Search Results (1,058)

There is no doubt that additive manufacturing (AM) with mortars presents an opportunity within the framework of a circular economy that should not be overlooked. The concepts of reduce, reuse, and recycle are fully aligned with this technology. One of the less explored possibilities is the utilisation of mining tailings as aggregates in printing mortars. This idea not only incorporates the concept of recycling but also contributes to a reduction in the production of potentially hazardous waste that would otherwise require storage in dams, thereby decreasing long-term environmental risks and improving the management of mineral resources. We employed a mortar composed of 12.5% material derived from mining tailings to highlight aspects of AM that are typically not subject to analysis, such as the necessity of considering contact interfaces between layers in structural design, the stackability of layers during the construction process, and the behaviour under fire and seismic events, which must be taken into account during the operational phase. Without aiming for exhaustiveness, we conducted a series of tests and computational modelling to show the significance of these factors, with the intention of drawing the attention of different stakeholders—including construction companies, regulatory authorities, standardisation agencies, insurers, and end-users. Full article

This study presents a seismic tomography analysis of the Târgu Jiu region in southwestern Romania, an area that experienced an unusual earthquake sequence in 2023. Using P- and S-wave arrival times local earthquakes, we applied the LOTOS algorithm to produce high-resolution 3D crustal seismic velocities models. High Vp and Vs values in the northern and northeastern areas suggest the presence of dense, rigid geological formations, likely associated with consolidated magmatic or metamorphic units. In contrast, the central region exhibits low Vs values, coinciding with an active seismic zone and intersecting major fault structures. This suggests the presence of highly fractured and weakly consolidated rocks, potentially saturated with fluids. The Vp/Vs ratio in the central region reached values of ≥1.8–1.9, indicating fluid-filled fractures that may influence fault dynamics and earthquake occurrence. In the southern region, velocity anomalies suggest weakly consolidated sedimentary units with a high degree of fracturing. These findings contribute to a better understanding of the geodynamic behavior of the Târgu Jiu area and its seismic hazard potential. Full article

This study focuses on the fundamental mechanical behavior of frame–shear wall split-foundation structures with shear walls at both upper and lower ground ends, investigating their basic mechanical characteristics, internal force redistribution patterns, and the influencing factor of intra-stiffness ratio on seismic performance. From the analysis results, it can be found that the relative drop height of frame–shear wall split-foundation structures significantly affects their internal force patterns. Shear-bending stiffness should be adopted in stiffness calculations to reflect the stiffness reduction effect of drop height on lower embedding shear walls. In frame–shear wall split-foundation structures, the existence of drop height causes upper embedding columns to experience more unfavorable stress conditions compared to lower embedding shear walls, potentially preventing lower embedding shear walls from serving as the primary seismic defense line. Strengthening lower embedding shear walls to reduce the intra-stiffness ratio can mitigate this issue. Performance evaluation under bidirectional rare earthquakes shows greater along-slope directional damage than cross-slope directional damage. Increasing shear wall length to reduce the intra-stiffness ratio improves component rotation-based performance, but shear strain-based evaluation of upper embedding shear walls indicates a limited improvement in shear capacity. Special attention should therefore be paid to along-slope directional shear capacity of upper embedding shear walls during structural design. Full article

Steel slag aggregate (SSA), as a high-performance and sustainable material, has demonstrated significant potential in enhancing the mechanical properties of concrete and improving the bond behavior between reinforcement and the concrete matrix, thereby contributing to the seismic resilience of steel slag concrete columns (SSCCs). Nevertheless, the underlying mechanism through which SSA influences the seismic performance of SSCCs remains insufficiently understood, and current analytical models fail to accurately capture the effects of bond strength on structural behavior. In this study, a comprehensive experimental program comprising central pull-out tests and quasi-static cyclic loading tests was conducted to investigate the influence of SSA on bond strength and the seismic response of SSCCs. Key seismic performance indicators, including the hysteresis curve, equivalent viscous damping ratio, and ductility coefficient, were evaluated. The role of bond strength in governing energy dissipation and ductility characteristics was elucidated in detail. The results indicate that bond strength significantly affects the seismic performance of SSCC components. At an SSA replacement ratio of 40%, the specimens show optimal performance: energy dissipation capacity increases by 11.3%, bond–slip deformation in the plastic hinge region decreases by 10%, and flexural deformation capacity improves by 9% compared to the control group. However, when the SSA replacement exceeds 60%, the performance metrics are similar to those of ordinary concrete, showing no significant advantages. Based on the experimental findings, a modified bond–slip constitutive model for the steel slag concrete–reinforcement interface is proposed. Furthermore, a finite element model incorporating bond–slip effects is developed, and its numerical predictions exhibit strong agreement with the experimental results, effectively capturing the lateral load-carrying capacity and stiffness degradation behavior of SSCCs. Full article

