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Using fly ash as a roadbed filler represents a significant and innovative advancement in civil engineering. This approach not only offers a sustainable solution for utilizing industrial solid waste but also helps alleviate the environmental pressures associated with waste disposal. By replacing traditional road construction materials with fly ash, engineers can reduce costs while simultaneously contributing to eco-friendly infrastructure development. However, the effective implementation of fly ash in roadbed construction presents challenges, with compaction control emerging as a critical issue. Poor compaction can lead to a range of problems, including uneven settlement, reduced bearing capacity, and compromised roadbed integrity. These issues can significantly impact the long-term performance and durability of the pavement, potentially resulting in costly repairs and safety hazards. To address these challenges, this study conducts a series of comprehensive electric compaction experiments and vibration compaction tests. By meticulously adjusting key parameters such as water content, compaction effort, vibration duration, and vibration frequency, the research aims to identify the optimal conditions for achieving maximum dry density and uniform compaction. The experimental results, rigorously validated through mathematical modeling, reveal several key insights. The relationship between dry density and water content follows a unimodal trend, with peak dry density occurring at an optimal water content of 44%-45%. Moreover, dry density increases with the number of compaction blows, reaching a satisfactory range of 1.05-1.14 g/cm3 after 98 blows. Interestingly, while dry density consistently increases with vibration frequency, it exhibits a peak at 30 seconds of vibration duration; beyond this point, excessive vibration may induce liquefaction, leading to a reduction in density. Based on these findings, a comprehensive compaction model covering the transition from drying to liquefaction is established. This model facilitates accurate predictions of dry density under various conditions, providing valuable references for optimizing construction parameters and enhancing compaction control. By implementing these optimized parameters, engineers can ensure more uniform and stable roadbeds, thereby promoting the wider application of fly ash in road construction. This, in turn, contributes to environmental sustainability and the development of resilient infrastructure, paving the way for a greener and more sustainable future.
Based on Self-Determination Theory, this study constructed an indicator system comprising 25 evaluation metrics across five dimensions(external regulation, introjected regulation, identified regulation, integrated regulation, and intrinsic motivation) to investigate the multidimensional motivational composition and influencing mechanisms of aviation safety confidential reporting behavior among civil aviation practitioners. The objective is to optimize the effectiveness of confidential reporting systems and improve both the quantity and quality of reports. Through a combined methodology of questionnaire surveys and statistical analysis, the research empirically examined the influence of different motivational dimensions on reporting behavior among airline employees. The results demonstrate that:(1) the confidential safety reporting behavior among civil aviation personnel is significantly influenced by five distinct regulatory factors, revealing a sophisticated multi-level motivational framework that governs their reporting decisions and actions;(2) current reporting practices are predominantly driven by external regulation and introjected regulation mechanisms, while autonomous motivation forms—particularly identified regulation and intrinsic motivation—remain substantially underdeveloped, resulting in suboptimal outcomes in both reporting frequency and information quality;(3) a quality-oriented incentive mechanism, a non-punitive safety culture, and value guidance coupled with behavioral internalization constitute the key pathways for transforming employees' voluntary reporting behavior from passive compliance to active engagement. The findings reveal the complex interplay between controlled and autonomous motivations in influencing reporting behavior and highlight the need for organizations to develop targeted strategies that enhance autonomous motivations through systematic interventions. These include redesigning incentive structures to emphasize quality over quantity, fostering an organizational culture that genuinely values and acts upon safety reports without fear of reprisal, and implementing communication and training programs that help employees internalize the importance of reporting for safety improvement. Ultimately, these efforts will contribute to more effective safety management systems in the aviation industry through increased frequency and quality of confidential safety reports.
