Search Results (12)

Road surface condition monitoring is critical for ensuring safe and efficient transportation networks. This study proposes and evaluates a framework that compares imagery-, Light Detection and Ranging (LiDAR), and accelerometer-based approaches for pavement anomaly detection. The analysis first focused on a 5-mile urban roadway segment, in which all three sensing modalities were evaluated under identical survey conditions using manually interpreted reference anomalies to compare detection accuracy, severity classification, and processing efficiency. The imagery-based Convolutional Transformer-based Crack Segmentation (CT-CrackSeg) model achieved a precision, recall, and F1-score of 88.5%, 88.5%, and 88.5%, respectively, but remained sensitive to environmental factors such as shadows, curbs, roadside features, and pavement texture variations. The LiDAR-based method achieved an F1-score of 93.0%, while the accelerometer-based Isolation Forest and Adaptive Threshold methods achieved F1-scores of 95.2% and 97.2%, respectively. These results indicate strong detection performance under the evaluated validation conditions; however, the reported precision values should be interpreted as dataset-specific rather than universal performance levels. Given the accelerometer-based approach’s strong detection performance, minimal processing time, and low deployment cost, it was further applied across a 36-mile roadway network to evaluate its scalability for network-level monitoring. Across the full route, the spatial agreement among accelerometer systems exceeded 0.91, while the agreement between the two detection methods exceeded 0.96, with 962–996 surface defects detected depending on the sensor and method. Integrating the anomaly detection results into a Potree-based web portal enabled interactive validation with geotagged imagery and point clouds, improving interpretability and diagnostic insight. Overall, the findings highlight that accelerometer-based monitoring, even with consumer-grade sensors, provides a practical, scalable, and low-cost solution for pavement evaluation, while LiDAR and imagery serve as complementary tools for detailed verification and characterization. Full article

With the transition to deep coal mining, the transparent detection of hidden geological hazards in the floor strata is fundamental for production safety. In mine seismic exploration, gliding waves—inhomogeneous plane waves propagating along the coal–rock interface—offer a unique advantage for penetrating high-velocity floors via the skin effect, overcoming the total reflection limitations of conventional in-seam waves. This study investigates the propagation laws and anomaly response characteristics of floor gliding waves using super-critical incidence theory and high-order staggered-grid finite difference simulations. The results demonstrate that the apparent velocities of gliding P and S-waves are bounded by those of the coal and host rock, exhibiting minimal dispersion. Quantitative analysis using a penetration depth model reveals that while penetration depth is frequency-dependent—with lower frequencies providing deeper reach—high-frequency components remain essential for high-resolution imaging. Crucially, the proposed method was validated through a field Case Study at the 11123 working face. By utilizing a specialized deep-hole excitation strategy to ensure super-critical incidence, the inversion successfully identified a hidden fault extending up to 60 m below the floor, which was subsequently confirmed by rock roadway excavation. These findings establish a robust physical basis for designing underground floor-detection systems and provide a significant theoretical reference for addressing detection blind spots in deep mining environments. Full article

Road geometry is a modifiable determinant of crash occurrence and severity; addressing it is critical for achieving sustainable transport systems. Yet, policy action requires clear prioritization across road types and years to ensure sustainable resource allocation. This study analyzes fatal and injury outcomes by roadway geometric context in Türkiye (2015–2024) and proposes a cell-level prioritization framework integrating crash burden, severity, and short-term deviations to support long-term sustainable road safety management. Annual data were structured as Year × Road type × Geometry × Category, with severity measured as deaths and injuries per 100 crashes (Kmin = 30). A Road Geometry Severity Index (RGSI; 0–100) combined standardized severity, log crash burden, and deviation from a three-year baseline. Isolation Forest and a MAD-based rule identified anomalies, while K-means clustering (K = 4) revealed burden–severity profiles. Results show deaths per 100 crashes declined from 7.91 (2015) to 3.29 (2022), then rose to 6.22 (2024). Severity was highest on provincial (8.82) and state roads (7.23), compared to motorways (4.66). High-severity cells were dominated by provincial-road contexts, especially dangerous curves and junction-related categories. The highest-priority cell was 2018–Provincial Road–Junction–No Junction (RGSI = 100). Under the predefined contamination specification (γ = 0.05), the Isolation Forest model flagged 35 anomalous cells, all of which also satisfied the MAD-based anomaly criterion. Findings highlight persistent high-priority roadway geometric contexts and demonstrate the potential of RGSI as a transparent infrastructure-prioritization tool. Full article

