FDS(flocculation-dehydration-solidification) coupling process has been proven to significantly enhance the efficiency of resource conversion in engineering waste soil. However, the material fate within the process and the advantages of recycling press-filter filtrate remain to be further investigated. FDS experiments were conducted to analyze the material fate of each component in the flocculant-solidifying agent during the FDS process. Based on the findings, the potential benefits of recycling press-filter filtrate were explored. The results reveal that approximately 18% to 35.57% of Na⁺ and 0.1% to 0.56% of Si elements are detected in the press-filter filtrate, whereas Ca, Mg, and Al elements primarily remained in the filter cake, with proportions close to or equal to 100%. The recycling of highly alkaline press-filter filtrate into the process is found to not only improve the dissociation efficiency of mud and sand but also serve as a “pretreatment” for subsequent FDS stages. Waste soil particles are observed to adsorb residual materials from the filtrate, enabling dynamic adjustments in material dosage according to material transformation patterns and filter cake performance requirements. This approach ensures that materials lost in the filtrate are continuously recycled and utilized, maintaining a dynamic circular process.

In order to explore the influence of environmental lighting on visual fatigue during safety sign identification, 12 mixed lighting environments were designed with illuminance and color temperature as environmental variables. Eye movement data were tested under different conditions by eye-tracking technology and a two-factor ANOVA was conducted.Combined with the test results of reaction speed and comfort, the changes of visual fatigue under different lighting conditions were analyzed.The experimental results show that the change of illumination has a significant effect on visual fatigue, subjects are more prone to visual fatigue in low illumination environment; The change of color temperature has little effect on visual fatigue, but in different mixed lighting environments, the change of color temperature will affect the visual comfort of the subjects, and the fatigue state will change accordingly.The suitable color temperature range for the lighting conditions in the working environment is 2 100~3 500 K, and the illuminance range is 550~900 lx. It is concluded that companies should pay attention to improving the lighting conditions during production, avoid reducing the recognition efficiency of safety signs by workers due to visual fatigue, and ensure the safety and health of workers.

Oil transfer station plays a crucial role in the oil and gas gathering and transportation system of an oilfield, ensuring stable production and continuous supply of oil and gas. However, given the complexity of its process system and the ambiguous uncertainty surrounding fault modes and relationships, a systematic reliability assessment method integrating T-S fuzzy fault trees with BNs(Bayesian networks) was proposed. Firstly, a T-S fuzzy fault tree was established based on T-S gates and their descriptive rules, which is subsequently converted into a Bayesian network model. Secondly, leveraging limited fault samples and general data sources, Bayesian updating estimation was employed to determine the failure rates of basic events, addressing the uncertainty inherent in fault sample data. Lastly, the T-S fault tree and BN model were synergistically utilized for forward reasoning to predict the reliability of the process system and the contribution of basic events, while reverse diagnosis is conducted to pinpoint the key factors causing different fault states of the system. Research conducted on typical oil transfer station process systems has demonstrated that the proposed method can effectively predict system failure rates and diagnose weak links even under conditions of uncertainty in basic data and event relationships. This provides crucial decision support for the optimal design and reliability maintenance of complex oil and gas process systems.

In order to guarantee the safety of UAV operation in low-altitude airspace and promote the rapid development of low-altitude economy, a detection method and resolution strategy for multi-UAV flight conflicts are constructed. Firstly, based on ADS-B(automatic dependent surveillance-broadcast) flight data, an improved FR-IMMCKF(fuzzy reasoning interactive multiple model cubature Kalman filter) algorithm was used to predict the UAV trajectory, and secondly, based on the relative motion status between UAVs, a preliminary screening of the conflict aircraft was carried out, and based on the velocity obstacle method, the vertical detection part was added so as to support the three-dimensional range of conflict detection, and then, the conflict coefficient was introduced as the weight in the flight conflict network, and the conflict status was proposed as the conflict status. Then, the conflict coefficients were introduced as the weights in the flight conflict network, and the conflict state SSM(space model) was proposed to visualize the resolution intervals, and finally, the resolution strategies of height adjustment, heading adjustment and speed adjustment were set up, and the optional resolution intervals of heading and speed were introduced. A low-altitude airspace five UAV flight conflict scenario was constructed for simulation and validation, and the results show that the proposed method is able to give a conflict resolution order and provide a feasible resolution strategy in a complex flight situation.

