Search Results (64)

To enhance the understanding of dust diffusion laws in tunnel blasting operations of metal mines and determine optimal ventilation dust removal times, a scaled physical model of a metal mine tunneling face under the China Zijin Mining Group was established based on field measurements. Numerical simulation was employed to investigate airflow movement and dust migration in the tunneling roadway, and the fundamental features of airflow field and dust diffusion laws after tunnel blasting operations in the fully mechanized excavation face were revealed. The effects of three main factors included airflow rate (Q), ventilation distance (S), and tunnel length (L) on the dust removal time after tunnel blasting operations were investigated based on the orthogonal design method. Results indicated that reducing the dust concentration in the roadway to 10 mg/m³ required 53 min. The primary factors influencing dust removal time, in order of significance, were determined to be L, Q, and S. The lowest dust concentration occurs when the ventilation distance was 25 m. A predictive model for dust removal time after tunnel blasting operations was developed, establishing the relationship between dust removal time and the three factors as T = 20.7Q^−0.73S^0.19L^0.86. Subsequent on-site validation confirmed the high accuracy of the predictive model, demonstrating its efficacy for practical applications. This study contributes a novel integration of orthogonal experimental design and validated CFD modeling to predict ventilation dust removal time, offering a practical and theoretically grounded approach for tunnel ventilation optimization. Full article

To elucidate evolutionary characteristics of the surrounding rock failure mechanism in a double-roadway layout, this work is grounded on in the research context of the Jinjitan Coal Mine, focusing on the deformation and failure mechanisms of double roadways. This paper addresses the issue of resource wastage resulting from the excessive dimensions of coal pillars in prior periods by employing a research methodology that integrates theoretical analysis, numerical simulation, and field monitoring to systematically examine the movement characteristics of overlying rock in the working face. On that basis, the size of coal pillar is optimized. The advance’s stress transfer law and deformation distribution characteristics of the return air roadway and transport roadway are studied. The cause of the asymmetric deformation of roadway retention is explained. A differentiated design is conducted on the support parameters of double-roadway bolts and cables under strong dynamic pressure conditions. The study indicates that a 16 m coal pillar results in an 8 m elastic zone at its center, balancing stability with optimal resource extraction. In the basic top-sloping double-block conjugate masonry beam structure, the differing stress levels between the top working face’s transport roadway and the lower working face’s return air roadway are primarily due to the varied placements of key blocks. In the return air roadway, floor heave deformation is managed using locking anchor rods, while roof subsidence is controlled with a constant group of large deformation anchor cables. The displacement of surrounding rock increases under the influence of both leading and lagging pressures from the previous working face, although the change is minimal. There is a significant correlation between roadway deformation and support parameters and coal pillar size. With a 16 m coal pillar, differential support of the double roadway lowers the return air roadway deformation by 30%, which improves the mining rate and effectively controls the deformation of the roadway. Full article

Construction project durations specified on contracts are influenced by adverse weather conditions such as rainfall and low temperatures. This study aimed to develop an efficient method for determining monthly Average Available Workdays (AAWDs) for roadway construction projects using historical long-term (ten years or more) local weather data. A survey was conducted to understand the status of current practices using weather information for contract time determination by transportation agencies. Excel spreadsheet tools with Visual Basic for Applications (VBA) programs were developed to process the downloaded long-term weather data with two different formats. Instead of manually processing the short-term (e.g., one–three years) weather data, VBA programs efficiently count for weekends, legal holidays, and adverse weather days as non-workdays each month over the years with weather data (>10 years) and then determine the monthly available workdays (AWDs) and AAWDs. This method was verified using daily records from five completed roadway construction projects. Many contractor-claimed non-workdays due to other factors, not weather-related, that contributed to substantially longer project duration affect the comparison of AWDs determined from nearby weather stations using the developed VBA tools. The method and VBA tools developed were applied to 88 weather stations (10–122 years, average 42 years of data) to determine AAWDs in Alabama, USA, as a case study. Monthly AAWDs in Alabama were grouped into three climate zones: North Region, Central Regions, and South Regions with 185, 193, and 200 AAWDs per year, respectively, with more workdays (17–19 days) in warmer months and fewer (9–11 days) in colder months. The determined AAWDs help both DOTs and construction contractors determine/propose reasonable construction project durations and resolve the construction delay issues. The method and VBA tools can be revised/updated by other DOTs and construction companies for different definitions and thresholds on non-workdays and then efficiently determine AWDs and AAWDs using long-term local weather data. Full article

