operating conditions on the ground crash range of the UAV was studied, and the ground risk buffer zone under different operating conditions was established. The results show that falling at higher speed and higher altitudes under stronger winds yields a wider impacting area and a larger ground risk buffer.

To achieve rapid and reliable prediction of asphalt mixture performance, a method for predicting asphalt mixture performance by optimizing the back propagation (BP) neural network with a genetic algorithm (GA) from the perspective of material composition design was proposed. Initially, a grey relational analysis method was employed to reduce the dimensionality of multidimensional input variables, identifying the core influencing factors of asphalt mixture performance. Subsequently, integrating the GA, a GA-BP neural network prediction model was constructed with the core influencing factors as the input layer and asphalt mixture performance as the output layer. The model underwent training, validation analysis, and prediction generalization application. A comparison with the training effectiveness and prediction accuracy of the BP neural network was conducted to verify the accuracy of the GA-BP neural network model. The research results indicate that the grey relational degrees of eight performance characteristics, including air void, asphalt-aggregate ratio, nominal maximum aggregate size, 4.75 mm passing rate, asphalt type, softening point, penetration, and ductility, are all greater than 0.6, signifying their significant impact on asphalt mixture performance. Compared to the BP neural network model, the GA-BP neural network model reduces the root mean square error (RMSE) by 16% to 31%, decreases the mean absolute error (MAE) by 15% to 24%, and improves the R² value by 0.01 to 0.27, indicating that it has better learning and fitting capabilities. The prediction accuracy for dynamic modulus, dynamic stability, residual stability, splitting tensile strength ratio, and ultimate bending strain of the asphalt mixture is respectively enhanced by 35.26%, 47.78%, 23.13%, 31.92%, and 35.75%, revealing the superior generalization application capability of the GA-BP neural network model. The research findings provide essential references for the rapid prediction of asphalt mixture performance and guidance in the design of asphalt mixture material composition.

In ultra-long-distance rock micro-shield pipe jacking, pipe wall friction is a key factor in determining the size of the jacking force and the arrangement of the intermediate jacking station, and the selection of a good bentonite mud is a key factor in controlling the pipe wall friction. In order to give full play to the polymer bentonite mud in the pipe jacking construction process of lubrication and support, the Chongqing Guanjingkou water conservancy hub project was used as the basis, through orthogonal tests and indoor straight shear test method on the basis of different types of bentonite mud ratio optimization, based on the performance parameters of the slurry, as well as the friction reduction mechanism to derive the optimal type of mud and its ratio. The results show these as follow. Sodium bentonite mud is the optimal type of mud, the ratio of bentonite to water is 14%, guar gum is 0.20%, soda ash is 0.40%, CMC(carboxymethyl cellulose) is 0.40%, PAM(polyacrylamide) is 0.10%, and its friction reduction effect is obvious, it can make the friction coefficient between concrete and gray rock reduced by 35%, which makes the friction between the pipe and the rock greatly reduced in the process of pipe jacking in the rock.

The narrow passageways and limited visibility in large underground parking lots often lead to vehicle collisions at blind intersections, posing significant dangers. In order analyze approach behavior at these intersections, a driving simulation experiment was designed. The experiment involved constructing a 3D model of the underground parking lot and integrating it with a connected vehicle warning information system. Four experimental scenarios were devised, considering variations in technical features (with or without warning information) and events (with or without vehicle conflicts at blind intersections). Using micro-driving behavior data from 31 participants, key metrics such as speed, acceleration, and braking position were selected to analyze approach behavior from the warning point to the blind intersection. Subsequently, correlation analysis was conducted, followed by the application of the k-means method to cluster driver types and examine the effects of warning information on the approach behavior of different drivers. Finally, the utility of the system was evaluated. The results indicate the following. ①When drivers approached blind intersections without warning information, the process typically involved three stages: speed maintenance, speed increase, and emergency braking. In contrast, when warning information was provided, speed decreased more uniformly and was 34.08% higher than without warning information. ②The warning information system reduced the average speed by 9.94 km/h compared to scenarios without warnings, and advanced the braking position by 4.49 meters, thereby effectively enhancing the safety of drivers passing through blind intersections in parking lots. ③The warning information system had discernible effects on conservative and general drivers, suggesting the need for additional training for radical drivers to help them understand the positive role of the warning system in improving driving efficiency and promoting safe driving practices. ④The warning information system significantly improved overall driver safety, with the greatest impact observed among conservative drivers, followed by ordinary and aggressive drivers. These research findings provide support for the application of connected vehicle warning information systems in parking lots and contribute to the enhancement of parking lot safety.

The background difference method and cross-correlation method used in the extraction of the bullet position in the images collected by the traditional CCD(charge coupled device) intersection stand-up target have the problems of poor versatility and long time-consuming. By analyzing the problems existing in CCD precision target image projectile extraction, a method for CCD precision target image projectile extraction based on IGWO(improved grey wolf optimizer) algorithm was proposed. The DLH (dimensional learning-based hunting) search strategy was used to update the position of each search factor through the neighborhood. Generate candid ate solutions, increase the diversity of search populations, and jump out of local optimal solutions. The bullet extraction problem was transformed into the problem of finding the minimum connected region of gray value under certain constraints. The minimization area gray value model, the vertical light spot area and the low gray area elimination model were established. Under the same parameter setting, the IGWO, GWO(grey wolf optimizer), MFO(moth-flame optimization) algorithm, cross-correlation algorithm, and background difference method were used to conduct comparative experiments. The experimental results show that the target detection success rate of the IGWO algorithm is much higher than other algorithms, reaching 95%, and the algorithm solution time is much lower than other algorithms, shortening to 12 ms.

A comprehensive joint optimization solution was proposed to address the issue of traditional UAV(unmanned aerial vehicle)-assisted wireless sensor network data collection schemes, where only UAV energy consumption was optimized, while wireless sensor energy consumption is neglected. Firstly, clustering analysis was performed using the K-means algorithm and communication threshold between UAVs and wireless sensors to achieve effective clustering of wireless sensors. Secondly, a multi-objective optimization model was constructed to collaboratively optimize sensor energy consumption and UAV hovering energy consumption. The optimal UAV hovering position and wireless sensor transmission power were determined using a multi-objective particle swarm optimization algorithm. Finally, based on the optimal hovering positions of UAVs in each cluster, an ant colony algorithm was applied to compute the optimal flight path of UAVs, minimizing UAV’s flight energy consumption and thus minimizing the overall energy consumption of the entire data collection system. Simulation results indicate that the proposed solution achieves significant improvements in system energy consumption compared to traditional methods. Specifically, when the clustering radius is 120 meters, sensor energy consumption is reduced by 16.2%, and UAV energy consumption is reduced by 24.9%.