Search Results (1,034)

We investigate the variation in shear-wave velocity (V_S) in the shallow soil of the ARGONET vertical array in Cephalonia, Greece, utilizing an extensive 8–10-year dataset of earthquake records and applying seismic interferometry by deconvolution and Generalized Additive Models (GAMs). We identify and quantify the contributions of seasonal variation, soil anisotropy, soil nonlinearity, and long-term Vs changes. Of the examined factors, nonlinearity produces the strongest V_S changes in the form of reduction of up to several tens of m/s. The azimuthal and seasonal partial effects appear similar in strength. However, V_S also exhibits year-to-year variation, with lower levels likely linked to the slow recovery of the soil following strong earthquakes in the broader region. When this partial effect is also considered, the temporal variation of V_S is more significant than the azimuthal variation. We also observed that strong weather phenomena, such as the unusual hurricane “Ianos” that hit western Greece in 2020, are captured in our model through tensor interaction terms. Our model can identify V_S drops related to nonlinear soil behavior even when masked by other effects. We demonstrate and verify this through seismic interferometry to stepwise increasing parts of earthquake recordings highlighting these within-events or coseismic V_S drops. Full article

Nepal is highly susceptible to natural hazards, including earthquakes, flooding, and landslides, all of which may occur independently or in combination. Climate change is projected to increase the frequency and intensity of these natural hazards, posing growing risks to Nepal’s infrastructure and development. To the authors’ knowledge, the majority of existing geohazard research in Nepal is typically limited to single hazards or localised areas. To address this gap, MiMapper was developed as a cloud-based, open-access multi-hazard mapping tool covering the full national extent. Built on Google Earth Engine and using only open-source spatial datasets, MiMapper applies an Analytical Hierarchy Process (AHP) to generate hazard indices for earthquakes, floods, and landslides. These indices are combined into an aggregated hazard layer and presented in an interactive, user-friendly web map that requires no prior GIS expertise. MiMapper uses a standardised hazard categorisation system for all layers, providing pixel-based scores for each layer between 0 (Very Low) and 1 (Very High). The modal and mean hazard categories for aggregated hazard in Nepal were Low (47.66% of pixels) and Medium (45.61% of pixels), respectively, but there was high spatial variability in hazard categories depending on hazard type. The validation of MiMapper’s flooding and landslide layers showed an accuracy of 0.412 and 0.668, sensitivity of 0.637 and 0.898, and precision of 0.116 and 0.627, respectively. These validation results show strong overall performance for landslide prediction, whilst broad-scale exposure patterns are predicted for flooding but may lack the resolution or sensitivity to fully represent real-world flood events. Consequently, MiMapper is a useful tool to support initial hazard screening by professionals in urban planning, infrastructure development, disaster management, and research. It can contribute to a Level 1 Integrated Geohazard Assessment as part of the evaluation for improving the resilience of hydropower schemes to the impacts of climate change. MiMapper also offers potential as a teaching tool for exploring hazard processes in data-limited, high-relief environments such as Nepal. Full article

Precast concrete frames integrating ultra-high-performance concrete (UHPC) beams and high-strength concrete (HSC) columns offer exceptional seismic resilience and construction efficiency. However, a performance-based seismic design methodology tailored for this hybrid structural system remains underdeveloped. This study aims to develop and validate a direct displacement-based design (DDBD) procedure specifically for precast UHPC-HSC frames. A novel six-tier performance classification scheme (from no damage to severe damage) was established, with quantitative limit values of interstory drift ratio proposed based on experimental data and code calibration. The DDBD methodology incorporates determining the target displacement profile, converting the multi-degree-of-freedom system to an equivalent single-degree-of-freedom system, and utilizing a displacement response spectrum. A ten-story case study frame was designed using this procedure and rigorously evaluated through pushover analysis. The results demonstrate that the designed frame consistently met the predefined performance objectives under various seismic intensity levels, confirming the effectiveness and reliability of the proposed DDBD method. This work contributes a performance oriented seismic design framework that enhances the applicability and reliability of UHPC-HSC structures in earthquake regions, offering both theoretical insight and procedural guidance for engineering practice. Full article