Mine water inrush remains one of the major hazards threatening the safety of coal mining operations. To assess the feasibility of integrating transient electromagnetic (TEM) and direct-current (DC) methods for advanced detection in underground settings, a three-dimensional geoelectric forward model for both techniques was developed in COMSOL Multiphysics based on the fundamental principles of electromagnetic prospecting. The model was used to examine the electromagnetic responses of water-rich anomalies surrounding mine roadways under different source configurations and spatial positions. Comparative analyses show that both DC and TEM methods effectively detect water-bearing targets within 40 m of the roadway, whereas TEM exhibits superior sensitivity at greater distances. TEM achieves its highest sensitivity when the anomaly is located within an azimuthal range of 30–45°. The characteristic response patterns derived from the simulations were applied to interpret field data acquired at the Tashan Coal Mine. The interpretation successfully delineated the presence and orientation of the water-bearing body ahead of the excavation face, and subsequent underground drilling verified the accuracy of the predictions. These findings demonstrate that COMSOL-based electromagnetic forward modeling provides a reliable framework for interpreting advanced geophysical detection data and is feasible for practical applications in mine water-inrush hazard assessment. Full article

This study presents case applications of developed vibration-based technologies for evaluating roadway networks at both point and street level, using smartphone-grade sensors. The approach is designed to (1) provide a low-cost but reliable alternative to expensive specialized equipment for pavement assessment, and (2) enable continuous data collection through participatory sensing. In the first case study presented, a smartphone was employed, whereas in the second case study, a custom integrated sensor device was utilized. In both case studies, hybrid vehicles were deployed as probe cars. Case Study 1 involved approximately two hours of roadway surveying, yielding about 0.6 million data points, while Case Study 2 extended to 16 hours of surveying, and produced roughly 1.6 million data points. The local municipality leverages the resulting ride-quality and pavement anomaly maps, by use of a simplified pavement management system (PMS), to prioritize roadway operations and maintenance activities. Ongoing research integrates in-development low-cost GNSS sensors with cameras, machine learning and machine vision and PMS software in a low-cost yet high-accuracy pavement assessment and management platform. Full article

To address the challenge of dynamic monitoring during grouting operations in coal mine fault zones under pressurized mining, this study proposes the Borehole–Tunnel Joint Resistivity Method (BTJRM). By integrating three-dimensional (3D) electrode arrays in both tunnels and boreholes with 3D resistivity inversion technology, this approach enables fully automated underground data acquisition and real-time processing, facilitating comprehensive dynamic monitoring of grout propagation. A case study was conducted on a coal mine fault grouting project, where tunnel and borehole survey lines were deployed to construct a 3D cross-monitoring network, overcoming the limitations of traditional 2D data acquisition. Finite volume method and quasi-Gauss–Newton inversion algorithms were employed to analyze dynamic resistivity variations, enhancing spatial resolution for detailed characterization of grout migration. Key findings include: (1) Grout diffusion reduced resistivity by 10%, aligning with electrical response patterns during fracture-filling stages; (2) 3D inversion reveals that grout propagates along the principal stress axis, forming a “Y”-shaped low-resistivity anomaly zone that penetrates the fault structural block and extends into roadway areas. The maximum planar and vertical displacements of grout reach 100 m and 40 m, respectively. Thirty days post-grouting, resistivity recovers by up to 22%, reflecting the electrical signature of grout consolidation; (3) This method enables 3D reconstruction of grout diffusion pathways, extends the time window for early warning of water-conducting channel development, and enhances pre-warning capabilities for grout migration. It provides a robust framework for real-time sealing control of fault strata, offering a novel dynamic monitoring technology for mine water inrush prevention. The technology can provide reliable grouting evaluation for mine disaster control engineering. Full article

