Search Results (81)

The behavior of gas emissions at the heading face of the coal mine is a key indicator of potentially harmful gas disaster risk, necessitating in-depth study via analytical and statistical methods. However, conventional prediction and evaluation methods depend on long-interval statistical data, which are too coarse for and lack the immediacy required for real-time applications. Based on the physical laws of gas storage and flow, a refined computational model has been developed to compute dynamic gas emission rates that vary with geology and excavating process. Furthermore, by comparing the computed outputs with actual monitoring data, it becomes possible to assess whether abnormal gas emissions are occurring. Methodologically, this model first applies the finite difference method to compute the dynamic gas flux and the dynamic residual gas content. It then determines the exposure duration of each segment of the roadway wall at any given moment, as well as the mass of newly dislodged coal. The total gas emission rate at a specific sensor location is obtained by aggregating the contributions from all of the exposed wall and the freshly dislodged coal. Owing to some simplifications, the model’s applicability is currently restricted to single, medium-thick coal seams. The model was preliminarily implemented in Python (3.13.2) and validated against a case study of an active heading face. The results demonstrate a strong concordance between model predictions and field measurements. The model notably captures the significant variance in emission rates resulting from different mining activities, the characteristic emission surges from dislodged coal and newly exposed coal walls, and the influence of sensor placement on monitoring outcomes. Full article

With accelerating urbanization and the exponential growth in vehicle populations, high-precision traffic monitoring has become indispensable for intelligent transportation systems (ITSs). Conventional sensing technologies—including inductive loops, cameras, and radar—suffer from inherent limitations: restrictive spatial coverage, prohibitive installation costs, and vulnerability to adverse weather. Distributed Acoustic Sensing (DAS), leveraging Rayleigh backscattering to convert standard optical fibers into kilometer-scale, real-time vibration sensor networks, presents a transformative alternative. This paper provides a comprehensive review of the principles and system architecture of DAS for roadway traffic monitoring, with a focus on signal processing techniques, feature extraction methods, and recent advances in vehicle detection, classification, and speed/flow estimation. Special attention is given to the integration of deep learning approaches, which enhance noise suppression and feature recognition under complex, multi-lane traffic conditions. Real-world deployment cases on highways, urban roads, and bridges are analyzed to demonstrate DAS’s practical value. Finally, this paper delineates emerging research trends and technical hurdles, providing actionable insights for the scalable implementation of DAS-enhanced ITS infrastructures. Full article

As an important traffic and transportation roadway, tunnel engineering is widely used in important fields such as highways, railways, water conservancy, subways and mining. It is limited by complex geological conditions, harsh construction environments and poor robustness of the monitoring system. If the construction process and monitoring method are not properly designed, it will often directly induce disasters such as tunnel deformation, collapse, leakage and rockburst. This seriously threatens the safety of tunnel construction and operation and the protection of the regional ecological environment. Therefore, based on distributed fiber optic sensing technology, the full–cycle spatiotemporally continuous sensing information of the tunnel structure is obtained in real time. Accordingly, the health status of the tunnel is dynamically grasped, which is of great significance to ensure the intrinsic safety of the whole life cycle for the tunnel project. Firstly, this manuscript systematically sorts out the development and evolution process of the theory and technology of structural health monitoring in tunnel engineering. The scope of application, advantages and disadvantages of mainstream tunnel engineering monitoring equipment and main optical fiber technology are compared and analyzed from the two dimensions of equipment and technology. This provides a new path for clarifying the key points and difficulties of tunnel engineering monitoring. Secondly, the mechanism of action of four typical optical fiber sensing technologies and their application in tunnel engineering are introduced in detail. On this basis, a spatiotemporal continuous perception method for tunnel engineering based on DFOS is proposed. It provides new ideas for safety monitoring and early warning of tunnel engineering structures throughout the life cycle. Finally, a high–speed rail tunnel in northern China is used as the research object to carry out tunnel structure health monitoring. The dynamic changes in the average strain of the tunnel section measurement points during the pouring and curing period and the backfilling period are compared. The force deformation characteristics of different positions of tunnels in different periods have been mastered. Accordingly, scientific guidance is provided for the dynamic adjustment of tunnel engineering construction plans and disaster emergency prevention and control. At the same time, in view of the development and upgrading of new sensors, large models and support processes, an innovative tunnel engineering monitoring method integrating “acoustic, optical and electromagnetic” model is proposed, combining with various machine learning algorithms to train the long–term monitoring data of tunnel engineering. Based on this, a risk assessment model for potential hazards in tunnel engineering is developed. Thus, the potential and disaster effects of future disasters in tunnel engineering are predicted, and the level of disaster prevention, mitigation and relief of tunnel engineering is continuously improved. Full article

