Landslide disasters pose a serious threat to residents’ lives and socio-economic development. Taking Xinyuan County as the study area, 17 landslide influencing factors were selected as the initial factor set. Through multiple collinearity analysis, 10 landslide factors were screened and an evaluation index system for landslide susceptibility in the study area was constructed. The landslide susceptibility was then evaluated based on three typical models: logistic regression (LR), support vector machine (SVM), and random forest (RF). The evaluation results of the model were compared and validated using the area under curve (AUC), landslide ratio, and field investigation under the receiver operating characteristics(ROC) curve. The results show that low-susceptibility areas are mainly concentrated in the valley plain of the Kongnais River, where the terrain is flat and the landslide susceptibility is relatively low. High-susceptibility areas are mainly located in the northern part of the Kongnais River valley, the Awulale hilly area, and the watersheds on both sides of the southern Yishikelike Mountains and Nalati Mountains, as well as the area south of the Qiafu River, where the terrain is complex and varied, leading to a higher susceptibility to landslides. Among the three evaluation models, the SVM model performs the best, with an AUC value of up to 0.985, indicating its high accuracy in landslide susceptibility assessment. Furthermore, the high-susceptibility areas identified by the SVM model have a high density of landslide points, accounting for 86% of the total, further validating its effectiveness in landslide susceptibility assessment. Based on the above results, the SVM model is more reasonable than the other two alrorithms in assessing landslide susceptibility in Xinyuan County, providing a scientific theoretical basis and reference for landslide prevention and control in the region.

The role of gas storage in regulating natural gas peaks is crucial. Improper allocation of gas injection schemes during the injection process not only results in excessive energy consumption by compressors, but also leads to excessive pressure changes in certain individual wells and convergence of salt karst cavities, thereby affecting the long-term stable operation of gas storage. By combining the simulated annealing algorithm with actual field conditions, a multi-objective optimization function was established considering both compressor energy consumption and dispersion degree of wellhead pressures across all gas storage wells within the same block. The variable for this optimization was set as the gas injection volume during the task period for each gas storage well, while variables such as maximum design pressure of pipelines, minimum operating pressure, and maximum operating pressure of gas storage wells were taken into account. Additionally, constraints were imposed based on the maximum design flow rate measured by target flowmeters for multi-objective optimization purposes. Results indicate that compressor power consumption can be reduced by over 40% and formation pressure differences can be decreased by more than 90%. It is evident that this scheme provides assurance for ensuring long-term stable operation of gas storage through effective guidance on actual production operations.

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.

Rockburst is an extremely destructive geological disaster in deep underground engineering. In order to accurately predict the intensity level of rockburst, a method for rockburst intensity level prediction based on parallel fusion graph Transformer (PFGT) was proposed. Firstly, the similarity structure relationship of rockburst data in Euclidean space was utilized to construct graph-structured data. Besides, another kind of graph-structured data was constructed by utilizing multiple rockburst criteria to constrain the structural distortion of rockburst data in European space. Single-scale features of rockburst data was obtained through parallel training. Secondly, a feature fusion graph Transformer strategy was designed, which obtains multi-scale features of rockburst data by fusing two types of graph-structured data features based on Euclidean space and based on rockburst criteria. The method improves the data representation capability by simultaneously utilizing single-scale features and multi-scale features. During the training process, using Transformer for feature fusion enables the model to more comprehensively capture the optimized features of rockburst data, thus improving model performance. Compared with traditional neural networks and other machine learning algorithms, the prediction accuracy of the PFGT model is 94.87%, which is superior to other algorithms, proving the effectiveness of this algorithm and providing a new method for rockburst level prediction.

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.

In order to explore the durability of microbially induced calcite precipitation(MICP) technology to improve the durability of aeolian sandy soil materials, 0.08% polymer absorbent resin (MICP+A) and 0.37% xanthan gum (MICP+B) were used to improve the traditional MICP materials. The microstructure of different cycles of high and low temperature cycle and ultraviolet irradiation was studied by nuclear magnetic resonance technology, and the durability of mineralized sandy soil materials was investigated. The results show that the porosity of both MICP+A and MICP+B materials increases with the increase of cycling cycles. In 20 cycles of high and low temperature cycling tests and 15 cycles of ultraviolet irradiation tests, the MICP+A material shows good stability, and the porosity increment decreases by about 1.8 times and 1.1 times, respectively, compared with the conventional MICP material. Under high and low temperature cycling and ultraviolet irradiation, the crystal structure of calcium carbonate is altered and the percentage of medium-sized pores in the soil increases, causing the 2nd peak of the T₂ spectra of all three materials to be higher than the pre-test peak. The test shows that the polymer water-absorbing resin can improve the stability performance of the traditional MICP specimens, and this study provides a basic experimental basis for the engineering application of microbial mineralized geotechnical materials in the treatment of desert areas.

