Search Results (269)

Accurate evaluation of subgrade behaviour under dynamic loading is essential for the long-term performance of transport infrastructure. While the Light Weight Deflectometer (LWD) is commonly used to assess subgrade stiffness, it provides only a single stiffness value and may not fully capture the time-dependent response of soil. This study presents an image-based vision system developed to monitor soil surface displacements during loading, enabling more detailed analysis of dynamic behaviour. The system incorporates high-speed cameras and MATLAB-based computer vision algorithms to track vertical movement of the plate during impact. Laboratory and field experiments were conducted to evaluate the system’s performance, with results compared directly to those from the LWD. A strong correlation was observed (R² = 0.9901), with differences between the two methods ranging from 0.8% to 13%, confirming the accuracy of the vision-based measurements despite the limited dataset. The findings highlight the system’s potential as a practical and cost-effective tool for enhancing subgrade assessment, particularly in applications requiring improved understanding of ground response under repeated or transient loading. Full article

Landslide InSAR monitoring is crucial for understanding the evolutionary mechanisms of geological disasters and enhancing risk prevention and control capabilities. However, for complex terrains and large-scale landslides, satellite-based SAR monitoring faces challenges such as a low observation frequency and limited spatial deformation interpretation capabilities. Additionally, two-dimensional monitoring struggles to comprehensively capture multi-directional movements. Taking the post-disaster monitoring of the landslide in Yunchuan, Sichuan Province, as an example, this study proposes a method for three-dimensional deformation dynamic monitoring by integrating dual-view MIMO ground-based synthetic aperture radar (GB-InSAR) data with high-resolution digital elevation model (DEM) data, successfully reconstructing the three-dimensional displacement fields in the east–west, north–south, and vertical directions. The results show that deformation in the landslide area evolved from slow accumulation to rapid failure, particularly concentrated in the middle and lower regions of the landslide. The average three-dimensional deformation of the main slip zone was approximately 60% greater than that of the original slope, with a maximum deformation of −100 mm. These deformation characteristics are highly consistent with the topographic structure and sliding direction. Field investigations further validated the radar data, with observed surface cracks and accumulation zones consistent with the high-deformation regions identified by the monitoring system. This system provides a solid foundation for geological disaster early warning systems, mechanism research, and risk prevention and control. Full article

Scorpions possess unique, ornate mid-ventral sensory organs called pectines. The pectines are used to process chemo- and mechanosensory information acquired from the ground as the animal walks, and they are implicated in a variety of behaviors including navigation and detection of mates and prey. Many previous researchers have investigated pecten function by cutting the organs from the animals (full ablation) and comparing their behaviors with those of intact scorpions. This drastic approach is likely to not only cause enormous stress to the ablated animals but also change their behavior. Here, we have developed a method for gently and reversibly impairing the pectines by partially covering them to prevent them from lowering to the ground. Specifically, we fabricated small rectangles of a commercially available lightly adhesive foil tape that we placed across the pectines and secured to the body wall with a thin strip of a more strongly adhesive lab tape. Using a repeated measures design, we monitored the animals’ locomotory activity overnight in small behavioral arenas under three conditions: unmodified (intact) control, pectines restrained, and sham control. We found that scorpions with their pectines restrained had a significant increase in both the distance and duration of movement when compared to unmodified and sham control animals. Our method allows for temporary, reversible compromise of pecten function and should be useful in fully understanding the role of pectines in behavior. Full article

Geological storage is an integral element of the green energy transition. Geological formations, such as aquifers, depleted reservoirs, and hard rock caverns, are used mainly for the storage of hydrocarbons, carbon dioxide and increasingly hydrogen. However, potential adverse effects such as ground movements, leakage, seismic activity, and environmental pollution are observed. Existing research focuses on monitoring subsurface elements of the storage, while on the surface it is limited to ground movement observations. The review was carried out based on 191 research contributions related to geological storage. It emphasizes the importance of monitoring underground gas storage (UGS) sites and their surroundings to ensure sustainable and safe operation. It details surface monitoring methods, distinguishing geodetic surveys and remote sensing techniques. Remote sensing, including active methods such as InSAR and LiDAR, and passive methods of multispectral and hyperspectral imaging, provide valuable spatiotemporal information on UGS sites on a large scale. The review covers modelling and prediction methods used to analyze the environmental impacts of UGS, with data-driven models employing geostatistical tools and machine learning algorithms. The limited number of contributions treating geological storage sites holistically opens perspectives for the development of complex approaches capable of monitoring and modelling its environmental impacts. Full article

