Seismic risk assessment of code-noncompliant reinforced concrete (RC) frames faces significant challenges due to structural heterogeneity and the complex interplay of site-specific hazard conditions. This study aims to introduce a novel framework that integrates three key concepts specifically targeting these challenges. Central to the methodology are fragility fuses, which employ a triplet of curves-lower bound, median, and upper bound-to rigorously quantify within-class variability in seismic performance, offering a more nuanced representation of code-noncompliant building behavior compared to conventional single-curve approaches. Complementing this, spectrum-consistent transformations dynamically adjust fragility curves to account for regional spectral shapes and soil categories, ensuring site-specific accuracy by reconciling hazard intensity with local geotechnical conditions. Further enhancing precision, the framework adopts a nonlinear hazard model that captures the curvature of hazard curves in log-log space, overcoming the oversimplifications of linear approximations and significantly improving risk estimates for rare, high-intensity events. Applied to four RC frame typologies (2-5 stories) with diverse geometries and material properties, the framework demonstrates a 15-40 % reduction in risk estimation errors through nonlinear hazard modeling, while spectrum-consistent adjustments show up to 30 % variability in exceedance probabilities across soil classes. Fragility fuses further highlight the impact of structural heterogeneity, with older, non-ductile frames exhibiting 25 % wider confidence intervals in performance. Finally, risk maps are presented for the four frame typologies, making use of non-linear hazard curves and spectrumconsistent fragility fuses accounting for both local effects and within-typology variability.

Background: Herbicides are chemical agents that promote plant and crop growth by killing weeds and other pests. However, unconsumed and excessively used herbicides may enter groundwater and agricultural areas, damaging water, air, and soil resources. Mesotrione (MT) is an extensively used herbicide to cultivate corn, sugarcane, and vegetables. Excessive consumption of MT residues pollutes the soil, water, and environmental systems. Methods: Henceforth, the potential electrocatalyst of the tungsten trioxide nanorods on the carbon microsphere (WO3/C) composite was synthesized for nanomolar electrocatalytic detection of MT. The electrocatalysts of WO3/C were synthesized hydrothermally, and the WO3/C composite was in-situ constructed by using the reflux method. Significant findings: Remarkably, the as-prepared WO3/C composite displayed a fantastic sensing platform for MT, characterized by an astonishingly nanomolar detection limit (10 nm), notable sensitivity (1.284 mu A mu M-1 cm-2), exceptional selectivity, and amazing stability. The actual sample test was carried out using MT added in food and environmental samples of corn, sugar cane, sewage water, and river water. The minimum MT response recovery in vegetable and water samples was determined to be approximately 97 % and 99 %, respectively. The results indicate that the WO3/C composite is an effective electrode material for real-time MT measurement in portable devices.

Over the past few decades, engineering research has increasingly focused on the reliability assessment of transport infrastructures and their critical components when faced with multiple natural hazards. This trend stems from recognizing the substantial direct and indirect economic losses associated with infrastructure damage and the resulting downtime. The increasing frequency of intense hazard occurrences, as a consequence of climate change, coupled with the time-intensive nature of post-event bridge inspections, highlights the need for an efficient approach to assess bridge fragility to hazards that occur either as single abrupt events or in compounds, i.e., multiple hazard perturbations or combined incremental deterioration. This approach should account for the order of hazards and the accumulation of damage to bridge components. Within this context, we introduce an analytical method for evaluating the fragility of bridges affected by independent or multiple successive and independent natural hazards. The proposed method is demonstrated through a case study in which a riverine bridge is evaluated considering different sequences of hazards. Initially, the fragility of the bridge under individual hazards, such as earthquakes or floods, is calculated. Subsequently, multi-hazard fragility curves are constructed to capture the combined effects of these events. This approach is a comprehensive method for generating fragility curves for bridges, considering all structural components involved in the resisting system of the structure. These curves are based on a detailed estimation of thresholds for different limit states, encompassing multiple failure modes and accounting for soil-structure interaction (SSI) effects. The method employs a probabilistic framework to manage uncertainties in both the demand on the structure and its capacity to withstand single hazards. The framework is extended to include scenarios involving multiple hazards that occur separately or in series, emphasizing how cumulative damage influences the overall bridge fragility. The findings indicate a significant increase in the probability of damage for all the limit states examined, underscoring the importance of considering the cumulative effect of multiple hazards in the fragility analysis of bridges. The fragility models can be used in life-cycle risk assessment of aging bridges exposed to multiple hazards to inform decision-making and prioritization of investments for risk mitigation and climate adaptation.