Using real-world operational data from an expressway in Shandong Province, China, this study developed a framework for short-term traffic crash risk prediction aimed at routine safety management. Traffic flow, meteorological conditions, and crash records from multiple monitoring sites along the expressway were aggregated into 5-minute intervals and spatiotemporally fused with crash occurrences. Candidate explanatory variables were constructed to characterize upstream and downstream traffic volume, speed, occupancy, visibility, rainfall, temperature, wind, time of day, and other temporal indicators. Recursive Feature Elimination(RFE) was employed to rank variable importance, resulting in the retention of the 15 most informative features within the pre-crash window, thereby highlighting the joint influence of traffic conditions and adverse weather. Within an ensemble learning framework, an expressway crash risk predictor was developed by integrating eXtreme Gradient Boosting(XGBoost) with a Convolutional Neural Network(CNN). XGBoost captures nonlinear relationships among heterogeneous tabular features, while the CNN extracts local temporal patterns from traffic flow sequences. Their probabilistic outputs were combined through an optimized weighting scheme, and model hyperparameters were tuned via cross-validation. For fair comparison, baseline models using only XGBoost and only CNN were trained on the same dataset following identical preprocessing and evaluation protocols. Experimental results indicate that the proposed integrated model achieves an accuracy of 0.96 and an AUC of 0.94 on the test set, outperforming both single models in terms of discrimination and robustness. Feature ablation analyses reveal that excluding meteorological variables results in the greatest performance degradation, with visibility and rainfall identified as the most influential factors. Prediction errors increase significantly under rainy or low-visibility conditions, confirming the critical role of meteorological factors in crash risk prediction. Furthermore, testing on a later time period of the same roadway yielded an accuracy of 87.4%, demonstrating effective out-of-time generalization. Overall, the XGBoost-CNN ensemble provides an accurate, robust, and interpretable tool for estimating short-term crash risk on expressways.
The mobile fan, as a portable smoke exhaust device, serves as an essential backup for smoke control when fixed smoke exhaust systems fail. This paper analyzes the wind field characteristics, wind speed, and wind pressure generated by various combinations of mobile fans(differing in the number of fans, spacing, and lifting height) within a tunnel through numerical simulation methods. The aim is to explore the smoke exhaust capabilities of the combined mobile smoke exhaust system in tunnel environments. The research findings indicate that the lift pressure produced by the combined fans in the tunnel results from the interference and mutual interaction of each fan's jet development. Notably, the actual increase in lift pressure does not follow a simple linear relationship with the number of fans. As the number of fans increases, the boost coefficient of the combined fan also rises, demonstrating that more fan energy is converted into the kinetic energy of the airflow within the tunnel. Conversely, increasing the spacing between the mobile fans reduces the interaction between multiple jets generated by the fans, which subsequently decreases the actual lift pressure and boost coefficient. When forming the fan matrix combination, appropriately elevating the height of the fan matrix enhances the uniformity of the induced airflow over shorter distances, thereby improving the stable wind speed in the pressure ventilation section. Under the conditions investigated in this study, the boost coefficient provided by the combined mobile fan ranged from 0.20 to 0.36. Based on these conclusions, recommendations for optimizing the configuration of the combined mobile smoke exhaust system are proposed. This study provides a foundation for the practical application of mobile fans in tunnels and offers valuable insights for the implementation of mobile smoke gas control equipment.