Smoke appears earlier than flames, so image-based fire monitoring techniques mainly focus on the detection of smoke, which is regarded as one of the effective strategies for preventing the spread of initial fires that eventually evolve into serious fires. Smoke monitoring in mine fires faces serious challenges: the underground environment is complex, with smoke and backgrounds being highly integrated and visual features being blurred, which makes it difficult for existing image-based monitoring techniques to meet the actual needs in terms of accuracy and robustness. The conventional ground-based methods are directly used in the underground with a high rate of missed detection and false detection. Aiming at the core problems of mixed target and background information and high boundary uncertainty in smoke images, this paper, inspired by the principle of information entropy, proposes a method for recognizing smoke from mine fires by integrating entropy-enhanced image processing and improved YOLOv8. Firstly, according to the entropy change characteristics of spatio-temporal information brought by smoke diffusion movement, based on spatio-temporal entropy separation, an equidistant frame image differential fusion method is proposed, which effectively suppresses the low entropy background noise, enhances the detail clarity of the high entropy smoke region, and significantly improves the image signal-to-noise ratio. Further, in order to cope with the variable scale and complex texture (high information entropy) of the smoke target, an improvement mechanism based on entropy-constrained feature focusing is introduced on the basis of the YOLOv8m model, so as to more effectively capture and distinguish the rich detailed features and uncertain information of the smoke region, realizing the balanced and accurate detection of large and small smoke targets. The experiments show that the comprehensive performance of the proposed method is significantly better than the baseline model and similar algorithms, and it can meet the demand of real-time detection. Compared with YOLOv9m, YOLOv10n, and YOLOv11n, although there is a decrease in inference speed, the accuracy, recall, average detection accuracy mAP (50), and mAP (50–95) performance metrics are all substantially improved. The precision and robustness of smoke recognition in complex mine scenarios are effectively improved. Full article

This article provides a concise overview of the existing tunnels located within the historic cultural heritage site of Gediminas Hill in Vilnius, with particular emphasis on the implications of a recently discovered tunnel. This newly identified tunnel is of particular interest due to its location beneath a retaining wall in close proximity to an adjacent structure. Long-term structural monitoring data indicate that the building has experienced displacement away from the retaining wall. Although the precise cause of this movement remains undetermined, the discovery of the tunnel adjacent to the structure has raised concerns regarding its potential role in the observed displacements. To investigate this hypothesis, a previously developed numerical model was employed to simulate the tunnel’s impact. The simulation results suggest that the tunnel’s construction was executed with careful consideration. During the excavation phase, the retaining wall exhibited displacements in a direction opposite to the expected ground pressure, indicating effective utilization of the wall’s gravitational mass. However, historical records indicate that no retaining structures were present in the area during the tunnel’s initial period of existence. Consequently, an additional simulation phase was introduced to model the behavior of the surrounding loose soil in the absence of retaining support. The results from this phase revealed that the deformations of the retaining wall and the adjacent building were elastically interdependent. The simulated deformation patterns closely matched the temporal trends observed in the monitoring data. These findings support the hypothesis that the tunnel’s construction may have contributed to the displacement of the nearby building. 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

This paper contributes a two-step approach to monitor clusters of thermal targets on the ground using unmanned aerial vehicles (UAVs) and Gaussian mixture models (GMMs) in a distributed manner. The approach is tailored to networks of UAVs that establish a flying ad hoc network (FANET) and operate without central command. The first step is a monitoring algorithm that determines if the GMM corresponds to the current spatial distribution of clusters of thermal targets on the ground. UAVs make this determination using local data and a sequence of data exchanges with UAVs that are one-hop neighbors in the FANET. The second step is the calculation of a new GMM when the current GMM is found to be unfit, i.e., the GMM no longer corresponds to the new distribution of clusters on the ground due to the movement of thermal targets. A distributed expectation-maximization algorithm is developed for this purpose, and it operates on local data and data exchanged with one-hop neighbors only. Simulation results evaluate the performance of both algorithms in terms of the number of communication exchanges. This evaluation is completed for an increasing number of clusters of thermal targets and an increasing number of UAVs. The performance is compared with well-known solutions to the monitoring and GMM calculation problems, demonstrating convergence with a lower number of communication exchanges. Full article