Energy harvesting systems (EHSs) are widely used to power wireless sensors. Piezoelectric harvesters have the advantage of producing an electric signal directly related to the exciting force and can thus be used to power condition monitoring sensors in dynamically loaded structures such as bridges. The need for such monitoring is exemplified by the fact that the condition of close to 25% of public roadway bridges in, e.g., Germany is not satisfactory. Stack-based piezoelectric energy harvesting systems (pEHSs) installed near bridge bearings could provide information about the traffic and dynamic loads on the one hand and condition-dependent changes in the bridge characteristics on the other. This paper presents an approach to co-optimizing the design of the mechanical and electrical components using a nonlinear solver. Such an approach has not been described in the open literature to the best of the authors’ knowledge. The mechanical excitation is estimated through a finite element simulation, and the electric circuitry is modeled in Simulink to account for the nonlinear characteristics of rectifying diodes. We use real traffic data to create statistical randomized scenarios for the optimization and statistical variation. A main result of this work is that it reveals the strong dependence of the energy output on the interaction between bridge, harvester, and traffic details. A second result is that the methodology yields design criteria for the harvester such that the energy output is maximized. Through the case study of an actual middle-sized bridge in Germany, we demonstrate the feasibility of harvesting a time-averaged power of several milliwatts throughout the day. Comparing the total amount of harvested energy for 1000 randomized traffic scenarios, we demonstrate the suitability of pEHS to power wireless sensor nodes. In addition, we show the potential sensory usability for traffic observation (vehicle frequency, vehicle weight, axle load, etc.). Full article

Traffic congestion poses a significant challenge to modern urban environments, impacting both driver satisfaction and road safety. This paper investigates the effectiveness of a smart traffic light system (STL), a solution developed under the Intelligent Transportation System (ITS) initiative by the Ministry of Works Malaysia, to address these issues in Malaysia. The system integrates a network of sensors, AI-enabled cameras, and Automatic Number Plate Recognition (ANPR) technology to gather real-time data on traffic volume and vehicle classification at congested intersections. This data is utilized to dynamically adjust traffic light timings, prioritizing traffic flow on heavily congested roads while maintaining safety standards. To evaluate the system’s performance, a comprehensive study was conducted at a selected intersection. Traffic patterns were automatically analyzed using camera systems, and the performance of the STL was compared to that of traditional traffic signal systems. The average travel time from the start to the end intersection was measured and compared. Preliminary findings indicate that the STL significantly reduces travel times and improves overall traffic flow at the intersection, with average travel time reductions ranging from 7.1% to 28.6%, depending on site-specific factors. While further research is necessary to quantify the full extent of the system’s impact, these initial results demonstrate the promising potential of STL technology to enhance urban mobility and more efficient and safer roadways by moving beyond traditional traffic signal functionalities. Full article