To address the challenge of accurately detecting hidden water inrush hazards ahead of working faces, a cross-hole transient electromagnetic (TEM) method utilizing a grounded-wire source is proposed. The technique positions a step-current-driven grounded-wire source within a working-face borehole, while electrode arrays in adjacent boreholes measure secondary electric field responses. This configuration minimizes interference from metal supports or machines, thereby enhancing the signal-to-noise ratio of the TEM signals. A theoretical analysis based on the unstructured finite-element (FE) method is used to investigate the configuration. The collected data are processed using differential techniques, and the results confirm the method’s effectiveness in detecting anomalies. This paper investigates the response of our cross-hole method to anomalies in terms of size, resistivity contrasts, and spatial location, with anomaly boundaries quantitatively delineated via first-order differential analysis. This significantly enhances the capability of TEM detection in identifying anomalies. A comparison between our cross-hole method and the traditional roadway–borehole TEM method, using the trapped column model, demonstrates that the proposed cross-hole device more effectively locates anomalies and improves accuracy. Furthermore, this technique enables the formation of a 3D observation framework by utilizing existing boreholes, presenting promising prospects for future applications. Full article

To ensure precise and real-time perception of high-speed roadway conditions and minimize the potential threats to traffic safety posed by road debris and defects, this study designed a real-time monitoring and early warning system for high-speed road surface anomalies. Initially, an autonomous mobile intelligent road inspection robot, mountable on highway guardrails, along with a corresponding cloud-based warning platform, was developed. Subsequently, an enhanced target detection algorithm, YOLOv5s-L-OTA, was proposed. Incorporating GSConv for lightweight improvements to standard convolutions and employing the optimal transport assignment for object detection (OTA) strategy, the algorithm’s robustness in multi-object label assignment was enhanced, significantly improving both model accuracy and processing speed. Ultimately, this refined algorithm was deployed on the intelligent inspection robot and validated in real-road environments. The experimental results demonstrated the algorithm’s effectiveness, significantly boosting the capability for real-time, precise detection of high-speed road surface anomalies, thereby ensuring highway safety and substantially reducing the risk of liability disputes and personal injuries. Full article

Efforts to enhance quality control (QC) practices in chip seal construction have predominantly relied on single surface friction metrics such as mean profile depth (MPD) or friction number. These metrics assess chip seal quality by targeting issues such as aggregate loss or excessive bleeding, which may yield low friction numbers or texture depths. However, aggregate loss, particularly due to snowplow operations, does not always result in slippery conditions and may lead to uneven surfaces. The correlation between higher MPD or friction number and superior chip seal quality is not straightforward. This research introduces an innovative machine learning-based approach to enhance chip seal QC. Using a hybrid DBSCAN-Isolation Forest model, anomaly detection was conducted on a dataset comprising 183,794 20 m MPD measurements from actual chip seal projects across six districts in Indiana. This resulted in typical 20 m segment MPD ranges of [0.9 mm, 1.9 mm], [0.6 mm, 2.1 mm], [0.3 mm, 1.3 mm], [1.0 mm, 1.7 mm], [0.6 mm, 1.9 mm], and [1.0 mm, 2.3 mm] for the respective six districts in Indiana. A two-step QC procedure tailored for chip seal evaluation was proposed. The first step calculated outlier percentages across 1-mile segments, with an established limit of 25% outlier segments per wheel track. The second step assessed unqualified rates across projects, setting a threshold of 50% for unqualified 1-mile wheel track segments. Through validation analysis of four chip seal projects, both field inspection and friction measurements closely aligned with the proposed methodology’s results. The methodology presented establishes a foundational QC standard for chip seal projects, enhancing both acceptance efficiency and safety by using a quantitative method and minimizing the extended presence of practitioners on roadways. Full article

