Search Results (1,114)

Urban trees are subjected to stressful conditions caused by anthropogenic, biotic, and abiotic factors. These stressors can cause structural changes, increasing the risks of branch failure or even complete uprooting. To mitigate the risks to people’s safety, administrators must assess and evaluate the health and structural stability of trees. Risk analysis typically takes into account environmental vulnerability and tree characteristics, assessed at a specific point in time. However, although dynamic tests play a crucial role in risk assessment in urban environments, the high cost of the sensors significantly limits their widespread application across large tree populations. For this reason, the present study aims to evaluate the effectiveness of low-cost sensors in monitoring tree dynamics. A low-cost micro-electro-mechanical systems (MEMS) sensor is tested in the laboratory and the field using a pull-and-release test, and its performance is compared with that of seismic reference accelerometers. The collected data are analyzed and compared in terms of both the frequency and time domains. To obtain reliable measurements, the accelerations must be generated by substantial dynamic excitations, such as high wind events or abrupt changes in loading conditions. The results show that the MEMS sensor has lower accuracy and higher noise compared to the seismic sensor; however, the MEMS can still identify the main peaks in the frequency domain compared to the seismic sensor, provided that the input amplitude is sufficiently high. Full article

The spatial variability of soil shear wave velocity (V_s) significantly influences the results of site seismic response analysis. Based on the collected measured V_s values of silty clay in a certain sea area in China, this study divides the V_s data into one set of on-site sample data and six sets of historical data. A power function is used to establish the regression equation between V_s and depth h, and the joint prior distribution of the mean and variance for parameters a and b in the power function is derived using historical data. The joint posterior distribution of parameters a and b is obtained by applying the Bayesian formula to the on-site sample data. Using the maximum a posteriori mean values of a and b combined with the point estimation method, the mean and standard deviation of the predicted V_s values as functions of depth h are derived. The accuracy of the point estimation results is verified using Monte Carlo simulation. Compared to the V_s values predicted using only the mean values of a and b derived from on-site sample data, the V_s values predicted based on the maximum a posteriori mean values of a and b are closer to the measured V_s values. Accordingly, the results of the site seismic response analysis also align more closely with those calculated using the true V_s values. Full article

Accurate assessment of coalbed methane (CBM) content is essential for characterizing subsurface reservoir distribution, guiding well placement, and estimating reserves. Current methods for determining coal seam gas content mainly rely on direct laboratory measurements of core samples or indirect interpretations derived from well log data. However, conventional coring is costly, while log-based approaches often depend on linear empirical formulas and are restricted to near-wellbore regions. In practice, the relationships between elastic properties and gas content are highly complex and nonlinear, leading conventional linear models to produce substantial prediction errors and inadequate performance. This study introduces a novel method for predicting gas content in deep coal seams using a Conditional Generative Adversarial Network (CGAN). First, elastic parameters are obtained through pre-stack inversion. Next, sensitivity analysis and attribute optimization are applied to identify elastic attributes that are most sensitive to gas content. A CGAN is then employed to learn the nonlinear mapping between multiple fluid-sensitive seismic attributes and gas content distribution. By integrating multiple constraints to refine the discriminator and guide generator training, the model achieves accurate gas content prediction directly from seismic data. Applied to a real dataset from a CBM block in the Ordos Basin, China, the proposed CGAN-based method produces predictions that align closely with measured gas content trends at well locations. Validation at blind wells shows an average prediction error of 1.6 m³/t, with 83% of samples exhibiting errors less than 3 m³/t. This research presents an effective and innovative deep learning approach for predicting coalbed methane content. Full article

The long-distance high-voltage transmission tower-line system, frequently traversing active fault zones, is vulnerable to severe symmetry-breaking damage during earthquakes due to asymmetric permanent ground displacements. However, the seismic performance of such systems, particularly concerning symmetry-breaking effects caused by asymmetric fault displacements, remains inadequately studied. This study investigates the symmetry degradation mechanisms in a 1:40 scaled 500 kV tower-line system subjected to cross-fault ground motions via shaking table tests. The testing protocol incorporates representative fault mechanisms—strike-slip and normal/reverse faults—to systematically evaluate their differential impacts on symmetry response. Measurements of acceleration, strain, and displacement reveal that while acceleration responses are spectrally controlled, structural damage is highly fault-type dependent and markedly asymmetric. The acceleration of towers without permanent displacement was 35–50% lower than that of towers with permanent displacement. Under identical permanent displacement conditions, peak displacements caused by normal/reverse motions exceeded those from strike-slip motions by 50–100%. Accordingly, a fault-type-specific amplification factor of 1.5 is proposed for the design of towers in dip-slip fault zones. These results offer novel experimental insights into symmetry violation under fault ruptures, including fault-specific correction factors and asymmetry-resistant design strategies. However, the conclusions are subject to limitations such as scale effects and the exclusion of vertical ground motion components. Full article