Current practice to model the occurrence of submarine landslides is based on methods that assess the potential of site-specific failures, all with the objective of providing elements to identify and quantify regional features associated to geohazards, before a project development takes place. Also, survey data to estimate parameters required to model submarine landslides show typically limited availability, mainly because of the cost associated to offshore surveying campaigns. In this paper, a probabilistic calibration approach is introduced using Bayesian statistical inference to maximize the use of available site investigation data, and to best estimate the occurrence of a marine landslide. For this purpose, a landslide model thought for its simplicity is used to illustrate the applicability and potential of the calibration methodology. The aim is to introduce a systematic approach to produce prior probability distributions of the model parameters, based on an actual integrated marine site investigation including geological, geophysical, and geomatics data, to then compare it with a posterior probability distribution of the same model parameters, but estimated after collecting in situ soil samples and testing them in the laboratory to produce the corresponding soil strength properties. This comparison allows to explore (a) the influence of the number of in situ samples, (b) the influence of a landslide factor of safety, and (c) the influence of the soil heterogeneity, into the likelihood of the occurrence of a marine landslide. The model parameters that are considered for calibration include the initial state of the submerged and saturated soil unit weight, the thickness of the soils' unit layers, the pseudo-static seismic coefficient, and the slope angle, while the soil undrained shear strength is considered as the reference parameter to conduct the calibration (i.e., to compare model predictions vs. actual observations). Results show the potential of the proposed methodology to produce landslide geohazard maps, which are needed for the planning and design of marine infrastructure.

Geohazards such as slope failures and retaining wall collapses have been observed during thawing season, typically in early spring. These geohazards are often attributed to changes in the engineering properties of soil through changes in soil phase with moisture condition. This study investigates the impact of freezing and thawing on soil stiffness by addressing shear wave velocity (Vs) and compressional wave velocity (Vp). An experimental testing program with a temperature control system for freezing and thawing was prepared, and a series of bender and piezo disk element tests were conducted. The changes in Vs and Vp were evaluated across different phases: unfrozen to frozen; frozen to thawed; and unfrozen to thawed. Results indicated different patterns of changes in Vs and Vp during these transitions. Vs showed an 8% to 19% decrease for fully saturated soil after thawing, suggesting higher vulnerability to shear failure-related geohazards in thawing condition. Vp showed no notable change after thawing compared to initial unfrozen condition. Based on the test results in this study, correlation models for Vs and Vp with changes in soil phase of unfrozen, frozen, and thawed conditions were established. From computed tomography (CT) image analysis, it was shown that the decrease in Vs was attributed to changes in bulk volume and microscopic soil structure.

The current study focuses on the long term strength reduction in lime stabilised Cochin marine clays with sulphate content. By introducing 6% lime and 4% sulphates to untreated Cochin marine clay, the research aims to investigate the effect of sulphates in these clays. Unconfined compression tests were conducted on lime treated clay both with and without additives, immediately after preparation and over 1 week, 1 month, 3 months, 6 months, 1 year and 2 years of curing. Test results indicated that both sodium sulphate and lithium sulphate has a negative impact on the strength gain of lime stabilised clay. To address this issue, Barium hydroxide, in both its pure laboratory form and the commercial product known as baryta, was incorporated into the lime stabilised soil. The study showed a consistent increase in shear strength with the addition of both barium hydroxide and baryta. When twice the predetermined quantity of baryta was added to lime stabilised clay, it outperformed pure barium hydroxide in terms of strength enhancement. Results of SEM and XRD analysis align with the strength characteristics. The cost-effective use of baryta offers a practical solution to counteract strength loss in lime stabilised, sulphate bearing Cochin marine clays.

This study presents the design and structural analysis of a bridge to protect two natural gas pipelines against static and dynamic loads resulting from a new railway line to be constructed above them. Structural analyses were conducted considering earthquake effects, particularly using the load combinations and coefficients recommended by AASHTO LRFD [2017]. The railway bridge is not designed to span any crossings. However, since the existing railroad is situated directly on the ground, a train load is transferred to the pipelines through the ground. To reduce this load transfer, a 25-30cm gap is maintained between the deck and the ground in this protective bridge design proposal. The maximum anticipated displacement of the bridge was considered in the analysis. Site-Specific Earthquake Hazard Analysis was first performed for the proposed bridge due to the critical implications of the pipelines. In the second stage, the structure underwent nonlinear dynamic displacement loading and bridge-pile-soil interaction was analyzed using both linear and nonlinear methods. The performance targets - Uninterrupted Use for DD2a class ground motion and Controlled Damage for DD1 earthquake) - stipulated by the Turkish Bridge Design Standards [TBDS, 2020] were evaluated using strength-based linear and strain-based nonlinear analyses. The results confirmed that the proposed bridge satisfied all target safety levels. In conclusion, this study aims to guide both designers and practitioners, as it is among the first to address the newly enacted TBDS-2020 regulation in Turkiye and serves as an exemplary engineering solution for similar protective bridge designs.