Post-earthquake fires are typically of great concern for fire protection services, which are expected to be in high demand immediately after a strong earthquake. The post-earthquake functionality of fire stations is necessary after strong earthquakes to reduce potential fire damage and improve emergency services. A reliable assessment of the seismic vulnerability and expected damage for fire stations is therefore a necessary step towards the identification of the most vulnerable structures and the prioritization of seismic retrofit activities. This article presents the development of a methodology for the damage assessment of fire stations based on earthquakes scenarios. The framework is based on four models: seismic hazard, inventory, fragility and impact. The seismic hazard model represents ground shaking in terms of intensity measure at each station using a ground motion prediction equation for Eastern Canada. The inventory model categorizes all the fire stations in building classes based on construction material and seismic code level. The fragility model associates building classes with fragility functions that provide the relationship between intensity measure and expected damage probabilities. The impact model converts damage probabilities into a mean damage state. All Montreal fire stations were selected as case study demonstrations. Simulations were conducted by varying the epicenter location and magnitude for a total number of 345 scenarios. Simplified relationships that correlate the earthquake magnitude and expected damage were developed. The study showed that, for magnitude 6 earthquakes, 45% of stations on average would sustain at least moderate damage. The methodology is particularly useful for emergency planning and prioritization of seismic retrofit activities. Full article

This research work aims to design and develop a dynamic vibration absorber that effectively reduces the vibrations of a flexible structure subjected to external loads. The analysis presented in this paper initially focuses on identifying the resonance frequencies of a typical structural system, which serves as the case study, since these frequencies are critical to dampening due to their potential to cause excessively large vibration amplitudes. Following this, the optimal parameters of the vibration absorber, including the mass, stiffness, and damping characteristics of the proposed design, were determined. Additionally, this paper proposes and examines the use of viscous-type damping, which is achieved through piston–cylinder systems connected to the structural components of the analyzed frame structure. Thus, the main contributions of this work include the analytical dimensioning, the technical design, and the virtual prototyping of a dynamic absorber constructed using a guyed mast structure capable of significantly reducing mechanical vibrations. This design solution ultimately enhances the strength and durability of the frame structure represented in the case study under external excitation, particularly in the worst-case scenario of seismic action. Furthermore, a key aspect of this study is implementing a new numerical procedure for identifying the system equivalent stiffness coefficient based on its mass and modal parameters, which is particularly useful in engineering applications. The numerical experiments conducted in this work support the effectiveness of the proposed design solution, devised specifically for the dynamic vibration absorber developed in this paper. Full article

Facing the problems in determining the distribution range of karst areas and detecting karst caves under the restrictions of complex building and human exploration environments on the urban surface, taking the karst detection of Tianmeixin village and its southern pond in the north extension section of Guanghua Intercity Railway Line 18 as the application research object, based on the formation mechanism of karst and the existing geophysical detection methods, the electrical resistivity tomography method with a large detection range and the cross-well seismic computed tomography method with a high detection accuracy are used to carry out application research on concealed karst cave detection, which are two geophysical technical detection methods with strong adaptability and anti-interference ability. The results show that the optimized combination of geophysical exploration techniques can effectively overcome the limitations of the environment, draw the main karst development areas, reveal the interface between rock and soil, and accurately characterize the size and shape of karst caves. The electrical resistivity tomography method was used to find a number of potential water conduction channels in the middle zone between Tianmeixin village and the south river. The overall distribution characteristics of karst in Tianmeixin village were summarized, and the key detection areas were drawn. This conclusion was verified by several sets of cross-well seismic computed tomography profiles, which provided a reference for the layout of the subsequent cross-well seismic computed tomography imaging method and greatly reduced the workload of drilling, shortened the construction period, saved on detection costs, and reduced the impact on the production and life of residents. Full article