A critical human factors analysis method for aircraft runway overrun and veer-off event was proposed to address the complex causal relationship of human factors that have not been fully revealed in the investigation of the event. Firstly, improve the human factors analysis and classification system model to identify the root cause of overrun and excursion event, and use the fault tree analysis method to identify the direct cause of runway overrun and veer-off event. Secondly, a human factors investigation decision support model was constructed, and obtained the causal chain of aircraft runway overrun and veer-off event. Then, a Bayesian network was constructed to quantify the importance of the causal factors for the runway overrun and veer-off event caused by human factors. Through predictive reasoning, diagnostic reasoning, and sensitivity analysis, the key causal chain of the runway overrun and veer-off event was obtained. The results indicate that 32 causal chains of overrun and veer-off events is obtained based on the proposed human factors investigation decision support model, comprehensively revealing the human factors and their coupling relationships of the event. Bayesian inference can obtain the key causal chain of “improper action execution/improper information preprocessing → unsafe behavior → runway overrun and veer-off”. The above conclusions are basically consistent with the findings of investigations into aircraft runway overrun and veer-off events, and they hold positive significance for improving the early warning and prevention capabilities of such incidents.

Traditional robot localization and navigation methods in complex building environments are characterized by low accuracy, heavy reliance on sensors, and an inability to effectively address dynamic obstacles, making it challenging to achieve satisfactory results in practical applications. To address these issues, building information modeling (BIM) technology was introduced. BIM, with its geometric and semantic information, was utilized to assist robot localization and navigation in complex environments. More accurate environmental perception and optimal path planning were provided to robots, reducing the risk of collisions with environmental components and improving the accuracy and efficiency of task execution. The current status of BIM technology in robot localization, mapping, and path planning was compared, the advantages and challenges of its application in architectural environments were analyzed, and future prospects for its application in intelligent buildings and robotic intelligence were explored.

The civil engineering industry faces with a vast array of unstructured textual information during its digital transformation. Large language models (LLMs) provide a new opportunity for the intelligent transformation of the industry because of its powerful natural language processing capability. A systematic literature review approach was employed, and based on the technical framework of LLMs and the current state of research in vertical domains, four major application scenarios for LLMs in civil engineering were suggested, along with corresponding technological approaches, challenges faced, and research trends. It is found that exploratory research and application of LLMs in civil engineering have been conducted, primarily focusing on content creation, intelligent Q & A, text summarization, and analytical reasoning, covering the entire lifecycle of civil engineering projects and featuring interdisciplinary and multimodal integration. However, the utilization of LLMs struggles with low specificity of knowledge, poor timeliness of information, and inferior data quality and interactivity. Based on this, a series of future research opportunities were proposed to enhance the breadth and depth of LLMs application in the field of civil engineering by using parametric efficient fine-tuning technology to inject expertise in model optimization. Combined with knowledge graph, LLMs can improve the accuracy, interpretability and timeliness of answers. Multi-modal data types were integrated to expand the application scenarios of LLMs in civil engineering. Appropriate model evaluation methods were developed to quantify the value and performance of LLMs applications in civil engineering. In terms of application scenarios, combined with the characteristics of LLMs and civil engineering fields, the application of LLMs in complex tasks such as document generation, question and answer system, information extraction and compliance review can be expanded, and the interaction efficiency between practitioners and data can be improved. The purpose of the study is to provide reference for the academic and business circles to further apply LLMs in the field of civil engineering.

