CLT rocking wall systems are increasingly being adopted as lateral load-resisting systems in seismic regions due to their numerous advantages and benefits. As wall hold-down designs advance toward ductile fuses and resilient joints, there is a growing need for diaphragm wall-to-floor connections to evolve beyond conventional bracket and fastener systems, where the connection must facilitate large wall uplift demands and accommodate free rocking of the wall while reliably transferring horizontal forces and allowing the wall to undergo repeatable cycles without loss of strength or stiffness. This paper presents two innovative shear keys that provide complete force and displacement compatibility between the rocking walls and the diaphragm (gravity frame), both in-plane and out-of-plane. The performance of these shear keys is presented and assessed as part of full-scale experimental tests of a resilient two-storey CLT wall structure, and the design methodology and considerations for both shear keys are described in detail. Cyclic quasi-static test series are conducted to drift levels of up to 4%, and both shear key configurations maintained continuous and adequate horizontal force transfer throughout the testing programme. This enabled the cyclic damage free performance of the resilient structure while demonstrating resilient characteristics, with the shear keys remaining elastic and free from damage (or yield) throughout the test series. The proposed shear keys have the potential to complement recent developments in innovative and advanced rocking systems that are increasingly being adopted in projects worldwide where resilience and performance based design are key objectives, for both new construction and retrofit applications.
The resilience of highway bridge networks is critical to maintaining quality of community life. Bridge networks must be sufficiently resilient to withstand various hazards, mitigate the impacts of catastrophic events, and rapidly recover to pre-event conditions. Bridges ensure continuity of daily commuting and enable the timely delivery of emergency aid and resources to affected communities, thereby accelerating infrastructure restoration. Moreover, bridges should not be considered as independent components, as their performance plays a vital role in preventing or minimizing community-level disruptions and outages. This study evaluates the seismic resilience of a regional bridge network by emphasizing the interconnectedness and interdependence among individual bridges and emergency facilities. An Artificial Neural Network (ANN)–based resilience assessment framework is developed and applied to a bridge network in Vancouver, British Columbia, consisting of 11 bridges across seven major routes. Using a network-based reliability model and simulated earthquake events with varying magnitudes and epicenter locations, resilience and reliability indices are calculated and used to train the ANN. The trained model efficiently predicts resilience and reliability metrics for different seismic scenarios, demonstrating high accuracy with significantly reduced computational cost and time.
Rocking Cross-Laminated Timber (CLT) wall systems incorporating resilient self-centring hold-down devices have emerged as low-damage alternatives to conventional timber lateral load resisting systems. Although experimental studies have demonstrated stable flag-shaped hysteretic behaviour and minimal residual drift, collapse based calibration of seismic behaviour factors within the New Zealand design framework has not been systematically performed. Recent revisions to the New Zealand National Seismic Hazard Model (NSHM 2022) have led to increased hazard intensities in several regions and underpin the forthcoming loading standard Draft Technical Specifications 1170.5 (DZ TS 1170.5: 2024), necessitating reassessment of collapse reliability for emerging structural systems This study evaluates the collapse performance of balloon-type rocking CLT shear walls using a reliability-based methodology consistent with FEMA P695 (2009) and aligned with the provisions of DZ TS 1170.5:2024 provisions. Nine two-dimensional archetype buildings, including single-wall (2–8 storeys) and coupled-wall (4–12 storeys) configurations, were designed for Wellington Site Class II conditions using displacement-based procedures. Nonlinear models were calibrated against extensive component, sub-assembly, and full-scale experimental testing up to 4.1% drift.Incremental Dynamic Analysis (IDA) was performed using the FEMA P695 far-field ground motion set, and collapse performance was quantified through Collapse Margin Ratios (CMR) and Adjusted Collapse Margin Ratios (ACMR) incorporating spectral shape effects and system uncertainties. All archetypes satisfied both FEMA P695 acceptance criteria and the implied collapse reliability objectives of DZ TS 1170.5. The results demonstrate that rocking CLT walls with resilient hold-downs designed with kμ = 3.5 provide adequate collapse safety margins for single-wall systems up to 8 storeys and coupled-wall systems up to 12 storeys under updated Wellington hazard conditions.
