Search Results (739)

Road networks are essential for access, resource distribution, and population evacuation during natural events. These challenges are pronounced in rural areas, where network redundancy is limited and communities may have social disparities. While traditional risk management systems often focus on the physical consequences of hazard events alone, specialized literature increasingly suggests the development of a more comprehensive approach for risk assessment, where not only physical aspects associated with infrastructure, such as damage level or disruptions, but also the social and economic attributes of the affected population are considered. Consequently, this paper proposes a Vulnerability Access Index (VAI) to support road network decision-making that integrates the social vulnerability of rural communities exposed to natural events, their accessibility to nearby critical infrastructure, and physical risk. The research methodology considers (i) the Social Vulnerability Index (SVI) calculation based on socioeconomic variables, (ii) Importance Index estimation (I_imp) to evaluate access to critical infrastructure, (iii) VAI calculation combining SVI and I_imp, and (iv) application to a case study in the influence area of the Villarrica volcano in southern Chile. The results show that when incorporating social variables and accessibility, infrastructure criticality varies significantly compared to the infrastructure criticality assessment based solely on physical risk, modifying the decision-making regarding road infrastructure robustness and resilience improvements. Full article

Due to increased precipitation intensity and sea-level rise, low-lying coastal roads are increasingly vulnerable to subbase saturation. Widely applied lumped hydrological approaches cannot accurately represent time and space-varying groundwater levels in some highly conductive coastal soils, calling for more sophisticated tools. This study assesses the suitability of the Gridded Surface Subsurface Hydrologic Analysis model (GSSHA) for representing hydrological processes and groundwater dynamics in a unique coastal roadway setting in Alabama. A high-resolution model was developed to assess a 2 km road segment and was calibrated for hydraulic conductivity and aquifer bottom levels using observed groundwater level (GWL) data. The model configuration included a fixed groundwater tidal boundary representing Mobile Bay, a refined land cover classification, and an extreme precipitation event simulation representing Hurricane Sally. Results indicated good agreement between modeled and observed groundwater levels, particularly during short-duration high-intensity events, with NSE values reaching up to 0.83. However, the absence of dynamic tidal forcing limited its ability to replicate certain fine-scale groundwater fluctuations. During the Hurricane Sally simulation, over two-thirds of the segment remained saturated for over 6 h, and some locations exceeded 48 h of pavement saturation. The findings underscore the importance of incorporating shallow groundwater processes in hydrologic modeling for coastal roads. This replicable modeling framework may assist DOTs in identifying critical roadway segments to improve drainage infrastructure in order to increase resiliency. Full article

As organizations continue to embrace digital transformation, the need for robust cybersecurity strategies has never been more critical. This paper explores the Zero Trust Architecture (ZTA) as a contemporary cybersecurity framework that addresses the challenges posed by increasingly interconnected systems. Zero Trust (ZT) operates under the principle of “never trust, always verify,” ensuring that every access request is thoroughly authenticated, regardless of the requester’s location within or outside the network. However, implementing ZT is a challenging task, requiring an adequate roadmap to prioritize the different initiatives in agreement with company culture, exposure and cyber posture. We apply multi-criteria decision analysis (MCDA) to evaluate the relative importance of various components within a ZT framework, using the Incomplete Analytic Hierarchy Process (IAHP). Expert opinions from professionals in cybersecurity and IT governance were gathered through structured questionnaires, leading to a prioritized ranking of the eight key ZT pillars, as defined by the Cybersecurity and Infrastructure Security Agency (CISA), Washington, DC, USA, along with a prioritization of the sub-elements within each pillar. The study provides actionable insights into the implementation of ZTA, helping organizations prioritize security efforts to mitigate risks effectively and build a resilient digital infrastructure. The evaluation results were used to create a prioritized framework, integrated into the ZEUS platform, developed with Teleconsys S.p.A., to enable detailed assessments of a firm’s cyber partner regarding ZT and identify improvement areas. The paper concludes by offering recommendations for future research and practical guidance for organizations transitioning to a ZT model. Full article

The compound effects of extreme climate change and intensive urban development have led to more frequent urban inundation, highlighting the urgent need for the fine-scale evaluation of stormwater drainage system performance in high-density urban built-up areas. A typical basin, located in Shenzhen, was selected, and a pipe–river coupled SWMM was developed and calibrated via a genetic algorithm to simulate the storm drainage system. Design storm scenario analyses revealed that regional inundation occurred in the central area of the basin and the enclosed culvert sections of the midstream river, even under a 0.5-year recurrence period, while the downstream open river channels maintained a substantial drainage capacity under a 200-year rainfall event. To systematically identify bottleneck zones, two novel metrics, namely, the node cumulative inundation volume and the conduit cumulative inundation length, were proposed to quantify the local inundation severity and spatial interactions across the drainage network. Two critical bottleneck zones were selected, and strategic improvement via the cross-sectional expansion of pipes and river culverts significantly enhanced the drainage efficiency. This study provides a practical case study and transferable technical framework for integrating hydraulic modeling, spatial analytics, and targeted infrastructure upgrades to enhance the resilience of drainage systems in high-density urban environments, offering an actionable framework for sustainable urban stormwater drainage system management. Full article