The Pernote landslide event in the Ramban area on April 25, 2024, caused significant damage and displaced many residents. Preliminary investigations identified the landslide as a massive, complex debris slide and flow, primarily involving overburden materials such as mud, silt, clay, and rock fragments. The slide was characterized by several rotational slip planes and debris flow channels. The severity of the event was attributed to explicit geological conditions, including fault and thrust zones, loose consolidated and deformed rocks from the Murree Formation, and thick deposits of Quaternary sediments exceeding similar to 20 m. Heavy antecedent rainfall (100-175 mm) from April 20th to 24th saturated the debris and soil cover, triggering the landslide on the steep slopes (angle > 45 degrees). The total displacement was approximately 40 m, with a depth of about similar to 12 m. The slide zone extended from the crown to the toe, reaching up to the River Chenab, covering approximately 1250 m. The Pernote landslide was not entirely unexpected, as early signs of movement-such as deep fissures, ground cracks, and bulges-were observed as early as 2021. Temporal analysis of high-resolution Google Earth images from 2012 to 2022 supports these observations, revealing signs like old landslide scars, ground cracks, and ongoing landslide activity. Additionally, during the past decade, significant changes in vegetation cover and a 19.2% increase in built-up areas were noted. These findings highlight the importance of monitoring early surface indications as warning signs for effective landslide mitigation, preparedness, and public awareness to prevent loss of life and infrastructure in future events.

Landslides are one of the most hazardous geological processes due to their difficult-to-predict nature and destructive effects, often leading to significant loss of life, infrastructure damage, and environmental disruption. In the Southern Andes of Chile, landslides are particularly frequent and destructive due to a combination of factors, including high seismic activity, steep topography, and the presence of weak, unconsolidated pyroclastic soils. Unfortunately, the geomechanical control of landslide initiation in the Southern Andes is still poorly understood, creating a significant source of uncertainty in developing accurate landslide susceptibility or risk models. This study evaluates the geological and geotechnical factors that control the generation of landslides in pyroclastic soils using in situ data, laboratory analysis and remote sensing approaches. The study area covers the surroundings of the Mocho-Choshuenco Volcanic Complex (MCVC), one of the most explosive volcanoes in the Southern Andes. The results show that the landslides are placed on slopes covered by multiple explosive eruptions that include a period of more than 12 ka. Landslide activity is related to pyroclastic soils with significant weathering and halloysite content. In addition, the geotechnical characteristics show very light soils, with highwater retention capacity, which is vital to induce mechanical instability. The detected deformation may be associated with seasonal precipitation that would increase the pore water pressure and reduce the shear strength of the soil, promoting slow-moving landslides. The geological and geotechnical characteristics of the soils suggests that slow-moving landslides would be extended to a large part of the Southern Andes. Finally, this study contributes to improving hazard assessment to mitigate the impact of landslides on the population, infrastructures and natural resources in the Southern Andes.

Hurricanes are one of the most destructive natural disasters that can cause catastrophic losses to both communities and infrastructure. Assessment of hurricane risk furnishes a spatial depiction of the interplay among hazard, vulnerability, exposure, and mitigation capacity, crucial for understanding and managing the risks hurricanes pose to communities. These assessments aid in gauging the efficacy of existing hurricane mitigation strategies and gauging their resilience across diverse climate change scenarios. A systematic review was conducted, encompassing 94 articles, to scrutinize the structure, data inputs, assumptions, methodologies, perils modelled, and key predictors of hurricane risk. This review identified key research gaps essential for enhancing future risk assessments. The complex interaction between hurricane perils may be disastrous and underestimated in the majority of risk assessments which focus on a single peril, commonly storm surge and flood, resulting in inadequacies in disaster resilience planning. Most risk assessments were based on hurricane frequency rather than hurricane damage, which is more insightful for policymakers. Furthermore, considering secondary indirect impacts stemming from hurricanes, including real estate market and business interruption, could enrich economic impact assessments. Hurricane mitigation measures were the most under-utilised category of predictors leveraged in only 5% of studies. The top six predictive factors for hurricane risk were land use, slope, precipitation, elevation, population density, and soil texture/drainage. Another notable research gap identified was the potential of machine learning techniques in risk assessments, offering advantages over traditional MCDM and numerical models due to their ability to capture complex nonlinear relationships and adaptability to different study regions. Existing machine learning based risk assessments leverage random forest models (42% of studies) followed by neural network models (19% of studies), with further research required to investigate diverse machine learning algorithms such as ensemble models. A further research gap is model validation, in particular assessing transferability to a new study region. Additionally, harnessing simulated data and refining projections related to demographic and built environment dynamics can bolster the sophistication of climate change scenario assessments. By addressing these research gaps, hurricane risk assessments can furnish invaluable insights for national policymakers, facilitating the development of robust hurricane mitigation strategies and the construction of hurricaneresilient communities. To the authors' knowledge, this represents the first literature review specifically dedicated to quantitative hurricane risk assessments, encompassing a comparison of Multi-criteria Decision Making (MCDM), numerical models, and machine learning models. Ultimately, advancements in hurricane risk assessments and modelling stand poised to mitigate potential losses to communities and infrastructure both in the immediate and long-term future. (c) 2025 China University of Geosciences (Beijing) and Peking University. Published by Elsevier B.V. on behalf of China University of Geosciences (Beijing). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).