This study establishes a comprehensive ecological risk assessment framework for Potentially Toxic Elements(PTEs) in industrial parks by integrating plant leaf bioindicators. It addresses existing knowledge gaps in evaluating multi-media migration pathways, including root uptake and foliar absorption, as well as multi-source pollution in complex industrial environments. Three common shrub species—Buxus megistophylla, Pittosporum tobira, and Photinia serrulata—were selected as bioindicators. To ensure consistency in environmental exposure conditions, synchronous sampling of leaves and 0-20 cm soil samples was conducted around the plants at each of the 28 sampling points. Concentrations of Cd, As, Mn, Zn, Cr, Cu, Ni, and Pb were quantified using Inductively Coupled Plasma Mass Spectrometry(ICP-MS). The Bioconcentration Factor(BCF) and Capture Rate(CR) were utilized to assess root uptake and foliar absorption. Ecological risk was evaluated using Håkanson's potential ecological Risk Index(RI). Source apportionment and contribution quantification were performed through Principal Component Analysis-Multiple Linear Regression(PCA-MLR). Results indicated that the average RI of soil PTEs was 115.86, suggesting a moderate overall ecological risk. Cd emerged as the predominant high-risk element, with an average single ecological risk index(E) of 72.42, categorized as moderate risk. Among all 28 sampling points, five exhibited a strong-risk status while three reached very strong-risk levels for this element. Arsenic was identified as the secondary high-risk element, with several samples exceeding the Class Ⅱ land screening value set forth in GB 36600—2018 of China(60 mg/kg). PCA-MLR revealed four sources: Alloy Manufacturing(AM; 44.0% RI contribution), Chemical Industry(CHEM; 30.0%), Thermal Power Generation(TPP; 15.0%), and Traffic(TP; 11.0%). AM and CHEM collectively contributed 74% to the RI, with Cd being the primary emitted element from these sources. Arsenic was chiefly associated with TPP emissions. Notably, thermal power generation and traffic sources contributed more significantly to the total PTEs in soil compared to those in leaves. Pittosporum tobira demonstrated efficient soil Cd bioaccumulation capacity, with a maximum bioconcentration factor reaching 6.42. Moreover, arsenic showed substantial retention of particulate matter-bound PTEs, with an average foliar capture rate of 57%. This indicates that atmospheric particulate matter capture and subsequent absorption are critical exposure pathways for this contaminant. For targeted remediation, Pittosporum tobira is recommended for planting adjacent to AM/CHEM facilities to facilitate the removal of soil Cd through periodic pruning of aboveground biomass. A mixed-shrub configuration, incorporating all three species, is advised throughout the park, particularly downwind of the thermal power plant, to mitigate atmospheric arsenic exposure risks via synergistic particulate matter capture. This study integrates pollution source identification, quantification of bioaccumulation and capture traits, and optimized species selection, thereby providing scientific support for precision management of PTE contamination in industrial parks.
To improve the functional integration, scenario adaptability, and modular coordination of rescue vehicles for hazardous chemical road accidents, this study develops a modular design method integrating the Fuzzy Analytic Hierarchy Process(FAHP), Fuzzy Quality Function Deployment(FQFD), and the Theory of Inventive Problem Solving(TRIZ). First, the emergency rescue process for hazardous chemical road transportation accidents was analyzed, and a hierarchical demand system with 9 primary indicators and 21 secondary indicators was established. Expert judgments were represented by triangular fuzzy numbers, and FAHP was used to calculate the weights of demand indicators. The results show that rapid response capability(0.182), accident-disposal effectiveness(0.160), and personnel safety guarantee(0.158) are the three highest-weighted user demands. Second, FQFD was used to translate weighted demands into technical characteristics. The 21 technical-characteristic modules obtained from preliminary mapping were further refined into 32 evaluation-level characteristics in the House of Quality, allowing comprehensive modules to be decomposed into measurable and configurable design elements. A relationship matrix between the 21 secondary demands and the 32 evaluation-level characteristics was then constructed by 10 engineering experts. The FQFD results indicate that the integrated communication module(0.122), integrated detection module(0.087), and energy power support module(0.077) should be prioritized in design optimization. Third, correlations and key conflicts among technical characteristics were identified through the House of Quality, mapped into TRIZ engineering parameters, and reorganized into seven core design contradictions to derive corresponding inventive principles and implementation strategies. Based on these results, the 32 evaluation-level characteristics were integrated into five functional units: rapid response, core disposal, intelligent support, survival support, and human-machine interaction. The final design achieves a 28% reduction in overall vehicle weight, module switching within 5 min, leak response and disposal within 45 s, a comprehensive driving range of at least 800 km, and a gradeability of 45%. It also defines a Class Ⅲ blast-resistant protection target and reserves interfaces for multi-robot collaborative operation, providing methodological support for the standardization and intelligent upgrading of hazardous chemical rescue equipment.