Search Results (191)

This study examines Phase B of the GREENERGY project focusing on the seismic performance and structural health monitoring of a renovated single-story RC frame with brick masonry infills that received significant strategic structural interventions. The columns were confined with basalt fiber ropes (FR, 4 mm thickness, two layers) in critical regions, the vertical interfaces between infill and concrete were filled with polyurethane PM forming PUFJ (PolyUrethane Flexible Joints), and glass fiber mesh embedded in polyurethane PS was applied as FRPU (Fiber Reinforced PolyUrethane) jacket on the infills. Further, greenery renovations included the attachment of five double-stack concrete planters (each weighing 153 kg) with different support-anchoring configurations and of eight steel frame constructions (40 kg/m²) simulating vertical living walls (VLW) with eight different connection methods. The specimen was subjected to progressively increasing earthquake excitation based on the Thessaloniki 1978 earthquake record with peak ground acceleration ranging from EQ0.07 g to EQ1.40 g. Comprehensive instrumentation included twelve accelerometers, eight draw wire sensors, twenty-two strain gauges, and a network of sixty-one PZTs utilizing the EMI (Electromechanical Impedance) technique. Results demonstrated that the structure sustained extremely high displacement drift levels of 2.62% at EQ1.40 g while maintaining structural integrity and avoiding collapse. The PUFJ and FRPU systems maintained their integrity throughout all excitations, with limited FRPU fracture only locally at extreme crushing zones of two opposite bottom bricks. Columns’ longitudinal reinforcement entered yielding and strain hardening at top and bottom critical regions provided the FR confinement. VLW frames exhibited equally remarkably resilient performance, avoiding collapse despite local anchor degradation in some investigated cases. The planter performance varied significantly, yet avoiding overturning in all cases. Steel rod anchored planter demonstrated superior performance while simply supported configurations on polyurethane pads exhibited significant rocking and base sliding displacement of ±4 cm at maximum intensity. PZT structural health monitoring (SHM) sensors successfully tracked damage progression. RMSD indices of PZT recordings provided quantifiable damage assessment. Elevated RMSD values corresponded well to visually observed local damages while lower RMSD values in columns 1 and 2 compared with columns 3 and 4 suggested that basalt rope wrapping together with PUFJ and FRPU jacketed infills in two directions could restrict concrete core disintegration more effectively. The experiments validate the advanced structural interventions and vertical forest renovations, ensuring human life protection during successive extreme EQ excitations of deficient existing building stock. Full article

The objective of this study is to develop an artificial intelligence algorithm that can predict both the risk level and damage level of reinforced concrete structures through classification and proportioning. This algorithm identifies buildings that require preventive measures before an earthquake and buildings that require immediate repair or demolition after an earthquake. A key aspect of the approach is calculating each building’s risk level as the ratio of its risky story to the total number of stories. That calculation provides a normalized figure, enabling comparison between buildings of varying sizes and complexities in an equitable way. The dataset of this study includes 100 buildings affected by previous earthquakes in Türkiye and 782 buildings with detailed seismic analysis. Thirteen different building parameters, structural, seismic, and geometric, have been considered within the scope of this study. Rapid visual screening (RVS) methods were applied for structural integrity analysis, and machine learning models were used for improvement in accuracy and efficiency. In the comparison of the model sets, the approach achieved the highest accuracy of 77% with an ensemble of four models. The results demonstrate the value of blending AI with traditional methodologies for risk analysis. It shows a viable and scalable mechanism for prioritization of retrofit and inspections and helps engineers and policymakers enhance disaster preparedness. By identifying structures at high risk, this work contributes towards overall aims for earthquake resilience in buildings. This study introduces a Pearson-correlation-based feature analysis and a Random Oversampling strategy to enhance model balance. The ensemble model achieved 83% external accuracy and outperformed the traditional RVS method (68%), reducing computation time from minutes to seconds. Full article