Proper geologic reservoir characterization is crucial for energy generation and climate change mitigation efforts. While conventional techniques like core analysis and well logs provide limited spatial reservoir information, seismic data can offer valuable 3D insights into fluid and rock properties away from the well. This research focuses on identifying important structural and stratigraphic variations at the Mississippi Canyon Block 118 (MC-118) field, located on the northern slope of the Gulf of Mexico, which is significantly influenced by complex salt tectonics and slope failure. Due to a lack of direct subsurface data like well logs and cores, this area poses challenges in delineating potential reservoirs for carbon storage. The study leveraged seismic multi-attribute analysis and machine learning on 3-D seismic data and well logs to improve reservoir characterization, which could inform field development strategies for hydrogen or carbon storage. Different combinations of geometric, instantaneous, amplitude-based, spectral frequency, and textural attributes were tested using Self-Organizing Maps (SOM) to identify distinct seismic facies. SOM Models 1 and 2, which combined geometric, spectral, and amplitude-based attributes, were shown to delineate potential storage reservoirs, gas hydrates, salt structures, associated radial faults, and areas with poor data quality due to the presence of the salt structures more than SOM Models 3 and 4. The SOM results presented evidence of potential carbon storage reservoirs and were validated by matching reservoir sands in well log information with identified seismic facies using SOM. By automating data integration and property prediction, the proposed workflow leads to a cost-effective and faster understanding of the subsurface than traditional interpretation methods. Additionally, this approach may apply to other locations with sparse direct subsurface information to identify potential reservoirs of interest. Full article

The application and promotion of grouted sleeve connectors in prefabricated structures are closely related to their high efficiency and intensive advantages. Numerous scholars have conducted experimental studies on the performance of sleeves, but there has been no systematic consolidation of these efforts. In this study, the latest developments in grouted sleeve connection technology are systematically reviewed and analysed, focusing on its applications and characteristics, performance testing, influencing factors, load-transfer mechanisms, and performance evaluation. First, the differences in sleeve code formulation across various countries are compared, the advantages and disadvantages of different sleeve types and grouting techniques are reviewed, and the application scenarios of sleeves are summarized. Second, an overview of the performance of grouted sleeves in tensile, fatigue, and seismic tests is provided, highlighting key factors affecting structural performance and experimental results. Furthermore, the effects of various factors (the anchorage length, diameter and strength of reinforcing bars; types and defects of grout materials; sleeve tube design; and temperature) on the performance of sleeves are investigated, and some beneficial conclusions are drawn. The load-transfer mechanisms of different sleeve types are subsequently compared, and the common features of the sleeves that meet the performance evaluation criteria are analysed. Finally, potential future research directions and innovations in sleeve technology are suggested to provide researchers and scholars with innovative ideas and research perspectives for developing new sleeves and advancing the application of grouted sleeve connectors. Full article

The introduction of a novel replaceable plastic hinge technology aims to enhance the performance of precast reinforced concrete (PRC) frames, particularly in seismically vulnerable areas where substandard structural systems are prevalent. This artificially controllable plastic hinge (ACPH) mechanism effectively localizes inelastic deformations to a detachable steel subassembly, thereby maintaining the integrity of the primary structural components. A numerical analysis was carried out on four distinct PRC frame configurations that utilized concrete and steel of inferior quality relative to contemporary standards. The frames underwent testing under a segment of the Mw 7.7 Kahramanmaraş ground motion, revealing that connections utilizing the ACPH not only reduce peak base shear but also mitigate cracking at beam–column interfaces, directing plastic strains towards replaceable fuse elements. The implementation of the ACPH also facilitates extended structural periods and localized plastic hinging, which serves to limit damage to essential members while expediting post-earthquake repairs. Comparative validation through prior subassembly tests confirms that this hinge exhibits a strong hysteretic response and ductile performance, surpassing traditional wet-joint connections in the context of substandard PRC frames. Overall, these results underscore the potential of standardized hinge modules in enhancing seismic resilience and supporting swift, economical rehabilitation of critical infrastructure. Thus, this proposed technology effectively tackles persistent issues related to low-strength materials in precast structures, presenting a practical approach to improving earthquake resilience and minimizing repair time and costs. Full article

This study investigates the application of machine learning (ML) algorithms for seismic damage classification of bridges supported by helical pile foundations in cohesive soils. While ML techniques have shown strong potential in seismic risk modeling, most prior research has focused on regression tasks or damage classification of overall bridge systems. The unique seismic behavior of foundation elements, particularly helical piles, remains unexplored. In this study, numerical data derived from finite element simulations are used to classify damage states for three key metrics: piers’ drift, piles’ ductility factor, and piles’ settlement ratio. Several ML algorithms, including CatBoost, LightGBM, Random Forest, and traditional classifiers, are evaluated under original, oversampled, and undersampled datasets. Results show that CatBoost and LightGBM outperform other methods in accuracy and robustness, particularly under imbalanced data conditions. Oversampling improves classification for specific targets but introduces overfitting risks in others, while undersampling generally degrades model performance. This work addresses a significant gap in bridge risk assessment by combining advanced ML methods with a specialized foundation type, contributing to improved post-earthquake damage evaluation and infrastructure resilience. Full article