Bridges are essential components of road networks, and their failure can lead to significant economic and social repercussions, as shown by many recent failures worldwide. In Italy, the dramatic collapse of the Morandi bridge prompted the introduction of improved management of its roadway infrastructure, leading to the issuing of multi-level multi-hazard assessment guidelines for bridge management, assessment and monitoring in 2020. Although these comprehensive guidelines marked a significant progress in standardising management practices, they do not facilitate detailed prioritisation of thousands of bridges due to their qualitative description of risk. To mitigate such a limitation, this study proposes an enhanced quantitative risk-based method for prioritising existing bridges for both structural/traffic and seismic hazards, built upon a recently proposed methodology settled on a simplified index-based evaluation of the three primary risk components: vulnerability, exposure, and hazard. The performance of the proposed enhanced approach, which includes hazard-dependent vulnerability parameters, is evaluated in different versions, when applied to a large case-study of 415 bridges in northern Italy. It is demonstrated that the proposed approach and indices can generate rankings that improve the differentiation among bridge priority, allowing for more nuanced prioritisation than most existing guidelines (e.g., within each risk class currently foreseen by the ones in Italy) for both traffic and seismic risk, thereby enhancing bridge management strategies.
Long-span spatial structures have been extensively utilized in the design of stadiums, transportation terminals, and various public infrastructure facilities. Such structures are widely recognized for their superior structural performance and remarkable architectural qualities. Nevertheless, the pursuit of lightweight designs and expansive spans frequently undermines structural redundancy, consequently increasing the risk of sudden collapse. A significant number of collapses of long-span spatial structures have been reported in recent years. These incidents have resulted in profound economic and social ramifications. Accordingly, this study presents a thorough review of the factors contributing to collapse and the associated mitigation strategies. It addresses both internal and external contributors to collapse and suggests corresponding preventive measures. Additionally, this review explores state-of-the-art technologies for intelligent maintenance. Finally, several critical research gaps are identified. These include, in particular, complex collapse mechanisms, multi-hazard interactions, and the practical implementation of intelligent technologies. On the basis of these findings, this study also outlines several key research avenues to advance the design and maintenance of long-span spatial structures.
This study presents a comprehensive framework for evaluating seismic resilience of old Reinforced Concrete (RC) buildings using YouSimulator, an urban earthquake platform enabling macroscopic modeling and analysis of large-scale building regions through nonlinear time-history analysis. The framework estimates resilience curves by calculating key post-earthquake metrics: building damage states, repair/retrofitting costs, workforce demand, Construction & Demolition Waste (CDW), recovery time and CO2 emissions, providing a holistic measure of urban vulnerability and post-retrofit gains. It was applied to 1,252 old RC buildings in central Thessaloniki, Greece, under three earthquake scenarios (frequent, design, and rare). Frequent earthquakes cause minor structural damage but high repair costs, reducing functionality (load-bearing capacity) by 11.9% with two-year recovery and 1,500 workers; post-repair functionality improvement is negligible (0.3%). Design-level earthquakes cause major damage without collapse, reducing functionality by 26.6%, with repair and retrofitting costs averaging 22.4% of replacement value, about five-year recovery and 2,500 workers; retrofitting improves functionality slightly (11.9%). In extreme cases, CDW reaches 290,698 tons, generating 57,558 tons of CO₂. Rare earthquakes cause catastrophic losses (>50% functionality), collapses, and recovery exceeding 12 years with 2,500 workers. CDW management becomes critical, averaging 537,680 tons and about 21.8 months for disposal, with peaks at 936,364 tons generating 185,400 tons of CO₂, comparable to the 2023 Turkey-Syria earthquake. Post-repair and retrofitting can increase city’s functionality up to 46.5% over pre-event conditions. The framework provides a robust tool for citywide seismic assessment, enabling decision-makers to identify vulnerable buildings, evaluate resilience, allocate resources and strengthen urban preparedness.