To evaluate the seismic resilience and post-earthquake sustainability of long-span deck-type reinforced concrete arch bridges, this paper constructs a seismic resilience assessment framework based on seismic vulnerability theory, utilizing post-earthquake functional loss and recovery functions. A post-earthquake sustainability evaluation method is further developed using social and environmental indicators. Taking Shatuo Bridge as an engineering case study, the seismic vulnerability, seismic resilience, and post-earthquake sustainability are evaluated. The influences of functional recovery functions and the rise-to-span ratio on seismic resilience are compared and analyzed. The results show that under the same Peak Ground Acceleration (PGA), the seismic vulnerability of the system is greater than that of individual components, and the vulnerability of bearings is greater than that of the main arch ring and columns. The seismic resilience of reinforced concrete arch bridges decreases with increasing PGA, showing a trend that is initially rapid and then slows down. The exponential functional recovery function yields the highest seismic resilience, followed by the linear function, and the triangular function yields the lowest. The resilience obtained by the improved method lies between that of the exponential and triangular functions, consistent with the failure pattern of arch bridges, providing preliminary insights and a methodological reference for seismic resilience assessment. The rise-to-span ratio significantly affects the seismic resilience of deck-type reinforced concrete arch bridges, with a ratio of 1/6 yielding the highest resilience, followed by 1/7, 1/5, 1/8, and 1/4. Under seismic action, the environmental cost caused by damage to the main arch ring is significantly greater than that caused by damage to columns and bearings, and the environmental cost due to column damage is greater than that due to bearing damage. Post-earthquake time loss is primarily controlled by damage to the main arch ring. It is acknowledged that the findings are subject to limitations, including model simplifications and the use of idealized recovery functions, as discussed in the full paper. Full article

Irregular multistorey buildings are prone to seismic forces due to torsional effects resulting from the eccentricity between the mass and stiffness centres. Shear walls are essential in multistorey buildings for improving structural behaviour when subjected to earthquake loads. The seismic response of buildings is highly sensitive to the placement and configuration of shear walls within the building infrastructure. This research focuses on optimising the location of shear walls in a T-shaped irregular reinforced concrete structure for better seismic resilience. The structural analysis is carried out, and the building is evaluated via the response spectrum as per the provisions of IS 1893:2016. This study examines various shear wall configurations to achieve optimised modal mass participation, thereby reducing dynamic irregularities and enhancing overall seismic performance. The impact of these optimised locations is assessed across various seismic zones in India, with a focus on critical response parameters, including lateral displacement, interstorey drift, storey shear, and base shear. The results reveal that strategically optimised shear wall placement significantly enhances seismic performance by reducing lateral drift and torsional effects. In this study, the shear wall configurations that resulted in higher modal participation factors and lower eccentricities between the centre of mass and the centre of stiffness demonstrated a superior seismic performance across all considered seismic zones. Full article

This paper presents an approach to risk mitigation strategies through seismic vulnerability of buildings’ non-structural elements (NSEs) proposing practical and accessible strategies for risk reduction aligned with the United Nations Sustainable Development Goals (SDG) framework. NSEs play a crucial role in the overall safety and resilience of built environments during seismic events. However, their vulnerability is often underestimated, despite their potential to cause significant human, economic, and social losses. Moreover, NSEs remain widely overlooked in both seismic risk assessments and mitigation strategies, including risk education. This issue directly impacts multiple SDGs. NSE damage exacerbates poverty by increasing financial burdens due to repair and recovery costs. It also affects access to quality education, not only by disrupting school infrastructure but also by limiting access to knowledge, which is essential for strengthening the coping capacity of communities. Furthermore, seismic risk mitigation must be inclusive to reduce inequalities, ensuring that safety is not a privilege but a right for all. Lastly, NSE vulnerability directly influences the resilience and sustainability of cities and communities, affecting urban safety and disaster preparedness. Simple mitigation actions, such as proper anchoring, reinforcement, or improved design guidelines, could drastically reduce their vulnerability and related consequences. Raising awareness of this underestimated issue is essential to foster effective policies and interventions. Full article