Ground-based interferometric radar (GBIR) is a powerful remote sensing technique used for infrastructure monitoring, particularly in the field of bridge structural health monitoring (SHM). Despite its high resolution and rapid data acquisition and the availability of various commercial systems, GBIR has not yet been fully recognised or routinely adopted in standard bridge monitoring practices. This study presents a comprehensive review of GBIR technologies and methods historically applied in bridge SHM. A total of 104 peer-reviewed papers were selected through a systematic review process, encompassing 128 monitored bridges assessed using a wide range of GBIR systems. The applications of GBIR across different bridge materials and operational conditions are discussed in detail. The review shows that 76% of GBIR applications focus on roadway and railway bridges. In terms of materials, steel and concrete bridges dominate the dataset, accounting for 95% of the total, while masonry bridges represent only 5%. The GBIR system types examined in this study are categorised into six main groups based on their technical specifications, with their key characteristics and capabilities analysed. The review also investigates bridge feature extraction techniques, revealing a predominant focus on identifying natural frequencies, while fewer studies explore the extraction of damping ratios and structural mode shapes. Furthermore, the integration of GBIR with other sensing technologies—particularly accelerometers—is explored, highlighting opportunities for complementary sensor fusion. Overall, this study provides a comprehensive overview of the current state of practice and identifies key areas for future research and technological development. Full article

In developing a digital twin of a real structure, finite element model updating (FEMU) is essential for refining the model’s response based on measured data, enabling the detection of structural damage or hidden reserves over time. This case study focused on a 40-year-old multi-span concrete roadway bridge, equipped with permanent bridge weigh-in-motion (B-WIM) and structural health monitoring (SHM) systems. Bridge responses from two calibration vehicles were used to derive strain influence lines (ILs) from mid-span B-WIM strain transducers mounted on the main girders. The error-domain model falsification (EDMF) methodology was applied to perform strain IL-based FEMU and the more conventional frequency-based, MAC-based, and combined frequency and MAC-based FEMU. Boundary conditions and three Young’s modulus adjustment factors, representing different groups of structural elements, were updated. The strain IL-based updated FE model, with averages of 35% and 50% stiffness increases for the two main girders, showed strong agreement with independently measured mid-span vertical displacements. Maximum values deviated not more than 5%. In contrast, the frequency and MAC-based updated FE model underestimated displacements by 25–30%. These findings highlight the potential of using B-WIM for FEMU and SHM on such types of bridges, particularly when the response under traffic load is of interest. Full article

This study selected green spaces from three residential areas in Hefei as the research subjects, combining behavioral observation methods and a natural experiment to collect behavioral data from 2010 and 2024. The data were then compared using Poisson regression models. Additionally, home visits were conducted to gather residents’ perceptions of the factors contributing to the decline in vitality. Based on the survey data, multilevel regression analysis was performed to explore the decline in RQGS usage vitality and its influencing factors in the context of rapid urbanization. This study found a significant decline in green space visits, particularly during the afternoon (16:00–18:00) and in areas adjacent to roadways. The main influencing factors include emerging leisure choices (such as taking the subway to large parks or preferring indoor activities) and residents’ satisfaction with RQGS characteristics (such as functional zoning, noise pollution, and neighborhood familiarity). Notably, there was no significant correlation between “disposable leisure time” and visit frequency. These findings suggest that, despite the inherent advantages of proximity, the vitality of RQGS faces increasing challenges due to emerging diverse leisure demands and growing environmental disturbances. In contrast to the traditional emphasis on accessibility, this study recommends that future RQGS planning prioritize functional zoning (e.g., dog-walking areas, sports zones), address the needs of vulnerable groups, and focus on mitigating vehicle noise and air pollution rather than merely expanding parking facilities. Interventions should be scheduled for the afternoon and emphasize strengthening community interaction and cohesion to enhance user experience. This research provides valuable scientific evidence and practical guidance for urban planners and policymakers to optimize residential green spaces in the context of rapid urbanization, offering new perspectives for the empirical evaluation of RQGS upgrades. Full article