Self-Centering Coupled Wall (SCCW) system is an innovative seismic force-resisting system designed for high-rise applications. SCCW is similar to the conventional reinforced concrete coupled wall (RCCW) system with the following two exceptions: 1) the SCCW replaces the reinforced concrete coupling beams with the steel selfcentering conical friction dampers (SCFDs); 2) the base of the SCCW is equipped with the steel rocking base dampers (RBDs) which is designed to rock during earthquake shaking. SCCW system dissipates the earthquake energy mainly through SCFDs and RBDs instead of yielding in the reinforced concrete coupling beams and the wall base. Since the SCCW and RBDs are specially designed to dissipate the earthquake energy through a stable and damage-free mechanism, the SCCW is expected to be damage-free after a strong earthquake shaking. More importantly, SCCW is designed to re-center after the shaking, making this system more resilient for applications in high seismicity regions. In this study, the equivalent energy design procedure (EEDP) developed for fused structural systems is adopted to ensure that the SCCW system can achieve high performance under different levels of ground shaking intensities. For this purpose, three prototype buildings with different heights (15-story, 25-story, and 35-story) located in Vancouver, British Columbia, Canada, are designed using EEDP. Detailed finite element models of the prototypes are developed using OpenSees and are subjected to a series of nonlinear time history analyses. The analysis results indicate that the SSCW system, designed using the EEDP, performs well under different levels of earthquake shaking intensities. Hence, this system can be used as an effective seismic force-resisting system for high-rise applications.
Despite the inherent ductility of steel as a material, design and detailing of high-performance ductile seismic fuses remains a challenge in seismic design. The connections for such fuses must be detailed carefully given the brittle nature of the weld material itself and the poor performance of the heat affected zones due to welding. In the last two decades, cast steel fuses have emerged as an innovative solution for the development of seismic fuses. Steel casting facilitates the design of steel shapes with a freeform geometry, which can minimize stress/strain concentration as well as residual stresses. In addition, steel casting eliminates the need for welded connections and heat affected zones in and close to the critical yielding regions. Furthermore, the use of steel casting enables a more economical, faster, and more efficient production of complex and mass-produced shapes. This review paper starts with a background on steel casting, casting processes, and its application in structural engineering. A detailed review of application of steel casting technology in the development of high-performance seismic fuses in steel structures is provided. Lastly, two example structures are designed once with a high-performance steel energy dissipative component and then alternatively designed with a high-performance cast steel fuse. The example structures are subjected to a suite of forty ground motions scaled to match the design uniform hazard spectrum. The global response of the example structures is presented to highlight the advantages of previously proposed cast steel fuses.
Past earthquake-related events have highlighted the vulnerability of Italian precast buildings, which are widely used for industrial purposes and therefore represent a significant source of potential economic loss in the event of an earthquake. In this context, insurance companies play a decisive role in the financial protection against earthquake-induced damage, and rapid and reliable procedures for seismic risk classification are essential for the preliminary definition of suitable policies. This study presents a taxonomy-based rapid assessment procedure for the seismic risk classification of single-storey precast reinforced concrete (RC) buildings. The approach introduces a classification framework that correlates building characteristics, construction period and seismic code evolution with corresponding fragility and demand models.By integrating fragility functions for structural elements, non-structural components and operational content (such as plant and machinery), the method enables a multi-component damage assessment leading to damage-based risk classes. The method was developed considering five representative building classes categorized based on their construction period and seismic regulations. The approach was validated using a case study of a RC precast building damaged during the 2012 Emilia earthquake, for which the main characteristics of the seismic event and the observed damage distribution are considered. This showed a strong correlation between the estimated and observed damage states, confirming the ability of the procedure to capture the main sources of structural and non-structural vulnerability. The proposed methodology provides a practical and transparent tool for large-scale screening and preliminary decision-making and is well suited for implementation in user-oriented software tools that allow even non-experts to assess the seismic classification of single-story precast industrial buildings built in Italy from the post-war period to the present day.