The sustainable rehabilitation of existing buildings is essential to achieve urban resilience, resource efficiency and seismic risk reduction. This study investigates an integrated retrofit strategy that combines vertical extension with inter-story isolation to simultaneously enhance seismic performance and energy efficiency, creating additional usable space without additional land consumption. The inter-story isolation mechanism reduces seismic demand by decoupling a new and existing structure and introducing beneficial damping effects, whereas vertical extension improves a building’s envelope to reduce energy demands for heating and cooling. A tailored design methodology for integrated intervention is presented, according to which, for the structural part, a two-degrees-of-freedom dynamic model is adopted to design the characteristics of the isolation layer. The methodology is applied to a case-study building located in L’Aquila, Italy, where two alternative vertical extensions, one rigid and one lightweight, are analyzed. Time-history analyses and energy simulations for annual primary energy demand are carried out to assess the structural and thermal performance of the integrated retrofit. The results indicate that the proposed solution can reduce top-floor acceleration by up to 35%, inter-story drift by 30–35%, base shear by over 30% and primary energy demand by 11%, demonstrating its effectiveness in improving both seismic safety and energy performance. The main novelty of this study lies in the systematic integration of inter-story isolation with building envelope enhancement through vertical extension, offering a unified design framework that merges structural and energy retrofitting objectives into a single sustainable intervention. Full article

Disaster management requires the development of effective technologies for managing both pre-and post-disaster processes. Therefore, utilizing effective tools and techniques to mitigate the disaster risks or lower the adversarial impacts is essential. Over the last decade, digital twin (DT) applications have found a wider implementation area for varying purposes, but most importantly, they are utilized for simulating disaster impacts. This study aims to develop an open-source digital twin (DT) framework for earthquake disaster management in the Cayirova district of Kocaeli, Türkiye, one of the country’s most seismically active regions. The primary objective is to enhance local resilience by integrating multi-source data into a unified digital environment that supports risk assessment, response planning, and recovery coordination. The digital model developed using QGIS (3.40.9 Bratislava), Autodesk InfraWorks 2025 software for DT modeling integrates various data types, including geospatial, environmental, transportation, utility, and demographic data. As a result, the developed model is expected to be used as a digital database for disaster management, storing both geospatial and building data in a unified structure. The developed model also aims to contribute to sustainable practices in cities, where disaster risks are particularly critical. In this respect, the developed model is expected to create sustainable logistics chains and sustainable targets aiming to reduce the number of people affected by disasters, reducing the direct economic losses caused by disasters. In this framework, the developed model is expected to further assess seismic risk and mitigate risks with DTs. These capabilities enable the project to establish an open-source district-level DT system implemented for the first time in Cayirova, provide an alternative disaster model focused on region-specific earthquakes, and integrate 2D/3D assets into an operational, ready-to-respond digital database. In terms of practical importance, the model provides a digital database (digital backup) that can be used in emergencies, helping decision-makers make faster, data-driven decisions. The significance of this study lies in bridging the gap between urban digitalization and disaster resilience by providing a scalable and transparent tool for local governments. Ultimately, the developed DT contributes to sustainable urban management, enhancing preparedness, adaptive capacity, and post-disaster recovery efficiency. Full article

Shipping expansion, offshore energy generation, fish farming, and construction work radiate high levels of underwater noise, which may critically stress marine ecosystems. Tools for simulating, analyzing, and forecasting underwater noise can be of great help in understanding the impact of underwater radiated noise both on the environment and on man-made equipment, such as underwater communication and telemetry systems. To address this challenge, we developed a web-based Marine Noise Analysis Tool (MNAT) that models, simulates, and predicts underwater radiated noise levels. To reproduce realistic shipping conditions, MNAT combines real-time Automatic Identification System data with environmental data using broadly accepted underwater acoustic propagation models, including Bellhop and RAM. Moreover, MNAT can simulate other kinds of noise sources, such as seismic airguns. It features an intuitive interface enabling real-time tracking, noise impact assessment, and interactive visualizations. MNAT’s noise modeling capabilities allow the user to design resilient communication systems in different noise conditions, analyze maritime noise data, and forecast future noise levels, with potential contributions to the design of noise-resilient systems, to the optimization of environmental monitoring device deployments, and to noise mitigation policymaking. MNAT has been made available for the community at a public GIT repository. Full article