This study addresses critical safety challenges in sustainable urban mobility by developing a probabilistic framework for lane-change risk prediction on congested expressways. Utilizing unmanned aerial vehicle (UAV)-captured trajectory data from 784 validated lane-change events, we construct a Bayesian network model integrated with an I-CH scoring-enhanced MMHC algorithm. This approach quantifies risk probabilities while accounting for driver decision dynamics and input data uncertainties—key gaps in conventional methods like time-to-collision metrics. Validation via the Asia network paradigm demonstrates 80.5% reliability in forecasting high-risk maneuvers. Crucially, we identify two sustainability-oriented operational thresholds: (1) optimal lane-change success occurs when trailing-vehicle speeds in target lanes are maintained at 1.0–3.0 m/s (following-gap < 4.0 m) or 3.0–6.0 m/s (gap ≥ 4.0 m), and (2) insertion-angle change rates exceeding 3.0°/unit-time significantly elevate transition probability. These evidence-based parameters enable traffic management systems to proactively mitigate collision risks by 13.26% while optimizing flow continuity. By converting behavioral insights into adaptive control strategies, this research advances resilient transportation infrastructure and low-carbon mobility through congestion reduction. Full article

The siphon effect between airports has amplified the polarization in passenger throughput, undermining the balanced development and sustainability of airport clusters. The airport siphon effect occurs when one airport attracts a disproportionate share of passengers, concentrating traffic at the expense of others, which affects the overall resilience of the entire airport cluster. To address this issue, this study proposes a siphon index, expands the range of ground transportation options for passengers, and establishes a zero-siphon model to assess the impact of siphoning on the resiliency of airport clusters. Using this framework, four major airport clusters in China were selected as research subjects, with regional aviation accessibility serving as a measure of resilience. The results showed that among the four airport clusters, the siphon effect is most pronounced in the Guangzhou region. To explore the implications of this effect further, three airport disruption scenarios were simulated to assess the resilience of the Pearl River Delta airport cluster. The results indicated that the intensity and timing of disruptive events significantly affect airport cluster resilience, with hub airports being particularly sensitive. This study analyzes the risks associated with excessive route concentration, providing policymakers with critical insights to enhance the sustainability, equity, and resilience of airport clusters. The proposed strategies facilitate coordinated infrastructure development, optimized air–ground intermodal connectivity, and risk mitigation. These measures contribute to building more sustainable and adaptive aviation networks in rapidly urbanizing regions. Full article

Effective water infrastructure planning and management is key to sustainable water supply globally. This research assesses water infrastructure planning and management in Gauteng, South Africa, amid growing challenges from rapid urbanisation, high water demand, climate change, and resource scarcity. These challenges threaten the achievement of Sustainable Development Goals 6 and 11; hence, an integrated approach is required for water sustainability. The study responds to a gap in the literature, which often treats planning and management separately, by adopting an integrated, multi-institutional approach across the water value chain. A mixed-methods triangulation strategy was employed for data collection whereby surveys provided quantitative data, while two sets of structured interviews were conducted: the first round to determine causal relationships among the critical success factors and the second round to validate the proposed framework. The findings reveal a misalignment between infrastructure planning and implementation, contributing to infrastructure backlogs and a short- to medium-term focus. Infrastructure management is further constrained by inadequate system redundancy, leading to ineffective maintenance. External factors such as delayed adoption of 4IR technologies, lack of climate resilient strategies, and fragmented institutional coordination exacerbate these issues. Using Decision-Making Trial and Evaluation Laboratory (DEMATEL) analysis, the study identified Strategic Alignment and a Value-Driven Approach as the most influential critical success factors in water asset management. The research concludes by proposing an integrated water infrastructure and planning framework that supports sustainable water supply. Full article