This paper takes the bottom pumping roadway of 33190 machine roadway in the No.10 mine of China PingMeiShenMa Group as the engineering background. This mine is a hydrothermal mine, with strong heat conduction and thermal convection activities between the surrounding rock and geothermal water. This forms a geothermal anomaly area, making the overall temperature of the surrounding rock temperature field increase and affecting the mine thermal environment. According to the measured field data and the engineering geological conditions of the roadway, a roadway seepage-heat transfer model is constructed using the comsol numerical simulation software, emulating the effect of geothermal water upwelling to the roadway through random cracks in the surrounding rock at different temperatures and pressures, which has an impact on the airflow temperature field of the roadway. The study shows that the evolution law of the airflow temperature field in the roadway under different water upwelling temperatures and pressures is roughly the same, and the temperature at the entrance of the roadway is almost unchanged: the heating rate is 0, and then increases linearly. The variation in the airflow outlet temperature is analyzed, both under the conditions of same temperature but different pressure, and under the same pressure but different temperature. The water upwelling temperature and the cooling efficiency are positively correlated, and the overall growth rate of the airflow temperature is positively correlated with the water upwelling temperature and pressure; however, the effect of temperature is far greater than that of pressure. The upwelling temperature of geothermal water is the main influencing factor on the temperature field of the airflow in the roadway. Therefore, it is possible to reduce the temperature of upwelling water by laying heat insulation materials on the bottom plate, evacuating geothermal water and circulating cold-water by injection, so as to improve the thermal environment of water-heated mines and increase their production efficiency. Full article

Connected and automated vehicles (CAVs) as a part of Intelligent Transportation Systems (ITS) are projected to revolutionise the transportation industry, primarily by allowing real-time and seamless information exchange of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). However, these connectivity and automation are expected to offer vast numbers of benefits, new challenges in terms of safety, security and privacy also emerge. CAVs continue to rely heavily on their sensor readings, the input obtained from other vehicles and the road side units to inspect roadways. Consequently, anomalous reading of sensors triggered by malicious cyber attacks may lead to fatal consequences. Hence, like all other safety-critical applications, in CAVs also, reliable and secure information dissemination is of utmost importance. As a result, real time detection of anomaly along with identifying the source is a pre-requisite for mass deployment of CAVs. Motivated by this safety concerns in CAVs, we develop an efficient anomaly detection method through the combination of Bayesian deep learning (BDL) with discrete wavelet transform (DWT) to improve the safety and security in CAVs. In particular, DWT is used to smooth sensor reading of a CAV and then feed the data to a BDL module for analysis of the detection and identification of anomalous sensor behavior/data points caused by either malicious cyber attacks or faulty vehicle sensors. Our numerical experiments show that the proposed method demonstrates significant improvement in detection anomalies in terms of accuracy, sensitivity, precision, and F1-score evaluation metrics. For these metrics, the proposed method shows an average performance gain of $7.95\%$, $9\%$, $8.77\%$ and $7.33\%$, respectively when compared with Convolutional Neural Network (CNN-1D), and when compared with BDL, the corresponding numbers are $5\%$, $7.9\%$, $7.54\%$ and $4.1\%$ respectively. Full article

Transportation agencies cannot afford to scale existing methods of roadway and railway condition monitoring to more frequently detect, localize, and fix anomalies throughout networks. Consequently, anomalies such as potholes and cracks develop between maintenance cycles and cause severe vehicle damage and safety issues. The need for a lower-cost and more-scalable solution spurred the idea of using sensors on board vehicles for a continuous and network-wide monitoring approach. However, the timing of the full adoption of connected vehicles is uncertain. Therefore, researchers used smartphones to evaluate a variety of methods to implement the application using regular vehicles. However, the poor accuracy of standard positioning services with low-cost geospatial positioning system (GPS) receivers presents a significant challenge. The experiments conducted in this research found that the error spread can exceed 32 m, and the mean localization error can exceed 27 m at highway speeds. Such large errors can make the application impractical for widespread use. This work used statistical techniques to inform a model that can provide more accurate localization. The proposed method can achieve sub-meter accuracy from participatory vehicle sensors by knowing only the mean GPS update rate, the mean traversal speed, and the mean latency of tagging accelerometer samples with GPS coordinates. Full article