In this study, the impact behavior of an eccentrically prestressed simply-supported concrete beam of prophase bending-stretching retention subjected to a constant moving load is investigated for structure impact resilience evaluation. The finite element method is adopted for beam behavior modeling and the Newmark-β time-domain integration technique is adopted for response computation. The geometric stiffness matrices, due to the prestressing axial compression and the prophase bending-stretching caused by eccentric prestressing and self-weight, are considered. Parametric study on varying prestressing force and eccentricity shows that eccentric prestressing will induce up to 58 % reduction of the fundamental frequency with the increase of the prestressing force. Moving-load induced impact for the considered eccentrically prestressed beam will amplify the peak bending moment by up to 53 %, while shift the position of peak dynamic response in the positive bending moment zone away from the midspan by more than ± 15 % of the span length. For the studied cases, the consideration of the eccentric prestressing will enlarge the shift of the moving load-induced peak bending moment position but will not induce the presence of new bending moment zone under moving load.
This paper presents an advanced seismic design concept for bridge structures using Ductile Self-Centring Joint (DSJ) that enables highly resilient, low-damage performance under major earthquake events. The DSJ integrates a central rotational bearing with multiple Resilient Slip Friction Joints (RSFJs), arranged to provide controlled rocking, flag-shaped hysteresis, and self-centring capability. This configuration allows the joint to emulate the behaviour of a plastic hinge while avoiding inelastic deformation in concrete members, thereby addressing long-standing challenges associated with damage accumulation, residual drift, and post-earthquake repairability in conventional ductile bridge systems.A comprehensive suite of nonlinear analyses—including nonlinear static pushover, cyclic pushover, and nonlinear time-history simulations—was performed on a three-span timber-concrete hybrid bridge with timber beam composite with concrete deck and integral diaphragms at supports modelled in SAP2000 to evaluate the joint’s global and local seismic performance. The DSJ was modelled using friction–spring link elements to capture RSFJ hysteresis, combined with a rotationally free bearing to accommodate uplift and rocking. Results show that the introduction of DSJs significantly reduces seismic demands: base shear and peak acceleration decreased by up to 65% and 64%, respectively, compared with a conventional elastic low-damage configuration. Equivalent viscous damping ratios of approximately 20% were achieved, and displacement demands were substantially reduced across a broad suite of matched near-fault and far-field ground motions. RSFJ hysteresis loops remained fully elastic throughout all analyses, confirming the self-centring mechanism and absence of structural damage.Overall, these findings demonstrate that the proposed DSJ offers a practical, replaceable, and highly effective solution for next-generation low-damage seismic bridge design.
Power plants are important lifeline engineering in urban construction. To improve their sustaining ability, adaptability, and recovery capabilities to uncertain hazards, it is vital for realizing engineering resilience, which contributes the reduction of economic losses and adverse social impacts. Functional quantitation has become more and more important for the resilience assessment of power plants after disasters. Quantitative evaluation of actual functionality for power plants becomes more significant to the resilience evaluation against disasters. However, it is difficult to understand the relationship between components and systems in power plants, which are composed of different kinds of equipment and pipeline. For thermal power plants, there is little research on the quantification of functional loss after disasters. This paper analyzes the main power generation system in thermal power plants and proposes a novel functional quantitative analysis method, namely functional state method. Quantification method of post-disaster functional loss for the main power generation system is presented. The functional quantification method proposed in this paper can be used further to conduct quantitative analysis of functional recovery, and evaluate resilience of the post-disaster power plants, based on the input-output relationships of components and systems.
Urban flash-flood management requires turning probabilistic forecasts into concrete, resource-constrained actions. We develop a forecast-to-decision pipeline that links calibrated incident-level harm prediction with prescriptive optimization. Using a processed DesignSafe-CI Texas flash-flood incident dataset covering 2005-2019 (6,137 incidents; 2.17% harm prevalence), we construct GIS-derived predictors of hazard intensity, exposure, and transportation infrastructure, plus physically motivated interactions (e.g., precipitation & times; bridge density). An & ell;(1)-regularized logistic model (LASSO) with out-of-fold isotonic calibration provides decision-grade probabilities and automatic feature selection. Temporal generalization is assessed with a strict year holdout (train 2005-2018; test 2019), leave-one-year-out (LOYO), and rolling-origin schemes. On the 2019 holdout, the calibrated model attains ROC-AUC 0.783, PR-AUC 0.127 (about six times prevalence), and Brier 0.021, although the holdout contains only five positive cases and therefore yields wide uncertainty intervals; complementary LOYO validation gives AUC values spanning 0.66-0.99, reflecting interannual variability that random splits obscure. We embed the learned calibrator as a piecewise-linear mapping in a mixed-integer program that selects intervention sets meeting risk-coverage targets at minimum effort. Results show strong spatial concentration of expected harm within the candidate set of historically active locations: intervening at 61 of 220 candidate assets (28%) captures 80% of baseline risk; 98 candidate assets (45%) achieve 90%, with certified optimality gaps < 0.1%. Leave-one-year-out frontier analysis confirms this concentration pattern is stable across years (median: 27% of assets for 80% coverage; IQR < 2 percentage points), indicating that the prescriptive conclusions are not artifacts of the single holdout year. The framework is reproducible, interpretable, and operationally tractable, providing a transparent pathway from geomatics-informed risk to prioritized preparedness and response portfolios for known high-risk locations.