The aim of this study is to investigate how the positioning of outrigger systems affects the seismic performance of high-rise buildings with either reinforced concrete (RC) shear walls or buckling-restrained braces (BRBs) in the core. Two important questions emerge as the focus and direction of the study: (1) How does the structural performance change when outriggers are placed at various positions? (2) How do outrigger systems affect structural behavior under sequential earthquake scenarios? Nonlinear time history analyses were employed as the primary methodology to evaluate the seismic response of the two reinforced concrete buildings with 24 and 48 stories, respectively. Each building type was developed for two different core configurations: one with a reinforced concrete shear wall core and the other with a BRB core system. Each analysis model also includes outrigger systems constructed with BRBs positioned at different floor levels. Five sequential ground motion records were used to assess the effects of main- and aftershocks. The analysis results were evaluated not only based on displacement and force demands but also using a damage measure called the Park-Ang Damage Index. In addition, displacement-based metrics, particularly the maximum inter-story drift ratio (MISD), were also utilized to quantify lateral displacement demands under consecutive seismic loading. With the results obtained from this study, it is aimed to provide design-oriented insights into the most effective use of outrigger systems formed with BRB in high-rise RC buildings and their functions in increasing seismic resistance, especially in areas likely to experience consecutive seismic events. Full article

This study proposes the first ground motion prediction equation (GMPE) for the parameter I_Np, an intensity measure based on the spectral shape. A Machine Learning Algorithm based on Support Vector Machines (SVMs) was employed due to its robustness towards outliers, which is a key advantage over ordinary linear regression. I_Np also offers a more robust measure of the ground motion intensity than the traditionally used spectral acceleration at the first mode of vibration of the structure Sa(T₁). The SVM algorithm, configured for regression (SVR), was applied to derive the prediction coefficients of I_Np for diverse vibration periods. Furthermore, the complete dataset was analyzed to develop a unified, generalized expression applicable across all the periods considered. To validate the model’s reliability and its ability to generalize, a cross-validation analysis was performed. The results from this rigorous validation confirm the model’s robustness and demonstrate that its predictive accuracy is not dependent on a specific data split. The numerical results show that the newly developed GMPE reveals high predictive accuracy for periods shorter than 3 s and acceptable accuracy for longer periods. The generalized equation exhibits an acceptable coefficient of determination and Mean Squared Error (MSE) for periods from 0.1 to 5 s. This work not only highlights the powerful potential of machine learning in seismic engineering but also introduces a more sophisticated and effective tool for predicting ground motion intensity. Full article

Cave systems are a kind of natural laboratory for interdisciplinary research on karstogenesis in the context of global changes. In this study, we investigate the concentration of ²²²Rn at 65 points in 37 representative caves of Bulgarian karst through continuous monitoring with passive and active detectors with a duration of 1 to 13 years. The concentration changes strongly both in the long term and seasonally, with values from 0.1 to 13 kBq m⁻³. These variations are analyzed from different perspectives (location and morphological features of the cave system, cave climate, ventilation regime, etc.). The seasonal change in the direction and intensity of ventilation is a leading factor determining the gas composition of the cave atmosphere during the year. Parallel measurements of ²²²Rn and CO₂ concentrations in the cave air show that both gases have a similar seasonal fluctuation. Cases of coincidences of an anomalous increase in the concentration of ²²²Rn with manifestations of seismic activity and micro-displacements along tectonic cracks in the caves have also been registered. The dependencies between the ²²²Rn concentration in the caves and in the soil above them are also discussed, as well as the possible connections between global trends in climate change and trends in ²²²Rn emissions. Special attention is paid to the risks of radiation exposure in show caves. A calculation procedure has been developed to achieve the realistic assessment of the effective dose of cave guides. It is based on information about the annual course of the ²²²Rn concentration in the respective cave and the time schedule of the guides’ stay in it. The calculation showed that the effective dose may exceed the permitted limits, and it is thus necessary to control it. Full article