In rapidly urbanizing environments, environmental stressors—such as air pollution, noise, heat, and green space depletion—substantially exacerbate public health burdens, contributing to the global rise of non-communicable diseases, particularly hypertension, cardiovascular disorders, and mental health conditions. Despite expanding research on green spaces and health (+76.9%, 2019–2025) and optimization and algorithmic approaches (+63.7%), the compounded and synergistic impacts of these stressors remain inadequately explored or addressed within current urban planning frameworks. This study presents a Mixed Methods Systematic Review (MMSR) to investigate the potential of AI-driven urban design optimizations in mitigating these multi-scalar environmental health risks. Specifically, it explores the complex interactions between urbanization, traffic-related pollutants, green infrastructure, and architectural intelligence, identifying critical gaps in the integration of computational optimization with nature-based solutions (NBS). To empirically substantiate these theoretical insights, this study draws on longitudinal 24 h dynamic blood pressure (BP) monitoring (3–9 months), revealing that chronic exposure to environmental noise (mean 79.84 dB) increases cardiovascular risk by approximately 1.8-fold. BP data (average 132/76 mmHg), along with observed hypertensive spikes (systolic > 172 mmHg, diastolic ≤ 101 mmHg), underscore the inadequacy of current urban design strategies in mitigating health risks. Based on these findings, this paper advocates for the integration of AI-driven approaches to optimize urban environments, offering actionable recommendations for developing adaptive, human-centric, and health-responsive urban planning frameworks that enhance resilience and public health in the face of accelerating urbanization. Full article

Transmission towers are particularly vulnerable to extreme wind events, which can lead to structural damage or collapse, thereby compromising the stability of power transmission systems. Enhancing the wind-resistant capacity of these towers is therefore critical for improving the reliability and resilience of electrical infrastructure. This study utilizes finite element analysis (FEA) to evaluate the structural response of a 220 kV transmission tower subjected to fluctuating wind loads, effectively capturing the dynamic characteristics of wind-induced forces. A comprehensive dynamic analysis is conducted to account for uncertainties in wind loading and variations in wind direction. Through this approach, this study identifies the most critical wind angle and local structural weaknesses, as well as determines the threshold wind speed that precipitates structural collapse. To improve structural resilience, a concurrent multi-scale modeling strategy is adopted. This allows for localized analysis of vulnerable components while maintaining a holistic understanding of the tower’s global behavior. To mitigate failure risks, the traditional perforated plate reinforcement technique is implemented. The reinforcement’s effectiveness is evaluated based on its impact on load-bearing capacity, displacement control, and stress redistribution. Results reveal that the critical wind direction is 45°, with failure predominantly initiating from instability in the third section of the tower leg. Post-reinforcement analysis demonstrates a marked improvement in structural performance, evidenced by a significant reduction in top displacement and stress intensity in the critical leg section. Overall, these findings contribute to a deeper understanding of the wind-induced fragility of transmission towers and offer practical reinforcement strategies that can be applied to enhance their structural integrity under extreme wind conditions. Full article

The widespread adoption of the Internet of Things (IoT) has driven major advancements in wireless communication, especially in rural and suburban areas where low population density and limited infrastructure pose significant challenges. Accurate Path Loss (PL) prediction is critical for the effective deployment and operation of Wireless Sensor Networks (WSNs) in such environments. This study explores the use of Convolutional Neural Networks (CNNs) for PL modeling, utilizing a comprehensive dataset collected in a smart campus setting that captures the influence of terrain and environmental variations. Several CNN architectures were evaluated based on different combinations of input features—such as distance, elevation, clutter height, and altitude—to assess their predictive accuracy. The findings reveal that CNN-based models outperform traditional propagation models (Free Space Path Loss (FSPL), Okumura–Hata, COST 231, Log-Distance), achieving lower error rates and more precise PL estimations. The best performing CNN configuration, using only distance and elevation, highlights the value of terrain-aware modeling. These results underscore the potential of deep learning techniques to enhance IoT connectivity in sparsely connected regions and support the development of more resilient communication infrastructures. Full article

Urban blue-green infrastructure (UBGI) is a comprehensive solution that balances environmental, social, and economic development objectives and has emerged as a critical approach for fostering urban resilience and sustainable development. This paper conducts a systematic bibliometric analysis of 975 academic articles published between 2000 and 2023 in the Web of Science Core Collection, focusing specifically on the ecosystem services associated with UBGI. Employing CiteSpace visualization technology, this study elucidates the major research trends, thematic clusters, and international collaboration patterns shaping this field. The research delves into the diverse range of ecosystem services provided by blue-green infrastructure and analyzes their contributions to urban well-being. Findings indicate that regulatory services—particularly climate regulation, biodiversity enhancement, and water resource management—have become central research foci within the contexts of urban green infrastructure (UGI), urban blue infrastructure (UBI), and UBGI. Co-citation and keyword analyses reveal that nature-based solutions, hybrid green–gray infrastructure, and the application of urban resilience frameworks are gaining increasing scholarly attention. By summarizing the evolutionary trajectory and priority directions of UBGI research, this study provides significant insights for future interdisciplinary research aimed at enhancing the supply of urban environmental ecosystem services. Full article