The Shenzijing-Hashituo Subsag is located in the central part of the Changling fault depression in the Songliao Basin, an area with promising oil and gas exploration prospects. It represents a favorable oil and gas accumulation zone within the Songliao Basin. Understanding the tectonic evolution of this region and its influence on hydrocarbon genesis is crucial for guiding future exploration and development efforts in the Changling fault depression. Based on the structural characteristics, ancient drop of faults, activity rate and growth index method, the structural evolution of the depression was analyzed. Combined with the characteristics of stratum erosion, it was determined that the Shenzijing-Hasituo subsag was co-deposited in the early stage, and the Shahezi Formation was affected by compression and uplift at the end, resulting in overall erosion and segmentation. The uplift on the west side of the Hasituo depression was further strengthened during the Denglouku Formation period. At the same time, it is clear that the tectonic evolution of the fault depression layer in the Shenzijing depression-Hatshituo depression of the Changling Fault depression follows the longitudinal evolution law of fault depression-co-deposition-inversion. This early rapid subsidence and late inversion and uplift form a relatively favorable accumulation combination model of lower generation and upper storage. The research results can provide theoretical references for the restoration and further exploration of the ancient structure of the Changling fault depression.

The generalized finite difference method for seismic wavefields numerical simulation is capable of adapting to undulating stratigraphic interfaces, eliminating the staircase scattering effect caused by such interfaces, and enhancing the accuracy of forward modeling. However, when the second-order generalized finite difference method is used to solve the wave equation, low temporal accuracy can lead to temporal dispersion at larger time intervals, affecting the precision of forward simulation. A fourth-order generalized finite-difference forward modeling algorithm for the scalar wave equation was studied, along with its stability conditions and dispersion characteristics. By transferring the fourth-order time derivative to the spatial derivative term, fourth-order accuracy in time was achieved, effectively suppressing temporal dispersion. Compared to the second-order generalized finite-difference method, the fourth-order approach allows for larger time intervals, thereby reducing computational costs to some extent. Experimental results demonstrate that the proposed algorithm effectively mitigates both staircase scattering and temporal dispersion, yielding higher computational accuracy. When applied to reverse time migration, it produces high-quality imaging profiles.

In order to study the relationship between rainfall and regional geological disaster risk, construct the meteorological warning model of geological disasters in Huangshui Basin, and provide reference for the geological disaster warning work in the basin. Huangshui Basin in Qinghai Province was taken as an example, where the susceptibility to geological hazards was evaluated using the information value method. The independence of each factor was tested using the Pearson correlation coefficient, and the accuracy of the evaluation results was assessed through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). Subsequently, a geological-rainfall coupling model was also established using the logistic regression method, incorporating rainfall data, to carry out geological disaster meteorological risk warning and verification. The results show that Huangshui Basin geological disasters of very high susceptibility area and high susceptibility area is mainly distributed in the river valley plain zone and its tributaries at all levels in the middle and lower reaches of the two sides, the region is a geological disaster area. The results of the susceptibility evaluation are assessed for precision, and an AUC value of 0.785 is found. Geological-rainfall coupled warning model in the susceptibility value, the day rainfall, the first 1 day rainfall, the first 2 days day rainfall, the first 2 days of rainfall and the first 3 days of rainfall on the disaster are all positive, the model's prediction accuracy for the occurrence of geological disasters is 84.2%, and the AUC value is 0.864. Based on the five typical geological disasters that occurred in Huangshui Basin on 29th August 2020, the established geological-rainfall coupling model was verified, and it is found that with the geological-rainfall coupling model warning, four geological disasters reach the first warning level, and the other one reaches the second warning level. Combined with the warning rainfall threshold, the model was used to achieve the early warning of the landslide in Hongyagou Village on 5 September 2024. It can be seen that the constructed meteorological early warning model for geological disasters in the Huangshui Basin has a good early warning effect and a relatively high prediction accuracy rate, which can provide references for disaster prevention and mitigation for relevant departments.