Mobile source air toxics (MSATs) are major contributors to urban air pollution, especially near high-traffic roadways, where populations face elevated pollutant exposures. Traditional human health risk assessments, based on deterministic methods, often overlook variability in exposure and the vulnerabilities of sensitive subpopulations. This study introduces and applies a new stochastic modeling approach, utilizing Monte Carlo simulations to evaluate cumulative cancer risks from MSATs exposure through inhalation and ingestion pathways. This method captures variability in exposure scenarios, providing detailed health risk assessments, particularly for vulnerable groups such as children and the elderly. This approach was demonstrated in a case study conducted in Saint Paul, Minnesota, using 2019 traffic data. Deterministic models estimated cumulative cancer risks for adults at 6.24E-02 (unitless lifetime cancer risk), while stochastic modeling revealed a broader range, with the 95th percentile reaching 4.98E-02. The 95th percentile, used in regulatory evaluations, identifies high-risk scenarios overlooked by deterministic methods. This research advances the understanding of MSATs exposure risks by integrating spatiotemporal dynamics, identifying high-risk zones and vulnerable subpopulations, and supporting resource allocation for targeted pollution control measures. Future applications of this methodology include expanding stochastic modeling to evaluate ecological risks from mobile emissions. Full article

Roadway mortality increased during COVID-19, reversing a multi-decade downward trend. The Fatality Analysis Reporting System (FARS) was used to examine contributing factors pre-COVID-19 and in the COVID-19 era using the five pillars of the Safe System framework: (1) road users; (2) vehicles; (3) roadways; (4) speed; and (5) post-crash care. Two study time periods were matched to control for seasonality differences pre-COVID-19 (n = 1725, 1 April 2018–31 December 2019) and in the COVID-19 era (n = 2010, 1 April 2020–31 December 2021) with a three-month buffer period between the two time frames excluded. Four of the five pillars of the safe system had road safety indicators that worsened during the pandemic. Mortality was 19.7% higher for motor vehicle occupants and 45.1% higher for riders of motorized two-wheeled vehicles. In adjusted analyses, failure to use safety equipment (safety belts/helmets) was associated with 44% higher mortality. Two road user groups, non-motorized bicyclists and pedestrians, did not contribute significantly to higher mortality. Urban roadway crashes were higher compared to rural crashes. Additional scientific inquiry into factors associated with COVID-19-era mortality using the Safe System framework yielded important scientific insights to inform prevention efforts. Motorized two-wheeled vehicles contribute disproportionately to pandemic-era higher mortality and constitute an emerging road safety issue that deserves further attention. Full article

This document introduces a novel equivalent algorithm for forecasting gas content within deep coal seams, which is subject to constraints stemming from the advancements and precision achieved in well and roadway engineering endeavors. This algorithm meticulously acknowledges that coal seam gas content comprises three fundamental components: the inherent gas emission rate of the equivalent stratum, the residual gas content retained within the coal seam itself, and the influence imparted by the gas content within the coal seam. Furthermore, the approach thoroughly considers variations in the level of porosity development within the coal seam and its surrounding rock formations, as well as the occurrence of gas within these structures. The equivalent layer is classified into two distinct groups: the sandstone zone and the clay zone. The sandstone zone utilizes pertinent parameters pertaining to fine sandstone, whereas the clay zone distinguishes between clay rock and thick mudstone. The influencing factor considerations solely encompass natural elements, such as the coal seam’s occurrence and geological structure. The residual gas content employs either existing measured parameters or acknowledged experimental parameters specific to the coal seam. Based on this predictive approach, an intelligent auxiliary software (V1.0) for mine gas forecasting was devised. The software calculates the gas content of deep coal seams within the mine at intervals of 100 m × 100 m, subsequently fitting the contour lines of gas content across the entire area. The gas content predictions derived from this equivalent algorithm demonstrate robust adaptability to variations in gas content caused by construction activities, and the prediction results exhibit an acceptable level of error on-site. Notably, the prediction process is not constrained by the progress of tunnel engineering, ensuring that the prediction outcomes can accurately represent the distribution characteristics of deep coal seam gas content. After a year of application, the prediction results have consistently met on-site requirements, providing a scientific foundation for the implementation of effective gas prevention and control measures in the mining area. Furthermore, this approach can effectively guide the formulation of medium- and long-term gas prevention and control plans for mines. Full article