Central Asia (CA) faces growing Water–Energy–Food (WEF) Nexus challenges, due to its complex transboundary water management, legacy Soviet-era water infrastructure, and increasing climate and socio-economic pressures. This study presents the development of a System Dynamics Model (SDM) to evaluate WEF interdependencies across the Aral Sea Basin (ASB), including the Amu Darya and Syr Darya river basins and their sub-basins. Different downscaling strategies based on the area, population, or land use have been applied to process open-access databases at the national level in order to match the scope of the study. Climate and socio-economic assumptions were introduced through the integration of already defined Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). The resulting SDM incorporates more than 500 variables interacting through mathematical relationships to generate comprehensive outputs to understand the WEF Nexus concerns. The SDM was successfully calibrated and validated across three key dimensions of the WEF Nexus: final water discharge to the Aral Sea (Mean Absolute Error, MAE, <5%), energy balance (MAE = 4.6%), and agricultural water demand (basin-wide MAE = 1.2%). The results underscore the human-driven variability of inflows to the Aral Sea and highlight the critical importance of transboundary coordination to enhance future resilience. Full article

The COVID-19 pandemic has highlighted critical vulnerabilities in urban logistics systems, particularly in last-mile delivery. To enhance logistics resilience and efficiency, the Korean government has initiated an innovative project that repurposes idle spaces in subway vehicle bases within the Seoul Metropolitan Area into logistics centers. This study proposes a comprehensive multi-criteria evaluation framework combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess the suitability of ten candidate sites. The evaluation criteria span four dimensions, facility, geographical, environmental, and social factors, derived from the literature and expert consultations. AHP results indicate that geographical factors, especially proximity to urban centers and major logistics facilities, hold the highest weight. Based on the integrated analysis using TOPSIS, the most suitable locations identified are Sinnae, Godeok, and Cheonwang. The findings suggest the strategic importance of aligning infrastructure development with spatial accessibility and stakeholder cooperation. Policy implications include the need for targeted investment, public–private collaboration, and sustainable logistics planning. Future research is encouraged to incorporate dynamic data and consider social equity and environmental impact for long-term urban logistics planning. Full article

This study evaluates the feasibility and visibility of electric vehicles (EVs) in Egypt, addressing critical research gaps and proposing actionable strategies to drive adoption. Employing a systematic review of academic, governmental, and industry sources, the paper identifies underexplored areas such as rural–urban adoption disparities, lifecycle assessments of EV batteries, and sociocultural barriers, including gender dynamics and entrenched consumer preferences. Its primary contribution is an interdisciplinary framework that integrates technical aspects, such as grid resilience and climate-related battery degradation, with socioeconomic dimensions, providing a holistic overview of EV feasibility in Egypt tailored to Egypt’s context. Key findings reveal infrastructure limitations, inconsistent policy frameworks, and behavioral skepticism as major hurdles, and highlight the untapped potential of renewable energy integration, particularly through synergies between solar PV generation (e.g., Benban Solar Park) and EV charging infrastructure. Recommendations prioritize policy reforms (e.g., tax incentives, streamlined tariffs), solar-powered charging infrastructure expansion, public awareness campaigns, and local EV manufacturing to stimulate economic growth. The study underscores the urgency of stakeholder collaboration to transform EVs into a mainstream solution, positioning Egypt as a regional leader in sustainable mobility and equitable development. Full article

Lightning represents a critical danger to facilities such as oil tank farms, with the potential to cause major explosive incidents. To address this risk, Indonesia’s oil and gas industry has adopted the NFPA 780 Standard for lightning protection systems. However, tank explosions and refinery disruptions caused by lightning strikes continue to occur annually, highlighting the need to reassess the standard’s self-protection criteria, particularly in Indonesia’s tropical climate. The NFPA 780 standard was primarily developed based on lightning characteristics in subtropical regions. This study evaluates its effectiveness in tropical environments, where lightning parameters such as peak currents, frequencies, and ground flash densities differ significantly. By analyzing specific incidents of tank explosions in Indonesia, the research reveals that compliance with the NFPA 780 standard alone may not be adequate to protect critical infrastructure. To address these challenges, this study proposes a novel approach to lightning protection by designing solutions tailored to the unique characteristics of tropical climates. By incorporating local lightning parameters, the proposed measures aim to enhance safety and resilience in oil and gas facilities. This research provides a framework for adapting international standards to regional needs, improving the effectiveness of lightning protection in tropical environments. Full article