This paper presents a probabilistic methodology for generating fragility surfaces for low- to mid-rise steel moment-resisting frames (MRFs) under fire-following-earthquake (FFE). The framework integrates nonlinear dynamic seismic analysis, residual deformation transfer, and temperature-dependent fire simulations within a Monte Carlo environment, while explicitly accounting for uncertainties in structural properties, ground motions, and fire simulation. A fiber-based modeling strategy is employed, combining temperature-sensitive steel materials with fatigue and fracture wrappers to capture cyclic deterioration and abrupt failure. This formulation yields earthquake-only and fire-only fragility curves along the surface boundaries, while interior points quantify the joint fragility response under sequential hazards. The methodology is benchmarked against a machine learning (ML) synthesis framework originally developed for earthquake–tsunami applications and extended here to FFE. Numerical results for a three-story steel MRF show excellent agreement (R² > 0.95, RMSE < 0.02) between simulated and ML-generated surfaces, demonstrating both the efficiency and hazard-neutral adaptability of the ML framework for multi-hazard resilience assessment. Full article

Assessing seismic vulnerability is a critical step in evaluating the resilience of existing buildings, and fragility curves are widely used to quantify the probability of damage under varying levels of seismic intensity. However, traditional methods for generating these curves often rely on generalized assumptions that may not accurately capture the seismic behavior of diverse building types within a region. This limitation is particularly evident for non-engineered masonry buildings, which typically lack standardized designs. Their irregular and informal construction makes them difficult to assess using conventional approaches. Transformer-based models, a type of machine learning (ML) technique, offer a promising alternative. These models can identify patterns and relationships in available data, making them well suited for developing seismic fragility curves with improved efficiency and accuracy. While transformers are relatively new to civil engineering, their application to seismic fragility assessment has been largely unexplored. This study presents a pioneering effort to apply transformer models for deriving fragility curves for non-engineered masonry buildings. A comprehensive dataset of 646 masonry buildings observed in Malawi is used to train the models. The transformers are trained to predict the probability of four damage states: Light Damage, Severe Damage, Near Collapse, and Collapse based on Peak Ground Acceleration (PGA). The performance of the transformer-based approach is compared with other ML methods, demonstrating its strong potential for more efficient and accurate seismic fragility assessment. Future work could adopt the proposed methodology and extend the approach by incorporating larger datasets, additional regional contexts, and alternative ML techniques to further enhance predictive performance. Full article

Integrated seismic–environmental retrofit is gaining attention in research and practice, as it combines resilience and sustainability objectives in building retrofits. However, current research and practice remain fragmented. This paper presents a systematic literature review to analyse how retrofit is addressed across four key dimensions: structural, environmental, social, and governance. A thematic analysis in NVivo was combined with Python-based quantitative analysis of code frequency and co-occurrence. The integrated retrofit literature reframes environmental assessment, shifting towards whole-building lifecycle assessment and having seismic environmental impacts and energy efficiency as embedded components. Retrofit practices are mainly discussed in technical and compliance terms, but are not properly examined using unified quantitative metrics; the broad use of metrics and indicators limits comparability and replication. Social and governance dimensions remain peripheral, with weak connections to structural and environmental dimensions, which constrain cross-domain integration and challenge scaling up retrofit interventions. These gaps encompass the barriers facing integrated retrofit, with potential pathways to overcome, including aligned standards and datasets, capacity building, community engagement, and coordinated regulatory frameworks. Full article