As earthquakes continue to pose significant risks to the built environment, the concept of seismic resilience has gained attention as a means to evaluate how effectively buildings recover functionality after such disruptive events. This study aims to develop a quantitative assessment framework that evaluates the post-seismic resilience of buildings using Expected Annual Resilience (EAR). By introducing the Expected Annual Resilience as a core metric, the framework enables integrated evaluation of both the extent of functional loss and the capacity for recovery over time, based on seismic hazard probabilities. EAR is defined as a probabilistic metric that quantifies the expected level of building functionality maintained or recovered throughout a year, considering the annual probability and frequency of seismic events. It reflects both the extent of retained functionality and the capacity for recovery over time across various earthquake scenarios. To implement this concept, a quantitative assessment framework is developed by integrating seismic hazard models, post-seismic functionality curves, and recovery timelines. Applying the framework to school buildings is particularly significant, as schools are essential community infrastructure and are often used as shelters after earthquakes. A seismic hazard curve was developed based on more than 2,000 historical ground motion records observed across Korea. Using each school's fragility function and loss function, the retained functionality following seismic events was estimated, and corresponding EAR values were calculated. EAR were evaluated across three seismic hazard levels corresponding to different probability of exceedance scenarios, categorized as low, medium, and high hazard. The results indicate that buildings under moderate hazard conditions exhibited EAR values approximately 8.6% higher than those under slight hazard, and 4.1% higher than those under low hazard. These findings suggest that as seismic hazard levels increase, the expected annual resilience decreases, highlighting the metric’s sensitivity to varying hazard intensities. The proposed methodology may be helpful for many stakeholders and decision makers for pre- and post-earthquake assessments to quantify seismic performance.
Current sustainability assessments for buildings often fail to account for the interactions between environmental risks, structural safety, and green performance, resulting in suboptimal prioritization for maintenance and retrofitting. To solve this problem, a novel framework is proposed to evaluate and prioritize the risk-based sustainability performance at the building asset level. The proposed framework integrates three different key aspects, including environmental, structural, and green, due to the multifaceted nature of sustainability. In the presented approach, fuzzy systems are modeled for each of these three aspects to address the uncertainties within the problem. These models introduced three indices: Environmental Risk index (considering factors like fault proximity and vegetation barriers), Structural Safety index (evaluating factors such as material quality and age), and Green index (evaluating factors such as energy efficiency and carbon footprint). The efficiency of the assessment process is upgraded using the results of three ensemble learning models, XGBoost, AdaBoost, and Random Forest, which are trained on a generated database obtained through the fuzzy system evaluations. Due to the complexities existing in the problem under study, a Graphical User Interface (GUI) is also presented based on the final systems, making the use of the computational framework simpler for the user. The framework helps engineers to make more informed decisions regarding maintenance, retrofitting, and redevelopment strategies in a construction project. The successful combination of fuzzy systems with machine learning and providing a user-friendly interface makes this research a significant contribution to advancing sustainable constructions.