Piezoelectric energy harvesting in roadways can power distributed sensors and electronics by capturing underutilized mechanical energy from traffic. In this research, 1-3 stacked piezocomposites were developed and evaluated to determine optimal designs for multiple applications. The design of these transducers aimed at operating in a multitude of scenarios, under compressive loads (1–10 kN) at low-frequency (10 Hz) applications, intended to simulate vehicular forces. Power comparison was utilized between numerous transducers to determine the most efficient configuration for electromechanical energy conversion. Design guidelines were based on mechanical integrity, output power, active piezoelectric volume percentage, aspect ratio, and geometric factors. The forces applied in this study were reliant on the average vehicle weight. An intermediate PZT volume fraction and moderate pillar aspect ratios were found to yield the highest power output, with the stacked 1-3 composite significantly outperforming a monolithic PZT of a similar size. Full article

This study focuses on the importance of the real-time monitoring of bolt loads in roadways affected by high-intensity mining and the limitations of conventional monitoring methods. Fiber Bragg grating (FBG) sensors were embedded and encapsulated in rock bolts, and tensile tests were conducted indoors to verify their feasibility. The research was conducted using the consolidated face of the Bultai Coal Mine in the Shendong Mining Area as the engineering background. Real-time monitoring wavelength data from the FBG bolt sensor were obtained through field tests. The analysis of the data aimed to assess the condition of the FBG sensor and variations in axial force within the service bolts of the mining roadway. Using these monitoring results, the real-time stability and safety of the roadway bolts were evaluated. The study indicates that as the working face advances, the axial force in the bolt progressively rises under the effect of mine pressure. The left gang bolt rod’s shaft force changes significantly, while the right gang’s change is relatively small. When the working face moves 60 m past the bolt rod, the axial force in the bolt rises sharply. Moreover, the axial force at different positions of the left and right gang bolts exhibits a distinct variation pattern. The real-time monitoring of bolt support in the return roadway provides essential data for assessing bolt safety. Full article

Underground coal mines face increasing challenges in the scheduling of Electric Rubber-Tired Vehicles (ERTVs) due to confined spaces, dynamic production demands, and the need to coordinate multiple constraints such as complex roadway topologies, strict time windows, and limited charging resources in the context of clean energy transitions. This study presents a Constraint Programming (CP)-based optimization framework that integrates Virtual Charging Station Mapping (VCSM) and sensor fusion positioning to decouple spatiotemporal charging conflicts and applies a dynamic topology adjustment algorithm to enhance computational efficiency. A novel RFID–vision fusion positioning system, leveraging multi-source data to mitigate signal interference in underground environments, provides real-time, reliable spatiotemporal coordinates for the scheduling model. The proposed multi-objective model systematically incorporates hard time windows, load limits, battery endurance, and roadway regulations. Case studies conducted using real-world data from a large-scale Chinese coal mine demonstrate that the method achieves a 17.6% reduction in total transportation mileage, decreases charging events by 60%, and reduces vehicle usage by approximately 33%, all while completely eliminating time window violations. Furthermore, the computational efficiency is improved by 54.4% compared to Mixed-Integer Linear Programming (MILP). By balancing economic and operational objectives, this approach provides a robust and scalable solution for sustainable ERTV scheduling in confined underground environments, with broader applicability to industrial logistics and clean mining practices. 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

As transportation systems evolve, ensuring safe and efficient mobility in Intelligent Transportation Systems remains a priority. Work zones, in particular, pose significant safety challenges due to lane closures, which can lead to abrupt braking and sudden lane changes. Most previous research on Connected and Autonomous Vehicles (CAVs) assumes ideal communication conditions, overlooking the effects of message loss and network unreliability. This study presents a comprehensive smart work zone (SWZ) framework that enhances lane-change safety by the integration of both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. Sensor-equipped SWZ barrels and Roadside Units (RSUs) collect and transmit real-time hazard alerts to approaching CAVs, ensuring coverage of critical roadway segments. In this study, a co-simulation framework combining VEINS, OMNeT++, and SUMO is implemented to assess lane-change safety and communication performance under realistic network conditions. Findings indicate that higher Market Penetration Rates (MPRs) of CAVs can lead to improved lane-change safety, with time-to-collision (TTC) values shifting toward safer time ranges. While lower transmission thresholds allow more frequent communication, they contribute to earlier network congestion, whereas higher thresholds maintain efficiency despite increased packet loss at high MPRs. These insights highlight the importance of incorporating realistic communication models when evaluating traffic safety in connected vehicle environments. Full article