The lack of research on the slip behavior of the NW-trending faults in the central Tibetan Plateau constrains our understanding of the deformation models for this region. The Ba Co Fault, located in the central Tibetan Plateau, is a NW–SE-trending right-lateral strike-slip fault. Its eastern section has been active in the Holocene and plays an important accommodating role in the northward compression and east–west extension of the Tibetan Plateau. This study presents a detailed analysis of the geomorphic features of the eastern section of the Ba Co Fault in the Ngari Prefecture of Tibet, precisely measuring the newly discovered surface rupture zone on its eastern side and preliminarily discussing the activity of the fault based on the optically stimulated luminescence (OSL) dating results. The results reveal that the eastern segment of the Ba Co Fault displays geomorphic evidence of offset, including displaced Holocene alluvial–fluvial fans at the mountain front and partially offset ridges. A series of pressure ridges, trenches, counter-slope scarps, and shutter ridge ponds have developed along the fault trace. Some gullies exhibit a cumulative dextral displacement of approximately 16–52 m. The newly discovered co-seismic surface rupture zone extends for a total length of ~21 km, with a width ranging from 30 to 102 m. Pressure ridges within the rupture zone reach heights of 0.3–5.5 m, while trenches exhibit depths of 0.6–15 m. Optically stimulated luminescence (OSL) dating constrains the timing of the surface-rupturing earthquake to after 5.73 ± 0.17 ka. The eastern segment of the Ba Co Fault experienced a NW-trending compressional deformation regime during the Holocene, manifesting as a transpressional dextral strike-slip fault. Magnitude estimation indicates that this segment possesses the potential to generate earthquakes of M ≥ 6. The regional tectonic analysis indicates that the activity of the eastern section of the Ba Co Fault is related to the shear model of the conjugate strike-slip fault zone in the central Tibetan Plateau and may play a boundary role between different shear zones. Full article

In the 1980s, Varotsos, Alexopoulos and Nomicos (VAN) introduced a short -term earthquake (EQ) prediction method based on measurements of the electric field of the Earth at various locations on the Earth’s surface. The corresponding electric signals are called Seismic Electric Signals (SES). Here, we present the advances of the VAN method during the period 2022–2025. For this purpose, we make use of the VAN telemetric network comprising of eight geoelectric field stations that have operated in Greece since the 1990s. The SES reported and documented well in advance (at arxiv.org) are compared with the subsequent seismicity in Greece during the same study period. The comparison reveals that all strong EQs of magnitude $M\ge 5.8$ within the area ${\mathrm{N}}_{34.5}^{41.5}$${\mathrm{E}}_{20.0}^{27.5}$ have been preceded by SES activities, thus leading to a hit rate of 100%. The study of the present results points to the need of continuing VAN experimentation in Greece. Moreover, we employ the Receiver Operation Characteristics (ROC) method to evaluate the performance of the method. Study of the ROC reveals a false alarm rate of approximately 5% which is shown to be statistically significant, while the method can be characterized as outstanding. Full article

Cracks in concrete structures, caused by aging, adjacent construction, and seismic activity, pose critical risks to structural integrity, durability, and serviceability. Traditional monitoring methods based solely on absolute thresholds are inadequate for detecting progressive crack growth at early stages. This study proposes a big data-driven anomaly detection model that combines absolute threshold evaluation with periodic trend analysis to enable both real-time monitoring and early anomaly identification. By incorporating relative comparisons, the model captures subtle variations within allowable limits, thereby enhancing sensitivity to incipient defects. Validation was conducted using approximately 2700 simulated datasets with an increase–hold–increase pattern and 470,000 real-world crack measurements. The model successfully detected four major anomalies, including abrupt shifts and cumulative deviations, and time series visualizations identified the exact onset of abnormal behavior. Through periodic fluctuation analysis and the Isolation Forest algorithm, the model effectively classified risk trends and supported proactive crack management. Rather than defining fixed labels or thresholds for the detected results, this study focused on verifying whether the analysis of detected crack data accurately reflected actual trends. To support interpretability and potential applicability, the detection outcomes were presented using quantitative descriptors such as anomaly count, anomaly score, and persistence. The proposed framework addresses the limitations of conventional digital monitoring by enabling early intervention below predefined thresholds. This data-driven approach contributes to structural health management by facilitating timely detection of potential risks and strengthening preventive maintenance strategies. Full article

Evaluation of seismic risk by capturing the influences of strong motion duration and frequency contents of ground motion through probabilistic approaches is the main element of this study. Unlike most existing studies that mainly focus on intensity measures such as peak ground acceleration or spectral acceleration, this work highlights how duration and frequency characteristics critically influence dam response. To achieve this, a total of 45 ground motion records, categorized by strong motion duration (long, medium, and short) and frequency content (low, medium, and high), were selected from the PEER database. Nonlinear numerical dynamic analysis was performed by scaling each ground motion from 0.05 g to 0.5 g, with the drift ratio at the dam crest used as the Engineering Demand Parameter. It is revealed that long-duration and low-frequency ground motions induced significantly higher drift demands. The fragility analysis was conducted using a lognormal distribution considering extensive damage threshold drift ratio. Finally, the probabilistic seismic risk was carried out by integrating the site-specific hazard curve and fragility curves which yield the height risk for long durations and low frequencies. The outcomes emphasize the importance of ground motion strong duration and frequency in seismic performance and these findings can be utilized in the dam safety evaluation. Full article