Addressing earthquake risk remains a significant global challenge, requiring rapid assessment of evacuation shelters for effective disaster response. Existing frameworks, such as FEMA’s Hazus, Copernicus EMS, and UNOSAT, offer valuable insights but are typically regional, static, and event-focused, lacking mechanisms for continuous shelter-level updates. This study introduces the Shelter Hazard Impact and Lifeline Outage Estimation (SHILOE) framework. SHILOE is a stepwise estimation approach integrating multisensor datasets for time-scaled assessments of shelter functionality and operability. These datasets include seismic intensity, liquefaction probability, tsunami inundation, IoT-derived power outage data, communication network disruptions, and social media. Application to the 2024 Noto Peninsula earthquake showed that ≥93.6% of designated and activated shelters were impacted by at least one hazard, with all experiencing at least one lifeline outage. The framework delivers estimates through three phases: immediate (within tens of minutes, e.g., simulation-based hazard models and lifeline data), intermediate (days, e.g., observation-based datasets), and refinement (ongoing, e.g., Social Networking Service and detailed field surveys). By progressively incorporating new data across these phases, SHILOE generates dynamic, facility-level insights that capture evolving hazard exposure and lifeline status. These outputs provide actionable information for emergency managers to prioritize resources, reinforce shelters, and sustain critical services, thereby advancing disaster resilience. Full article

In recent years, the increasing frequency of global seismic events has imposed severe impacts on human society. Timely and accurate assessment of post-earthquake damage and recovery is essential for developing effective emergency response strategies and enhancing urban resilience. This study investigates 11 provinces in Turkey affected by the February 2023 Turkey–Syria earthquake, conducting a multidimensional evaluation of disaster loss and recovery. For loss assessment, existing studies typically focus on changes in the total value of nighttime lights at the regional level, overlooking variations at the pixel scale. In this study, we introduce a pixel-level NTL loss metric, which provides finer-grained insights and helps interpret outcomes driven by spatial heterogeneity. For recovery assessment, we propose a Composite Nighttime Light Index (CNLI) that integrates multiple recovery-phase indicators into a single quantitative measure, thus capturing more information than a one-dimensional metric. To account for complex interrelationships among indicators, a Bayesian network is employed, which moves beyond the conventional independence assumption. Moreover, an information gain (IG) approach is applied to optimize indicator weights, minimizing subjectivity and avoiding abnormal weight distributions compared with traditional methods, thereby ensuring a more objective construction of the Resilience Index (RI). Results show that Sanliurfa, Kilis, and Hatay suffered the most severe damage; Kahramanmaras and Malatya exhibited the lowest CNLI values, while Hatay, Kilis, and Gaziantep showed higher CNLI values. In contrast, Gaziantep and Adana obtained the highest RI values. Since CNLI reflects actual recovery performance while RI characterizes inherent resilience, accordingly, effectively linking CNLI and RI establishes a dual-perspective and novel framework, the 11 provinces are classified into four categories, and differentiated recovery strategies are suggested. This study contributes a refined quantitative framework for post-earthquake loss and recovery assessment and provides scientific evidence to support emergency response and targeted reconstruction. Full article

This article presents an extensive experimental investigation of the dynamic characteristics of three-arch historical masonry bridges, using Operational Modal Analysis (OMA). The research thoroughly characterizes the dynamic behavior of four representative masonry bridges from the Apulia Region in Southern Italy through detailed experimental campaigns. These campaigns employed calibrated and optimally implemented accelerometric monitoring systems to acquire high-quality dynamic data under controlled excitation and environmental conditions. The selected bridges include the Santa Teresa Bridge in Bitonto, the Roman Bridge in Bovino, the Roman Bridge in Ascoli Satriano and a moderner road bridge on the Provincial Road SP123 in Troia; they span almost two millennia of construction history. The experimental framework incorporated several non-invasive excitation methods, including controlled vehicle passes, instrumented hammer impacts and ambient vibration tests, strategically chosen for optimal signal quality and heritage preservation. This investigation demonstrates the feasibility of capturing the dynamic behavior of these complex and specific historic structures through customized sensor configurations and various excitation methods. The resulting natural frequencies and mode shapes are accurate, robust, and reliable considering the extended data set used, and have allowed a rigorous seismic assessment. Eventually, this comprehensive data set establishes a fundamental basis for understanding and predicting the seismic response of several three-span masonry bridges to accurately identify their long-term resilience and effective conservation planning of these valuable and vulnerable heritage structures. In conclusion, the data comparison enabled the formulation of a predictive equation for the identification of the first natural frequency of bridges from geometric characteristics. 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