With the rapid development of the wind power industry, wind turbine towers are evolving toward greater heights and larger capacities. When the tower height exceeds 140 m, hybrid towers demonstrate significant advantages over conventional steel towers in terms of economic efficiency and safety. Specifically, this hybrid system comprises a lower segmental concrete section compressed by external prestressed cables to prevent tension, a steel transition section, and an upper steel tower section. Although hybrid towers exceeding 160 m have been implemented in engineering practice, refined numerical simulations concerning their dynamic response under strong wind conditions and seismic actions remain limited. In this study, a 160 m-class hybrid tower located in a region with a seismic design intensity of 8 was selected as the prototype. Numerical models were established using ABAQUS to analyze the dynamic responses under strong wind conditions and seismic actions, respectively. Based on the Davenport fluctuating wind speed spectrum, a wind load time history corresponding to a wind speed of 37.5 m/s (strong wind conditions) was generated to analyze the wind-induced response. Furthermore, based on the design response spectrum for site type II, three ground motion records were selected from the PEER ground motion database. The seismic responses under frequent earthquake, design earthquake, and rare earthquake conditions were investigated, and measures to enhance the structural performance under rare earthquakes were proposed. The results indicate that the hybrid tower exhibits excellent performance under strong wind conditions, frequent earthquakes, and design earthquakes. No concrete tensile stress was observed in the concrete tower section, and the stress levels in key components—such as the upper steel tower section and prestressed cables—as well as the maximum tower top displacement, remained within allowable design limits. However, under rare earthquake action, concrete tensile stress developed in the concrete tower section, reaching a maximum value of 0.40 MPa. For the concrete tower section, the emergence of tensile stress indicates that the pre-compression stress at the concrete segment joint has been completely offset. This implies a potential opening of the segmented joints throughout the tower, thereby increasing the risk of collapse. Increasing the pre-tension force in the prestressed cables by 21.9% eliminated the tensile stress in the concrete tower section under rare earthquake action, ensuring the section remained in a compressive state throughout. Notably, even with this adjustment, the stresses in the upper steel tower section and prestressed cables, as well as the tower top displacement, remained within safe limits under strong wind conditions. The findings of this study provide a valuable reference for the safety evaluation of hybrid towers.
Computer simulations are critical for assessing the resilience of civil infrastructure to natural hazards. To support efficient and comprehensive analyses that account for interdependencies among infrastructure systems, an integrated platform that spans the full resilience evaluation process, from damage to recovery, is essential. This paper presents such a platform by linking SimCenter’s R2D tool, together with embedded infrastructure system operation simulators, to the recovery simulator pyrecodes. Compared to state-of-the-art post-disaster recovery simulation tools, the proposed platform offers three key advantages: first, it employs high-fidelity traffic flow and potable water delivery simulators to improve the accuracy of resource allocation and the representation of system interdependencies in recovery simulations; second, it streamlines continuous system performance evaluation for interdependent systems considering the change in both resource supply and demand, thereby facilitating quantitative assessment of system resilience; and third, it provides an integrated open-source framework that supports reconfiguration and extension, enabling the continuous incorporation of newer and more advanced regional-scale simulators and allowing flexible levels of simulation fidelity across infrastructure systems. Thus, the proposed simulation platform establishes a unique and foundational framework for future integrated regional resilience modeling and assessment. The integration is achieved through two application programming interfaces (APIs): one connecting regional damage and recovery simulators, and the other linking the recovery simulator with infrastructure-specific service generation and dispatch (operation) simulators. The resulting open-source platform provides a user-friendly interface for hazard, exposure, vulnerability, and recovery analysis, as well as resilience quantification. The platform’s utility is demonstrated in a case study of a California community subjected to an Mw 7.0 earthquake, where damage and recovery of buildings, bridges, tunnels, roadways, and water distribution pipelines are simulated. The case study illustrates the ability of the tools to quantify resilience comprehensively with high spatial and temporal resolution. Although only one case study and two system operation simulators are presented, the APIs are designed to integrate a broad range of hazard, exposure, damage, recovery, and operation simulators (e.g., PyPSA and pandapower for power system modeling), beyond those featured here.