This research investigates the characterization of bituminous mixes utilizing microwave imaging and non-destructive testing. We studied the electromagnetic characteristics of various samples, including bituminous concrete (BC) and open-grade friction course (OGFC) samples. A novel ring filter with log-periodic feedlines, designed on the RT/Duroid 5880 substrate, was utilized within the frequency range of 0.3–0.7 GHz. The samples were assessed using average attenuation and group delay measures, which detailed clear electromagnetic characteristics. The samples’ flow value and specific gravity were correlated to these parameters. The calculated flow value and specific gravity (using the filter) and measured flow value and specific gravity (using the conventional method) coincided well. The filter could predict the parameters of the samples with a high accuracy of roughly 99.8% for the flow value and specific gravity, whereas the OGFC sample displayed an accuracy of 99.7%, correspondingly, as shown in high R² values. This demonstrates that the filter can precisely measure the parameters required for studying the interaction between the binder and aggregate in bituminous mixes without being invasive. The findings indicate a significant disparity between OGFC and BC samples in their responses to electromagnetic fields and their characteristics. This demonstrates the high sensitivity and significant value of microwave techniques in the study of bitumen and the construction of roadways. Full article

Pipeline leakage seriously threatens the efficient and safe gas drainage in coal mines. To achieve the accurate detection and localization of gas drainage pipeline leakages, this study proposes a gas drainage pipeline leakage detection and localization approach based on the pressure gradient method. Firstly, the basic law of gas flow in the drainage pipeline was analyzed, and a pipeline network resistance correction formula was deduced based on the pressure gradient method. Then, a drainage pipeline model was established based on the realizable $k\text{-}\epsilon$ turbulence model, and the pressure and flow velocity distribution during pipeline leakage under different leakage degrees, leakage locations, and pipeline negative pressures were simulated and analyzed, thus verifying the feasibility of the pipeline leakage detection and localization method. It is concluded that the positioning errors of pipeline leakage points under different leakage degrees, different leakage positions, and different pipeline negative pressures were 0.88~1.08%, 0.88~1.49%, and 0.68~0.88%, respectively. Finally, field tests were conducted in the highly located drainage roadway 8421 of the Fifth Mine of Yangquan Coal Industry Group to verify the accuracy of the proposed pipeline leakage detection and localization method, and the relative error was about 8.2%. The results show that with increased pipeline leakage hole diameters, elevated pipeline negative pressures, and closer leakage positions to the pipeline center, the relative localization error was smaller, the localization accuracy was higher, and the stability was greater. The research results could lay the foundation for the fault diagnosis and localization of coal mine gas drainage pipeline networks and provide technical support for safe and efficient coal mine gas drainage. Full article

This study aims to improve the accuracy of predicting the severity of traffic accidents by developing an innovative traffic accident risk prediction model—StackTrafficRiskPrediction. The model combines multidimensional data analysis including environmental factors, human factors, roadway characteristics, and accident-related meta-features. In the model comparison, the StackTrafficRiskPrediction model achieves an accuracy of 0.9613, 0.9069, and 0.7508 in predicting fatal, serious, and minor accidents, respectively, which significantly outperforms the traditional logistic regression model. In the experimental part, we analyzed the severity of traffic accidents under different age groups of drivers, driving experience, road conditions, light and weather conditions. The results showed that drivers between 31 and 50 years of age with 2 to 5 years of driving experience were more likely to be involved in serious crashes. In addition, it was found that drivers tend to adopt a more cautious driving style in poor road and weather conditions, which increases the margin of safety. In terms of model evaluation, the StackTrafficRiskPrediction model performs best in terms of accuracy, recall, and ROC–AUC values, but performs poorly in predicting small-sample categories. Our study also revealed limitations of the current methodology, such as the sample imbalance problem and the limitations of environmental and human factors in the study. Future research can overcome these limitations by collecting more diverse data, exploring a wider range of influencing factors, and applying more advanced data analysis techniques. Full article