The remote sensing of seismic waves in challenging and hazardous environments, such as active volcanic regions, remains a critical yet unresolved challenge. Conventional methods, including laser Doppler interferometry, InSAR, and stereo vision, are often hindered by atmospheric turbulence or necessitate access to observation sites, significantly limiting their applicability. To overcome these constraints, this study introduces a Moiré-based apparatus augmented with active convolved illumination (ACI). The system leverages the displacement-magnifying properties of Moiré patterns to achieve high precision in detecting subtle ground movements. Additionally, ACI effectively mitigates atmospheric fluctuations, reducing the distortion and alteration of measurement signals caused by these fluctuations. We validated the performance of this integrated solution through over 1900 simulations under diverse turbulence intensities. The results illustrate the synergistic capabilities of the Moiré apparatus and ACI in preserving the fidelity of Moiré fringes, enabling reliable displacement measurements even under conditions where passive methods fail. This study establishes a cost-effective, scalable, and non-invasive framework for remote seismic monitoring, offering transformative potential across geophysics, volcanology, structural analysis, metrology, and other domains requiring precise displacement measurements under extreme conditions. Full article

Background/Objectives: Parkinson’s disease (PD) is a neurodegenerative disorder with a prolonged prodromal phase and progressive symptom burden. Traditional monitoring relies on clinical visits post-diagnosis, limiting the ability to capture early symptoms and real-world disease progression outside structured assessments. Social media provides an alternative source of longitudinal, patient-driven data, offering an opportunity to analyze both pre-diagnostic experiences and later disease manifestations. This study evaluates the feasibility of using Facebook to analyze PD-related discourse and disease timelines. Methods: Participants (N = 60) diagnosed with PD, essential tremor, or atypical parkinsonism, along with caregivers, were recruited. Demographic and clinical data were collected during structured interviews. Participants with Facebook accounts shared their account data. PD-related posts were identified using a Naïve Bayes classifier (recall: 0.86, 95% CI: 0.84–0.88, AUC = 0.94) trained on a ground-truth dataset of 6750 manually labeled posts. Results: Among participants with PD (PwPD), Facebook users were significantly younger but had similar Movement Disorder Society-United Parkinson’s Disease Rating Scale scores and disease duration compared to non-users. Among Facebook users with PD, 90% had accounts before diagnosis, enabling retrospective analysis of pre-diagnostic content. PwPD maintained 14 ± 3 years of Facebook history, including 5 ± 6 years pre-diagnosis. On average, 3.6% of all posts shared by PwPD were PD-related, and 1.7% of all posts shared before diagnosis were PD-related. Overall, 69% explicitly referenced PD, and 93% posted about PD-related themes. Conclusions: Facebook is a viable platform for studying PD progression, capturing both early content from the premorbid period and later-stage symptoms. These findings support its potential for disease monitoring at scale. Full article

Automatic passenger counting (APC) systems are an important asset for public transport operators, allowing them to optimise networks by monitoring lines’ utilisation. However, the cost of these systems is high and the development of alternative devices, cheaper than the most widely used optical systems, seems promising. This paper aims at understanding the influence of local factors on the accuracy of a Wi-Fi APC system by analysing error patterns in a real-world scenario. The APC system was installed on a bus operating regularly within the public transport network and, in the meantime, ground truth data were collected through manual counting. The collected data were then analysed to calculate accuracy and, finally, multilevel modelling was used to identify error patterns due to local factors. This study challenges traditional assumptions, revealing that factors like high pedestrian traffic or intense vehicular movement around the bus have minimal impact on accuracy, if effective received signal strength indicator filters are used. Instead, the number of passengers within the bus affects Wi-Fi systems significantly, especially when the bus is carrying more than 10 passengers, which leads to undercounting due to signal obstruction. This research lays the foundation for strategic error correction to improve accuracy in real-world scenarios. Full article

Pillarless mining technology is of great significance for improving coal recovery rates, but the intense mining-induced stress disturbances on gob-side entries often lead to surrounding rock instability. In this study, we focused on the ground control challenges in the headgate of Panel 81308 at Huayang Mine No. 2. Comprehensive monitoring of roof–floor convergence, rib deformation, and support resistance revealed the gob-side entry retaining deformation mechanisms with roof-cutting pressure relief; the results show that this retaining deformation exhibits the following three phases of characteristics: the rapid, decelerated, and stable stages. The average roof–floor convergence (607 mm) was significantly greater than the average rib deformation (170 mm), with floor heave accounting for 72.6% of total convergence. The coal pillar side showed dominant deformation in rib movements. The mining influence zones can be divided, based on their distances behind the working face, into strong disturbance zones (0–88 m), weak disturbance zones (88–142 m), and stabilized zones (>178 m). The cable bolt support system demonstrated advanced response characteristics. Compared with conventional gob-side entry retaining, the roof-cutting pressure relief technique altered stress transmission paths, significantly reduced roof load transfer efficiency, and effectively controlled roadway convergence, providing technical guidance for safe production in both this panel and mines with similar geological conditions. Full article