The relationship between the seismic regime and humidity, pressure, temperature, and wind speed measured at a network of stations on the Japanese islands was studied for the period from 1973 to 2025. For each of the parameters, weighted average time series were constructed using the principal component method and then subjected to wavelet decomposition. For wavelet decomposition levels, the amplitudes of the envelopes and the points of their local extrema were found and compared with the times at which earthquakes occurred. The problem of estimating an advanced measure of envelope extremum points relative to earthquake moments was considered using a model of interacting point processes. For a sequence of 213 strong earthquakes with a magnitude of at least 6.5, the same numbers for the largest local maxima and the smallest local minima were selected for the extrema of the envelope amplitude of each parameter. It turned out that the largest advance measures occurred for the seventh level of decomposition (the period from 16 to 32 days). Two advance mechanisms were identified: one mechanism is associated with the trigger effect of cyclones on seismicity, and the second is associated with the occurrence of atmospheric earthquake precursors. Full article

The traditional magnetoelectric geophone is widely used in the microseismic monitoring of coal mines. However, its measurement capability in the low-frequency range is insufficient and cannot fully meet the monitoring requirements of underground coal mines, which extend as low as 0.1 Hz. This paper proposes a signal conditioning (SC) circuit based on the extended filtering method to improve the low-frequency response capability of the geophone. Through simulation and experimental tests, it is verified that the designed SC circuit can reduce the cut-off frequency of the EST-4.5C geophone from 4.5 Hz to 0.16 Hz. Meanwhile, the noise introduced by this SC circuit is relatively low thanks to its simple and easy-to-implement structural model. The test results also indicate that it provides a strong ability to resist noise interference for the geophone, which is valuable under complex working conditions. Overall, this circuit offers a feasible option for enhancing the capability of the seismic geophones used in coal mines to detect low-frequency vibration signals. Full article

Assessing seismic vulnerability and prioritizing railway tunnels for seismic rehabilitation are critical components of railway infrastructure management, especially in seismically active regions. This study focuses on a railway network in Northwest Iran, consisting of 103 old masonry rock tunnels. The vulnerability of these tunnels is evaluated under 12 active faults as seismic sources. Fragility curves derived from the HAZUS methodology estimate the probability of various damage states under seismic intensities, including peak ground acceleration (PGA) and peak ground displacement (PGD). The expected values of the damage states are computed as the damage index (DI) to measure the severity of damage. A normalized prioritization index (NPI) is proposed, considering seismic vulnerability and life cycle damages in tunnel prioritizing. Finally, a detailed prioritization is provided in four classes. The results indicate that 10% of the tunnels are classified as priority, 33% as second priority, 40% as third priority, and 17% as fourth priority. This prioritization is necessary when there are budget limitations and it is not possible to retrofit all tunnels simultaneously. The main contribution of this study is the development of an integrated, data-driven framework for prioritizing the seismic rehabilitation of aging masonry railway tunnels, combining fragility-based vulnerability assessment with life-cycle damage considerations in a high-risk and data-limited region. The framework outlined in this study enables decision-making organizations to efficiently prioritize the tunnels based on vulnerability, which helps to increase seismic resilience. Full article

Background: Despite the identification of various environmental factors that increase the risk of multiple sclerosis (MS), the effects of many factors on the etiology of MS remain to be elucidated. In this study, we aimed to investigate the effects of radon, a factor previously studied in relation to various other neurodegenerative diseases, on the epidemiology of MS. Methods: A door-to-door field study was conducted in residential areas with relatively high and low radon gas concentrations to determine the prevalence of MS. The study area comprises the Bolvadin and İhsaniye regions, which have different geological characteristics, such as seismic activity, active faults, and distributions of volcanic rocks. CR-39 detectors, with an accepted limit of 300 Bq/m³, were utilized to measure radon gas concentrations. During the screening field, the patients diagnosed with multiple sclerosis were confirmed with their hospital records. Mc Donald’s revised diagnostic criteria were used for multiple sclerosis diagnosis. Results: The regions were grouped into higher radon areas and lower radon areas. The İhsaniye city center, Kayıhan, Kemerkaya, Döğer, and Bolvadin city center were classified as higher radon regions, whereas Dişli, Yaylabağı, Gazlıgöl, and Özburun were identified as lower radon regions. A total of 40,841 individuals were surveyed in the field. The crude MS prevalence was 41.8/100,000 in settlements with high radon gas concentrations and 20.5/100,000 in settlements with low radon gas concentrations. Conclusions: In this study, we revealed that the prevalence of MS was greater in settlements with high radon gas concentrations than in settlements with low radon gas concentrations. These results demonstrated that radon gas is an important environmental risk factor in the etiopathogenesis of MS. Full article