With the continuous improvement of the prefabricated modular technology system, the prefabricated subway station structures are widely used in underground engineering projects. However, prefabricated subway stations in soft soil can suffer significant adverse effects under seismic action. In order to study the seismic performance of a prefabricated subway station, this work is based on an actual project of a subway station in soft soil. And the nonlinear static and dynamic coupling two-dimensional finite element models of cast-in-place structures (CIPs), assembly splicing structures (ASSs), and assembly monolithic structures (AMSs) are established, respectively. The soil-structure interaction is considered, and different peak ground accelerations (PGA) are selected for incremental dynamic analysis. The displacement response, internal force characteristics, and structural damage distribution for three structural forms are compared. The research results show that the inter-story displacement of the AMS is slightly greater than that of the CIP, while the inter-story displacement of the ASS is the largest. The CIP has the highest internal force in the middle column, the ASS has the lowest internal force in the middle column, and the AMS is between the two. The damage to the CIP is concentrated at the bottom of the middle column and sidewall. The AMS compression damage moves upward, but the tensile damage mode is similar to the CIP. The ASS can effectively reduce damage to the middle column and achieve redistribution of internal force. Further analysis shows that the joint splicing interface between cast-in-place and prefabricated components is the key to controlling the overall deformation and seismic performance of the structure. The research results can provide a theoretical basis for the seismic design optimization of subway stations in earthquake-prone areas. Full article

Traditional anti-seismic methods are constrained by high construction costs and the potential for severe structural damage under earthquakes. Energy dissipation technology provides an effective solution for structural earthquake resistance by incorporating energy-dissipating devices within structures to actively absorb seismic energy. However, existing research lacks in-depth analysis of the influence of energy dissipation devices’ placement on structural dynamic response. Therefore, this study investigates the seismic mitigation effectiveness of viscous dampers in multi-storey frame structures and their optimal placement strategies. A comprehensive parametric investigation was conducted using a representative three-storey steel-frame kindergarten facility in Shandong Province as the prototype structure. Advanced finite element modeling was implemented through ETABS software to establish a high-fidelity structural analysis framework. Based on the supplemental virtual damping ratio seismic design method, damping schemes were designed, and the influence patterns of different viscous damper arrangement schemes on the seismic mitigation effectiveness of multi-storey frame structures were systematically investigated. Through rigorous comparative assessment of dynamic response characteristics and energy dissipation mechanisms inherent to three distinct energy dissipation device deployment strategies (perimeter distribution, central concentration, and upper-storey localization), this investigation delineates the governing principles underlying spatial positioning effects on structural seismic mitigation performance. This comprehensive investigation elucidates several pivotal findings: damping schemes developed through the supplemental virtual damping ratio-based design methodology demonstrate excellent applicability and predictive accuracy. All three spatial configurations effectively attenuate structural seismic response, achieving storey shear reductions of 15–30% and inter-storey drift reductions of 19–28%. Damper spatial positioning critically influences mitigation performance, with perimeter distribution outperforming central concentration, while upper-storey localization exhibits optimal overall effectiveness. These findings validate the engineering viability and structural reliability of viscous dampers in multi-storey frame applications, establishing a robust scientific foundation for energy dissipation technology implementation in seismic design practice. Full article

Underwater crack detection is critical for ensuring the safety and longevity of submerged infrastructures, yet it remains challenging due to water-induced image degradation, limited labeled data, and the poor generalization of existing models. This paper proposes a novel deep learning framework that integrates physical priors and uncertainty modeling to address these challenges. Our approach introduces a physics-guided enhancement module that leverages underwater light propagation models, and a dual-branch segmentation network that combines semantic and geometry-aware curvature features to precisely delineate irregular crack boundaries. Additionally, an uncertainty-aware Transformer module quantifies prediction confidence, reducing the number of overconfident errors in ambiguous regions. Experiments on a self-collected dataset demonstrate State-of-the-Art performance, achieving 81.2% mIoU and 83.9% Dice scores, with superior robustness in turbid water and uneven lighting. The proposed method introduces a novel synergy of physical priors and uncertainty-aware learning, advancing underwater infrastructure inspection beyond the current data-driven approaches. Our framework offers significant improvements in accuracy, robustness, and interpretability, particularly in challenging conditions like turbid water and non-uniform lighting. Full article