The rapid advancement of modern megapolises has led to a dearth of surface space, and, in response, engineers have begun to trial substitutes below ground level. Shafts are generally used to provide temporary access and permanent work to the subsurface for tunnelling, as well as for lifts or ventilation purposes. In urban areas, one important design issue is the prediction of the excavation-induced displacements by open caisson shaft construction. Settlements and ground movements associated with open caisson shafts are influenced by the choice of construction method, soil composition, and excavation geometry. Compared with other geotechnical construction events, for instance, tunnelling, the literature relating to the ground deformations induced from open caisson shafts are comparatively limited. This review offers an evaluation of several case studies that utilize experimental and computational modeling techniques to provide clearer insights into earth pressure distribution and induced surface and subsurface soil displacements, as well as the associated ground deformations during open caisson shaft construction. The modeling test results are compared to the state of the practice ground deformation prediction theories and measured results from field monitoring data. Findings indicate that the lateral earth pressure distribution aligns closely with the theoretical predictions based on Terzaghi’s and Berezantzev’s models, and lateral earth pressure diminishes gradually until the onset of active wall displacement. Current modeling techniques generally fail to properly represent in situ stress states and large-scale complexities, emphasizing the need for hybrid approaches that combine physical and numerical methodologies. In future studies, modern approaches, including artificial intelligence (AI) monitoring (e.g., PINNs, ACPP), multi-field coupling models (e.g., THMC), and transparent soil testing, hold profound potential for real-time prediction, optimization, and visualization of soil deformation. Numerical–physical coupling tests will integrate theory and practice. Improving prediction reliability in complicated soil conditions such as composite and heterogenous strata using different modeling techniques is still unclear, and further investigation is therefore needed. Full article

Accurate estimation of gait characteristics, including step length, step velocity, and travel distance, is critical for assessing mobility in toddlers, children, and teens with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers. This study introduces a novel method leveraging Fast Fourier Transform (FFT)-derived step frequency from a single waist-worn consumer-grade accelerometer to predict gait parameters efficiently. The proposed FFT-based step frequency detection approach, combined with regression-derived stride length estimation, enables precise measurement of temporospatial gait features across various walking and running speeds. Our model, developed from a diverse cohort of children aged 3–16, demonstrated high accuracy in step length estimation ($R^2=0.92$, $RMSE=0.06$ m) using only step frequency and height as inputs. Comparative analysis with ground-truth observations and AI-driven Walk4Me models validated the FFT-based method, showing strong agreement across step count, step frequency, step length, step velocity, and travel distance metrics. The results highlight the feasibility of using widely available mobile devices for gait assessment in real-world settings, offering a scalable solution for monitoring disease progression and mobility changes in individuals with DMD. Future work will focus on refining model performance and expanding applicability to additional movement disorders. Full article

Virtual reality (VR) devices that integrate eye-tracking and hand-tracking technologies can capture users’ natural eye–hand data in real time within a three-dimensional virtual space, providing new opportunities to explore users’ attentional states during natural 3D interactions. This study aims to develop an attention-state prediction model based on the multimodal fusion of eye and hand features, which distinguishes whether users primarily employ goal-directed attention or stimulus-driven attention during the execution of their intentions. In our experiment, we collected three types of data—eye movements, hand movements, and pupil changes—and instructed participants to complete a virtual button selection task. This setup allowed us to establish a binary ground truth label for attentional state during the execution of selection intentions for model training. To investigate the impact of different time windows on prediction performance, we designed eight time windows ranging from 0 to 4.0 s (in increments of 0.5 s) and compared the performance of eleven algorithms, including logistic regression, support vector machine, naïve Bayes, k-nearest neighbors, decision tree, linear discriminant analysis, random forest, AdaBoost, gradient boosting, XGBoost, and neural networks. The results indicate that, within the 3 s window, the gradient boosting model performed best, achieving a weighted F1-score of 0.8835 and an Accuracy of 0.8860. Furthermore, the analysis of feature importance demonstrated that the multimodal eye–hand features play a critical role in the prediction. Overall, this study introduces an innovative approach that integrates three types of multimodal eye–hand behavioral and physiological data within a virtual reality interaction context. This framework provides both theoretical and methodological support for predicting users’ attentional states within short time windows and contributes practical guidance for the design of attention-adaptive 3D interfaces. In addition, the proposed multimodal eye–hand data fusion framework also demonstrates potential applicability in other three-dimensional interaction domains, such as game experience optimization, rehabilitation training, and driver attention monitoring. Full article