Recently, the development of autonomous vehicles (AVs) and intelligent driver assistance systems has drawn significant attention from the public. Despite these advancements, AVs may encounter critical situations in real-world scenarios that can lead to severe traffic accidents. This review paper investigated these critical scenarios, categorizing them under weather conditions, environmental factors, and infrastructure challenges. Factors such as attenuation and scattering severely influence the performance of sensors and AVs, which can be affected by rain, snow, fog, and sandstorms. GPS and sensor signals can be disturbed in urban canyons and forested regions, which pose vehicle localization and navigation problems. Both roadway infrastructure issues, like inadequate signage and poor road conditions, are major challenges to AV sensors and navigation systems. This paper presented a survey of existing technologies and methods that can be used to overcome these challenges, evaluating their effectiveness, and reviewing current research to improve AVs’ robustness and dependability under such critical situations. This systematic review compares the current state of sensor technologies, fusion techniques, and adaptive algorithms to highlight advances and identify continuing challenges for the field. The method involved categorizing sensor robustness, infrastructure adaptation, and algorithmic improvement progress. The results show promise for advancements in dynamic infrastructure and V2I systems but pose challenges to overcoming sensor failures in extreme weather and on non-maintained roads. Such results highlight the need for interdisciplinary collaboration and real-world validation. Moreover, the review presents future research lines to improve how AVs overcome environmental and infrastructural adversities. This review concludes with actionable recommendations for upgrading physical and digital infrastructures, adaptive sensors, and algorithmic upgrades. Such research is important for AV technology to remain in the zone of advancement and stability. Full article

While automated-driving technology is advancing rapidly, human-centered research is still in its early stages. Research on negative user responses to automated driving is particularly limited in complex roadway environments such as roundabouts, where driving decisions typically depend on driver judgment and traffic conditions. In these environments, automated-driving vehicles may exhibit unstable behaviors, such as sudden stops or forced intersection entries. Since successful interaction between users and automated systems is critical for widespread adoption, understanding when and how automated driving negatively affects users is essential. This study investigated user psychological responses and corresponding physiological changes during unstable automated-driving situations. Using a virtual environment driving simulator, we compared two scenarios: sensor-only automated driving (A.D(S)), which exhibited unstable driving patterns; and cooperative automated driving (A.D(C)), which achieved more stable performance through infrastructure communication. We analyzed the responses of 30 participants using electromyography (EMG) measurements and pupil diameter tracking, supplemented by qualitative evaluations. Results showed that A.D(S) participants experienced higher levels of frustration during prolonged waiting times compared to A.D(C) participants. In addition, sudden braking events elicited startle responses characterized by pupil dilation and elevated arm-muscle EMG readings. This research advances our understanding of how automated-driving behaviors affect user experience and emphasizes the importance of human factors in the development of automated-driving technologies. Full article