Due to cost and variability of geotechnical test results, the number of samples for geotechnical material parameters in one engineering project is limited, resulting in a certain degree of errors in the calculation of probability distribution, mean, and variance of mechanical parameters of the geotechnical materials. To improve the reliability of geotechnical engineering design, reducing the variance of shear strength is one of the methods. Currently, the least squares method is widely used to regress the shear strength of soil; however, the regression residuals often exhibit heteroscedasticity and correlation, which undermine the validity of the variance estimates of soil shear strength parameters. This study aims to address this issue by applying the generalized least squares method to eliminate the heteroscedasticity and correlation of regression residuals. The results of triaxial consolidated drained (CD) tests on the coarse-grained soil; triaxial unconsolidated undrained(UU), CD, and consolidated undrained (CU) tests on gravelly clay; and triaxial CD tests on sand were analyzed to estimate the mean and variance of their shear strength. The results show that while the mean values of shear strength parameters remain largely unchanged, the generalized least squares method reduces the standard deviation of cohesion by an average of 30.575% and that of the internal friction angle by 14.21%. This reduction in variability enhances the precision of parameter estimation, which is critical for reliability-based design in geotechnical engineering, as it leads to more consistent safety assessments and optimized structural designs. The reliability analysis of an infinitely long slope stability shows that the reliability index of the soil slope calculated by the traditional method is either large or small. The generalized least squares method, which eliminates the heteroscedasticity and correlation of the regression residuals, should be adopted to regress the shear strength of soil. Full article

Prestressed concrete structures are facing serviceability challenges due to rising live loads, material degradation, and seismic demands. Retrofitting with carbon fiber-reinforced polymer (CFRP) offers a cost-effective alternative to full replacement. This study presents a finite element (FE) modeling framework to simulate the seismic performance of prestressed concrete T-beams retrofitted in the negative moment region using near-surface-mounted (NSM) CFRP rods and sheets. The model incorporates nonlinear material behavior and cohesive interaction at the CFRP–concrete interface and is validated against experimental benchmarks, with ultimate load prediction errors of 4.41% for RC T-beams, 0.49% for prestressed I-beams, and 1.30% for prestressed slabs. A parametric investigation was conducted to examine the influence of CFRP embedment depth and initial prestressing level under three seismic conditions. The results showed that fully embedded CFRP rods consistently improved the beams’ ultimate load capacity, with gains of up to 10.84%, 16.84%, and 14.91% under cyclic loading, near-fault ground motion, and far-field ground motion, respectively. Half-embedded CFRP rods also prove effective and offer comparable improvements where full-depth installation is impractical. The cyclic load–displacement histories, the time–load histories under near-fault and far-field excitations, stiffness degradation, and damage contour analysis further confirm that the synergy between full-depth CFRP retrofitting and optimized prestressing enhances structural resilience and energy dissipation under seismic excitation. Full article

The reliability analysis of slope stability is significantly influenced by the variance of the soil’s shear strength. Currently, the shear strength of coarse-grained soil is commonly determined using the Duncan formula, which establishes a relationship between the shear strength and confining pressure. Specifically, the parameters of the Duncan formula are estimated based on available test data using least squares regression, which mitigates the limitations associated with small sample sizes and significant errors in the classic grouped data method. However, hypothesis testing in mathematical statistics reveals that the residuals from the classic least squares estimation exhibit heteroscedasticity and correlation, violating the fundamental assumptions required for least squares regression. Consequently, parameter estimates obtained through classic least squares regression have large variances, leading to unreliable statistical inferences. To address this issue, we propose a generalized least squares method that eliminates the heteroscedasticity and correlation of the residuals. Triaxial test data for five different coarse-grained soils are analyzed using the proposed method. The results show that the mean values of the estimated parameters of the Duncan formula are close to those obtained using the classic method, while their variances are significantly reduced, demonstrating the effectiveness of the proposed approach. The reliability analysis of the anti-sliding stability of an infinitely long slope shows that the strength parameters estimated by the classical least squares method tend to underestimate the stability of the slope due to the large variance. Therefore, the Duncan nonlinear shear strength parameters of coarse-grained soils should be estimated using the generalized least squares method that eliminates the heteroscedasticity and correlation of the regression residuals. Full article