This study focuses on the renovation and construction of compressed air energy storage chambers within abandoned coal mine roadways. The transient mechanical responses of underground gas storage chambers under a cycle are analyzed through thermal-solid coupling simulations. These simulations highlight changes in key parameters such as displacement, stress, and temperature within the chamber group during the loading and unloading processes of compressed air energy storage. It is found that within a cycle, the small circular chamber experiences the most significant deformation, with an average peak displacement of 0.24 mm, followed by the large circular chamber and horseshoe-shaped tunnels. The small circular chamber exhibits maximum tensile and compressive stresses. Therefore, special attention in engineering practice should be paid to the long-term safety and stability of small circular tunnels, and the stability of horseshoe-shaped tunnels should be also carefully considered. The findings from this study offer some insights for theoretical support and practical implementation in the planning, design, construction, and operation of high-pressure underground gas storage chambers for compressed air energy storage. Full article

This study utilizes multilevel ordinal logistic regression (M-OLR), an approach that accounts for spatial heterogeneity, to assess the dynamics of crash severity in Virginia, USA, over the years 2018 to 2023. This period was notably influenced by the COVID-19 pandemic and its associated stay-at-home orders, which significantly altered traffic behaviors and crash severity patterns. This study aims to evaluate the pandemic’s impact on crash severity and examine the consequent changes in driver behaviors. Despite a reduction in total crashes, a worrying increase in the proportion of severe injuries is observed, suggesting that less congested roads during the pandemic led to riskier driving behaviors, notably increased speed violations. This research also highlights heightened risks for vulnerable road users such as pedestrians, cyclists, and motorcyclists, with changes in transportation habits during the pandemic leading to more severe crashes involving these groups. Additionally, this study emphasizes the consistent influence of environmental and roadway features, like weather conditions and traffic signals, in determining crash outcomes. These findings offer vital insights for road safety policymakers and urban planners, indicating the necessity of adaptive road safety strategies in response to changing societal norms and behaviors. The research underscores the critical role of individual behaviors and mental states in traffic safety management and advocates for holistic approaches to ensure road safety in a rapidly evolving post-pandemic landscape. Full article

Although the literature provides valuable insight into tornado vulnerability and resilience, there are still research gaps in assessing tornadoes’ impact on communities and transportation infrastructure, especially in the wake of the rapidly changing frequency and strength of tornadoes due to climate change. In this study, we first investigated the relationship between tornado exposure and demographic-, socioeconomic-, and transportation-related factors in our study area, the state of Kentucky. Tornado exposures for each U.S. census block group (CBG) were calculated by utilizing spatial analysis methods such as kernel density estimation and zonal statistics. Tornadoes between 1950 and 2022 were utilized to calculate tornado density values as a surrogate variable for tornado exposure. Since tornado density varies over space, a multiscale geographically weighted regression model was employed to consider spatial heterogeneity over the study region rather than using global regression such as ordinary least squares (OLS). The findings indicated that tornado density varied over the study area. The southwest portion of Kentucky and Jefferson County, which has low residential density, showed high levels of tornado exposure. In addition, relationships between the selected factors and tornado exposure also changed over space. For example, transportation costs as a percentage of income for the regional typical household was found to be strongly associated with tornado exposure in southwest Kentucky, whereas areas close to Jefferson County indicated an opposite association. The second part of this study involves the quantification of the tornado impact on roadways by using two different methods, and results were mapped. Although in both methods the same regions were found to be impacted, the second method highlighted the central CBGs rather than the peripheries. Information gathered by such an investigation can assist authorities in identifying vulnerable regions from both transportation network and community perspectives. From tornado debris handling to community preparedness, this type of work has the potential to inform sustainability-focused plans and policies in the state of Kentucky. Full article