This study investigates co-seismic landslides triggered by the 1 January 2024 Mw 7.6 Noto Peninsula earthquake in Japan using LiDAR differentiation and a modified Savage–Hutter model. By analyzing pre- and post-earthquake high-resolution topographic data from 13 landslides in a geologically homogeneous area of the peninsula, we characterized distinct landslide morphologies and dynamic behaviours. Our approach combined static morphological analysis from LiDAR data with simulations of granular flow mechanics to evaluate landslide mobility. Results revealed two distinct landslide types: those with clear erosion-deposition zonation and complex landslides with discontinuous topographic changes. Landslide dimensions followed power-law relationships (H = 7.51L^0.50, R² = 0.765), while simulations demonstrated that internal deformation capability (represented by the μ parameter) significantly influenced runout distances for landslides terminating on low-angle surfaces but had minimal impact on slope-confined movements. These findings highlight the importance of integrating both static topographic parameters and dynamic flow mechanics when assessing co-seismic landslide hazards, particularly for predicting potential runout distances on gentle slopes where human settlements are often located. Our methodology provides a framework for improved landslide susceptibility assessment and disaster risk reduction in seismically active regions. Full article

Preliminary tunnel surveys are essential for identifying geological hazards such as aquifers, faults, and karstic zones. While first-arrival tomography is effective for imaging shallow anomalies, traditional seismic sources face significant limitations in forested mountainous regions due to mobility, cost, and environmental impact. To address this, we deployed a seismic source delivered by an unmanned aerial vehicle (UAV) for a highway tunnel survey in Lijiang, China. The UAV system, paired with nodal geophones, enabled rapid, low-impact, and high-resolution data acquisition in rugged terrain. To enhance the weak far-offset refractions affected by near-surface attenuation, we applied supervirtual refraction interferometry (SVI), which significantly improved the signal-to-noise ratio and expanded the usable first-arrival dataset. The combined use of UAV excitation and SVI processing produced a high-precision P-wave velocity model through traveltime tomography, aligned well with borehole data. This model revealed the spatial distribution of weathered zones and bedrock interfaces, and allowed us to infer potential fracture zones. The results offer critical guidance for tunnel alignment and hazard mitigation in complex geological settings. Full article

Earthquakes cause enormous social and economic damage. Consequently, the seismic process requires regular monitoring and systematic forecasting of strong earthquakes. This study introduces an enhanced iteration of the method of the minimum area of alarm (MMAA), refined to advance earthquake forecasting technology closer to its practical application. In the new version, a forecast is considered successful when all target earthquake epicenters within a specified time interval are contained within predefined alarm zones. Our updated algorithm optimizes the probability of successfully detecting earthquakes across forecast cycles and the probability for subsequent periods. A case study from the Kamchatka region demonstrates the practical application of this systematic forecasting approach. We propose that this computational technology can serve as an operational tool for generating early warnings of potential seismic hazards, and a research platform for conducting detailed investigations of precursor phenomena. Full article

The COVID-19 pandemic presented many potentially traumatic circumstances. Research continues to investigate pandemic-related Post-traumatic Growth (PTG). However, most studies fail to fulfil the parameters of PTG whereby a triggering event must be of seismic intensity and have ceased before PTG can manifest, producing significant validity and reliability issues. The relationships between PTG, trait resilience and fear are also under-researched, particularly in circumstances where the parameters of PTG are met. This study examined the relationship between PTG, COVID-19-related fear and trait resilience. Participants (n = 229) completed an online questionnaire incorporating the Post-Traumatic Growth Inventory and the Connor–Davidson Resilience Scale. The sample participants were moderately traumatised with moderate–low PTG (M = 50.85). Participants reported greater levels of PTG compared to participants from pre-COVID studies, notably in relation to the constructs of Relating to Other (d = 0.29), New Possibilities (d = 0.47), Personal Strength (d = 0.39), and Spiritual Change (d = 0.29). Higher levels of resilience (B = 0.48) and COVID-19-related fear (B = 0.16) were associated with greater overall PTG. Younger participants also reported greater levels of PTG (B = −0.29). The findings advance current knowledge regarding the potential relationship between fear and PTG and demonstrate that trait resilience is a promotional factor, presenting opportunity for future intervention formulation. However, reform is required within the PTG literature pool. Future research investigating PTG must reach both parameters. In circumstances where this is impossible, research concerning newfound positive cognition during adverse circumstances should be re-explored as Post-Adversarial Appreciation (PAA) to maintain validity. Full article