Reinforced concrete (RC) columns play a vital role in structural integrity, and accurately predicting their failure modes is essential for enhancing seismic safety and performance. This study explores the use of a supervised machine learning approach—specifically, an artificial neural network (ANN) model—to classify failure modes of RC columns. The model is trained using data from the well-established Pacific Earthquake Engineering Research Center (PEER) structural performance database, which contains results from over 400 cyclic lateral-load tests on RC columns. These tests encompass a wide range of column types, including those with spiral or circular hoop confinement, rectangular ties, and varying configurations of longitudinal reinforcement with or without lap splices at critical sections. The ANNs were evaluated using a randomly selected subset from the PEER database, achieving classification accuracies of 94% for rectangular columns and 95% for circular columns. Notably, in certain cases, the model’s predictions aligned with or exceeded the accuracy of traditional building code-based methods. These findings underscore the strong potential of machine learning—particularly ANNs—for reliably postdicting failure modes (even the brittle ones) in RC columns, signaling a promising advancement in the field of earthquake engineering. Full article

Earthquakes remain among the most destructive natural hazards, causing severe loss of life and property in seismically active regions such as Nepal. Major events such as the 1934 Nepal–Bihar earthquake (M_w 8.2), the 2015 Gorkha earthquake (M_w 7.8), and the 2023 Jajarkot earthquake (M_L 6.4) have repeatedly exposed the vulnerability of Nepal’s built environment. In response, the Ready-to-Use Detailing (RUD) guideline (NBC 205:2024) was introduced to provide standardized structural detailing for low-rise reinforced concrete buildings without masonry infill, particularly for use in areas where access to professional engineering services is limited. This study was motivated by the need to critically assess the structural performance of buildings designed according to such rule-of-thumb detailing, which is widely applied through owner–builder practices. Nonlinear pushover analyses were carried out using finite element modelling for typical configurations on soil types C and D, under peak ground accelerations of 0.25 g, 0.30 g, 0.35 g, and 0.40 g. The response spectrum from NBC 105:2020 was adopted to determine performance points. The analysis focused on global response, capacity curves, storey drift, and hinge formation to evaluate structural resilience. The maximum story drift for the linear static analysis is found to be 0.56% and 0.86% for peak ground acceleration of 0.40 g, for both three and four-storied buildings. Also, from non-linear static analysis, it is found that almost all hinges formed in the beams and columns are in the Immediate Occupancy (IO) level. The findings suggest that the RUD guidelines are capable of providing adequate seismic performance for low-rise reinforced concrete buildings, given that the recommended material quality and construction standards are satisfied. Full article

This study investigates the creep behavior of remolded Wenzhou (China) coastal silt through one-dimensional (1D) and triaxial creep tests. Results show that the secondary consolidation coefficient exhibits a non-monotonic response to stress levels, while it decreases with increasing overconsolidation ratios (OCRs). The e-lgt curves reveal four distinct creep stages, and the soil exhibits significant time-dependent behavior that diminishes with depth. Triaxial tests highlight nonlinear stress–strain characteristics, where increasing confining pressure elevates the deviatoric stress required for creep acceleration. A proposed structural parameter exhibits an inverse correlation with creep deformation, which suggests that enhanced soil cementation can improve long-term stability. This finding provides critical insights for the management of silt foundations in Wenzhou. Full article