Ambient air pollution is the most important environmental factor impacting human health. Urban landscapes present unique air quality challenges, which are compounded by climate change adaptation challenges, as air pollutants can also be affected by the urban heat island effect, amplifying the deleterious effects on health. Nature-based solutions have shown potential for alleviating environmental stressors, including air pollution and heat wave abatement. However, such solutions must be designed in order to maximize mitigation and not inadvertently increase pollutant exposure. This study aims to demonstrate potential applications of nature-based solutions in urban environments for climate stressors and air pollution mitigation by analyzing two distinct scenarios with and without green infrastructure. Utilizing low-cost sensors, we examine the relationship between green infrastructure and a series of environmental parameters. While previous studies have investigated green infrastructure and air quality mitigation, our study employs low-cost sensors in tropical urban environments. Through this novel approach, we are able to obtain highly localized data that demonstrates this mitigating relationship. In this study, as a part of the NERC-FAPESP-funded GreenCities project, four low-cost sensors were validated through laboratory testing and then deployed in two locations in São Paulo, Brazil: one large, heavily forested park (CIENTEC) and one small park surrounded by densely built areas (FSP). At each site, one sensor was located in a vegetated area (Park sensor) and one near the roadside (Road sensor). The locations selected allow for a comparison of built versus green and blue areas. Lidar data were used to characterize the profile of each site based on surrounding vegetation and building area. Distance and class of the closest roadways were also measured for each sensor location. These profiles are analyzed against the data obtained through the low-cost sensors, considering both meteorological (temperature, humidity and pressure) and particulate matter (PM₁, PM_2.5 and PM₁₀) parameters. Particulate matter concentrations were lower for the sensors located within the forest site. At both sites, the road sensors showed higher concentrations during the daytime period. These results further reinforce the capabilities of green–blue–gray infrastructure (GBGI) tools to reduce exposure to air pollution and climate stressors, while also showing the importance of their design to ensure maximum benefits. The findings can inform decision-makers in designing more resilient cities, especially in low-and middle-income settings. Full article

The focus of this study is to investigate the underexplored operational effects of disengagements on the speed of an automated shuttle, providing novel insights into their disruptive impact on performance metrics. For this purpose, global positioning system data, disengagement records, weather reports, and roadway geometry data from an automated shuttle pilot program, from July to December 2023, at the University of North Carolina in Charlotte, were collected. The automated shuttle uses sensors for localization, navigation, and obstacle detection. A multi-level mixed-effects Gaussian regression model with a log-link function was employed to analyze the effect of disengagement events on the automated shuttle speed, while accounting for control variables such as roadway geometry, weather conditions, time-of-the-day, day-of-the-week, and number of intermediate stops. When these variables are controlled, disengagements significantly reduce the automated shuttle speed, with the expected log of speed decreasing by 0.803 units during such events. This reduction underscores the disruptive impact of disengagements on the automated shuttle’s performance. The analysis revealed substantial variability in the effect of disengagements across different route segments, suggesting that certain segments, likely due to varying traffic conditions, road geometries, and traffic control characteristics, pose greater challenges for autonomous navigation. By employing a multi-level mixed-effects model, this study provides a robust framework for quantifying the operational impact of disengagements. The findings serve as vital insights for advancing the reliability and safety of autonomous systems through targeted improvements in technology and infrastructure. Full article

Accurate prediction of methane concentration in mine roadways is crucial for ensuring miner safety and enhancing the economic benefits of mining enterprises in the field of coal mine safety. Taking the Buertai Coal Mine as an example, this study employs laser methane concentration monitoring sensors to conduct precise real-time measurements of methane concentration in coal mine roadways. A prediction model for methane concentration in coal mine roadways, based on an Improved Black Kite Algorithm (IBKA) coupled with Informer-BiLSTM, is proposed. Initially, the traditional Black Kite Algorithm (BKA) is enhanced by introducing Tent chaotic mapping, integrating dynamic convex lens imaging, and adopting a Fraunhofer diffraction search strategy. Experimental results demonstrate that the proposed improvements effectively enhance the algorithm’s performance, resulting in the IBKA exhibiting higher search accuracy, faster convergence speed, and robust practicality. Subsequently, seven hyperparameters in the Informer-BiLSTM prediction model are optimized to further refine the model’s predictive accuracy. Finally, the prediction results of the IBKA-Informer-BiLSTM model are compared with those of six reference models. The research findings indicate that the coupled model achieves Mean Absolute Errors (MAE) of 0.00067624 and 0.0005971 for the training and test sets, respectively, Root Mean Square Errors (RMSE) of 0.00088187 and 0.0008005, and Coefficient of Determination (R²) values of 0.9769 and 0.9589. These results are significantly superior to those of the other compared models. Furthermore, when applied to additional methane concentration datasets from the Buertai Coal Mine roadways, the model demonstrates R² values exceeding 0.95 for both the training and test sets, validating its excellent generalization ability, predictive performance, and potential for practical applications. Full article