In order to better protect the safety of bridge piers and ships, a step-by-step progressive high energy consumption anti-collision magnetorheological damper for bridge piers was designed for the problems of passive energy dissipation, poor dynamic response and anti-collision energy dissipation, and limited adaptability of the device. The structural parameters of the damper were established through the establishment of a mechanical model. The structural strength and magnetic circuit were analyzed using finite elements. The results show that the strength of the structural components meets the requirements, the magnetic induction strength at the gap under 2 A current can reach about 1.2 T, three pistons are set up in the cylinder barrel, which can work together under different crash depth displacements to divide the energy dissipation of the damper into three stages, the minimum damping force under no current is 15 kN, the maximum damping force is 496 kN, and the damping force improves with displacement by 481 kN. The damping force under 2.5 A current increases with displacement from 78 kN to 1 204 kN, which is an improvement of about 15 times. It effectively improve the force of the damper and achieve the excellent effect of graded progressive impact energy dissipation. The application in the bridge pier collision avoidance device can achieve semi-active collision avoidance energy dissipation. The theoretical and finite element simulation results basically coincide with each other, proving the rationality of the damper design.

To enhance the grid connection stability of virtual synchronous machines, a global optimization design method for virtual synchronous machine control parameters was proposed. Firstly, a small-signal model of the virtual synchronous generator with virtual exciter and governor was established, and the system eigenvalues were obtained by solving the state matrix. Secondly, the sensitivity of the controller parameters to the position of eigenvalues was studied, and a wide range of parameter optimization was conducted using genetic algorithms based on the main eigenvalue positions. Finally, analytical solutions of the model and MATLAB/Simulink simulation data were compared. The results show that significant improvements in frequency stability can be achieved by optimizing a wide range of virtual synchronous machine parameters. After optimization, the system response transient stability time is 0.25 s, only 5% of the transient stability time with general parameters, and the frequency stability improves noticeably with load changes.

Process production safety monitoring is the main technical method for safety risk control and accident prevention, and data is a significant basis for safety control and decision-making. In the existing security monitoring network, there are many sensor nodes and large amounts of data, which cause heavy channel load in the wireless sensor network. This often leads to issues such as data latency and loss, which affects the timeliness and accuracy of safety control decisions. Therefore, the security risk factors of typical process production scenarios were focused on. Based on this, the sensor deployment plan and wireless sensor network data transmission architecture were clarified, a security monitoring data flow scheduling method based on superior-subordinate server was proposed, and the data stream congestion index and abnormal data packet frequency index were used as the main indicators of data flow performance evaluation. Subsequently, the chemical polymerization reactor were taken as an engineering scenario, the performance improvement was examined after the subordinate server was initiated to share the data flow for the superior server. Through the comprehensive study of channel load balancing, the method of superior-subordinate server is benefit to ensure the effectiveness of orderly transmission of safety monitoring data and risk control.

In order to study the influence of slurry shield tunneling parameters on surface settlement, based on the slurry shield tunneling and monitoring data of the left line of the Hesong-Heshan stacked section of Harbin Metro Line 3 project, based on the BP neural network optimized by genetic algorithm, the different settlement output forms were studied. The tunnel distance label was introduced to optimize the neural network fitting effect, and the parameter sensitivity analysis was carried out according to this network model. Three most sensitive parameters were obtained, and exhaustive tests were carried out to further analyze the specific influence of parameters on surface settlement. The research shows that the surface settlement performance of slurry shield tunneling is not closely related to the tunneling parameters after passing through a certain ring for two days, and the surface settlement analysis can focus on the monitoring value of the day. Before, during and after the shield machine passes through a certain ring, it will have different effects on the surface settlement above the ring. Subsequent research on surface settlement based on neural network can be considered to include this index. Among the parameters of slurry shield tunneling, reducing slurry viscosity and increasing slurry specific gravity can control surface subsidence, and increasing propulsion speed can reduce the impact of construction on surface subsidence.