In recent years, the synergistic effect among production, maintenance, and quality control within manufacturing systems has garnered increasing attention in academic and industrial circles. In high-quality production settings, the real-time identification of minute process deviations holds significant importance for ensuring product quality. Traditional approaches, such as routine quality inspections or Shewhart control charts, exhibit limitations in sensitivity and response speed, rendering them inadequate for meeting the stringent requirements of high-precision quality control. To address this issue, this paper presents an integrated framework that seamlessly integrates stochastic process modeling, dynamic optimization, and quality monitoring. In the realm of quality monitoring, an exponentially weighted moving average (EWMA) control chart is employed to monitor the production process. The statistic derived from this chart forms a Markov process, enabling it to more acutely detect minor shifts in the process mean. Regarding maintenance strategies, a state-dependent preventive maintenance (PM) and corrective maintenance (CM) mechanism is introduced. Specifically, preventive maintenance is initiated when the system is in a statistically controlled state and the inventory level falls below a predefined threshold. Conversely, corrective maintenance is triggered when the EWMA control chart generates an out-of-control (OOC) signal. To facilitate continuous production during maintenance activities, an inventory buffer mechanism is incorporated into the model. Building upon this foundation, a joint optimization model is formulated, with system states, including equipment degradation state, inventory level, and quality state, serving as decision variables and the minimization of the expected total cost (ETC) per unit time as the objective. This problem is formalized as a constrained dynamic optimization problem and is solved using the genetic algorithm (GA). Finally, through a case study of the production process of vibroseis equipment, the superiority of the proposed model in terms of cost savings and system performance enhancement is empirically verified. Full article

The historic mortuary at Zagreb’s Mirogoj Cemetery, built in 1886, sustained moderate damage during the 2020 Mw 5.3 earthquake. Aiming to preserve heritage value while meeting Croatia’s Level 4 seismic safety requirements, the structure was assessed using in situ and laboratory tests followed by macro-element modeling with 3Muri software. The study evaluated four scenarios: (A) post-earthquake damaged state, (B) reinforcement with new masonry and RC walls, (C) partial fiber-reinforced cementitious matrix (FRCM) plastering, and (D) systematic FRCM plastering. Results show that Case B improved Ultimate Limit State (ULS) scaling factors from 0.64/0.56 to 0.92/0.90 (X/Y), while Case D raised them to 1.03/1.17, satisfying Eurocode 8 and national renovation criteria. Systematic FRCM application improved story shear capacity by up to 57% and shifted failure modes from brittle shear to ductile rocking. Partial plastering proved insufficient, highlighting the need for comprehensive global retrofitting. While the solution is minimally invasive and reversible, uncertainties remain regarding long-term durability and out-of-plane performance. This hybrid retrofitting strategy offers a replicable model for heritage masonry buildings in seismically active regions. Full article

The twin earthquakes that struck Kahramanmaraş in 2023 (Mw 7.7 and Mw 7.6) caused widespread structural destruction across southeastern Türkiye, underscoring the need for more refined approaches to seismic damage assessment. In this study, a large-scale machine learning (ML) analysis is conducted to identify and classify damage patterns among 304,299 buildings across 11 cities. Ten ML algorithms are implemented, and their performance in the multiclass classification of damage severity is comparatively evaluated (collapsed, urgent demolition, moderately damaged, and severely damaged). Unlike conventional methods that rely on single-station data, the proposed approach integrates ground motion parameters from the six seismic stations closest to each building. These parameters include peak ground acceleration, several distance measures (Joyner–Boore, rupture, and epicentral distances), and site condition indicators such as mean shear wave velocity in the upper 30 m and soil classification, yielding 60 engineered features per building. The analysis reveals that ensemble learning models, particularly the random forest and a voting ensemble, achieve the highest classification accuracies (79.65% and 79.62%, respectively). Moreover, classification performance varies across damage categories: severely damaged structures exhibit the highest F1-score (0.891), whereas collapsed buildings exhibit lower accuracy (F1-score: 0.408). These findings offer practical value for post-earthquake emergency operations. Furthermore, the methodology establishes a precedent for future seismic risk assessments and supports data-driven decision-making. Full article