The greatest subduction earthquake in Mexico in almost 100 years (Mw=8.2) struck on September 7, 2017 with epicenter located at the Gulf of Tehuantepec, 133 km southwest of Pijijiapan, Chiapas. The Tehuantepec earthquake caused severe damage to some cities and towns of the Mexican states of Chiapas and Oaxaca. In order to assess the recovery process in the infrastructure of the states of Chiapas and Oaxaca, the research team that originally conducted the post-earthquake damage reconnaissance in cities and towns of those states just a few days after the September 7, 2017 earthquake, revisited most of those locations during a six-day effort in February 2025, more than seven years after. For each state, the amounts of money invested by the National Reconstruction Program are reported, as well as their distribution in four main sectors: dwellings, education, health, and culture. Photographic evidence of the recovery process in two specific cities is also presented: Juchitán, Oaxaca and Tonalá, Chiapas. Additionally, statistics on the distribution of economic resources from the National Reconstruction Program in both cities are shown, with a particular focus on the dwellings sector, as it was the most severely affected by the earthquake. Finally, the authors reflect on the efficiency of the recovery strategies implemented in the promotion of resilient communities in the face of future intense earthquakes.
Rapid restoration of communication infrastructure is essential for effective coordination, emergency response, and resource allocation in post-disaster scenarios. As conventional communication systems are often damaged or rendered inaccessible during disasters, Unmanned Aerial Vehicles (UAVs) have emerged as a promising alternative for establishing temporary, ad-hoc communication networks. However, despite growing academic and practical interest, UAV-based communication recovery remains in its early stages, facing numerous technical and operational challenges that hinder widespread and effective adoption in complex disaster environments. In response, this study conducts a systematic literature review following the PRISMA protocol to synthesize the current state of knowledge on UAVs applications for Aerial Base Stations (ABSs) deployed as temporary replacements for disaster-affected terrestrial base stations. The review identified key concepts and system architectures, hardware configurations, deployment strategies, emerging AI-driven autonomy, and energy optimization approaches. The findings reveal notable technological progress in energy harvesting (e.g., solar-assisted and tethered platforms) and AI-driven energy conservation strategies; however, persistent limitations, particularly sustained endurance and unresolved coverage–capacity trade-offs, continue to hinder real-world implementation due to the lack of robust real-world validation. By consolidating these insights, the study provides a comprehensive assessment of current advancements and identifies strategic priorities for future research and policy development. This plays a crucial role in supporting the design and deployment of resilient, adaptive, and efficient UAV-based ABSs systems in disaster management contexts.
This study presents a hybrid methodology for predicting building collapses within the Intelligent Circular Resilience (ICR) framework. This uses a supervised Machine Learning (ML) approach, earthquake damage reports, and the Simplified Resilience Index (SRI), derived from existing earthquake damage models (EDM)—based on fragility and vulnerability functions—used in the probabilistic seismic risk assessment (PSRA). A curated building damage database comprising 89 structures (71 collapsed and 18 non-collapsed) from ten countries affected by major earthquakes (Mw 6.1–8.1, epicentral distances of 3–125 km, and PGA values ranging from 0.14 g to 0.82 g) was developed, including attributes related to exposure: occupancy, main structural material, number of stories, construction year, and hazard: magnitude, epicentral distance, intensity measures (Peak-ground acceleration, PGA, and elastic spectral acceleration). The dataset includes events such as the 2017 Puebla–Morelos earthquake (Mw 7.1, Mexico), the 1999 Kocaeli earthquake (Mw 7.6, Turkey), and the 2011 Christchurch earthquake (Mw 6.1, New Zealand), among others. Likewise, dependent attributes such as time elapsed and SRI (under 120-, 180-, and 365-day recovery scenarios) were calculated using 2-EDMs. Eight Random Forest models were trained and tested for collapse and non-collapse classification using combinations of independent and dependent attributes. The results indicate that models incorporating exposure-related variables—such as structural material, number of stories, construction year, and occupancy—alongside the SRI significantly improve collapse classification performance, achieving recall and F1 scores above 95%. Notably, many collapsed buildings exhibited low intensities (PGA ≤ 0.25 g), emphasizing the influence of local site effects—particularly in Mexico City. The findings demonstrate that incorporating SRI enhances the reliability of collapse prediction and supports its use as an interpretable resilience proxy during early ICR stages. This hybrid methodology bridges empirical data, traditional PSRA models, and ML techniques, contributing to more accurate and scalable post-earthquake resilience assessments.
