Resilient Infrastructure Research Articles

National critical infrastructure protection via supply chain security in the United States of America

The protection of critical infrastructure in the United States is vital for ensuring national security, economic stability, and public safety. As sectors such as energy, transportation, water, and communications become increasingly reliant on globalized supply chains, vulnerabilities to cyberattacks, natural disasters, and geopolitical tensions have heightened. This study examines the role of secure supply chain practices, including vendor screening, technology integration, and adherence to regulatory frameworks, in mitigating risks to critical infrastructure. Through content and thematic analysis of secondary data, this research identifies key security measures, such as blockchain, artificial intelligence (AI), and real-time monitoring, which enhance the resilience of supply chains. The study highlights the importance of cybersecurity, intermodal coordination, and geopolitical risk management in safeguarding infrastructure. Case studies, particularly the 2021 Colonial Pipeline ransomware attack, emphasize the need for proactive security measures, such as continuous monitoring, regular cybersecurity assessments, and improved stakeholder collaboration. The study provides strategic recommendations for strengthening supply chain security, including prioritizing vendor screening, adopting advanced technologies, enhancing intermodal coordination, and diversifying supply networks to mitigate geopolitical risks. These measures are essential for reinforcing the resilience, efficiency, and continuity of critical infrastructure in the face of evolving threats.

Green Infrastructure and Urban Flooding: A Survey of Prevailing Issues and Current Modelling Approaches

Abstract Green Infrastructure (GI) is gaining attention in urban flood management for its ability to reduce peak flow and volume while providing multiple urban and environmental benefits. Climate change and urbanization intensify GI development needs but also amplify design, management, and performance challenges for local governments and stakeholders. Understanding the complex challenges of implementing GI is essential for advancing sustainable urban water management in line with the Sustainable Development Goals (SDGs), particularly SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Previous studies have often focused on specific components of GI systems, but this research provides the first comprehensive work covering it from pre-design to post-installation—of GI implementation in metropolitan Melbourne. Through a targeted survey of local councils and water consultancies, our study bridges the gap between theoretical design and practical application. By incorporating practitioners’ real-world experiences alongside technical modelling challenges, we reveal how factors like limited real-time data and regional parameterization can affect GI performance. Our approach uniquely examines interlinked obstacles, from financial constraints to maintenance protocols, giving decision-makers valuable insights to enhance GI project outcomes. This research addresses critical knowledge gaps, such as the need for community engagement and tailored technical expertise, providing actionable solutions for improving GI planning, implementation, and sustainability. Our findings offer a practical framework for future GI initiatives, helping practitioners pre-emptively tackle challenges, optimize resource use, and establish a resilient urban infrastructure that supports clean water access, sustainable urban growth, and climate adaptation.

Blame Attribution and Compliance with COVID-19 Measures in Australia: The Theory of Planned Behaviour

This study scrutinizes the influence of “blame attribution” and the Theory of Planned Behaviour (TPB) on compliance with COVID-19 public health measures in Australia. This study elucidates that blaming individuals rather than governments surprisingly augments support for governmental regulations, highlighting the complexities of blame attribution in shaping public adherence to health policies. It underscores the nuanced roles of TPB elements like subjective norms and behavioural control, revealing that feelings of empowerment, social responsibility, and recognizing personal roles in pandemic control enhance the inclination to support governmental directives. The outcomes emphasize the criticality of understanding blame attribution and TPB dynamics for devising efficacious communication and management strategies, promoting societal adherence to essential regulations and actions during health crises, and fostering a more resilient societal infrastructure for dealing with pandemics.

Unified monitoring and telemetry platform supporting network intelligence in optical networks

In recent years, machine-learning (ML) applications have generated considerable interest and shown great potential in optimizing optical network management, such as quality of transmission estimation, traffic prediction, and resource allocation. However, these applications often require large datasets for training, inference, and updating, while network operators are generally reluctant to disclose their data due to privacy concerns and the sensitivity of operational information. Most open-source datasets typically lack transparency regarding network specifics, such as topology details and device configurations, making data acquisition and ML model training more difficult. In response, this paper presents a unified monitoring and telemetry platform that leverages distributed and centralized time-series databases on InfluxDB, a Kafka-based telemetry pipeline, and advanced ML applications. The separation of distributed and centralized databases improves data management flexibility and scalability. The Kafka-based telemetry pipeline ensures high-throughput, low-latency data streaming with end-to-end latency under 0.05 s through optimized partitioning. Additionally, integrating Kafka and InfluxDB allows for real-time data visualization from multiple sources, improving transparency and supporting real-time data streaming for network applications. By implementing this advanced telemetry and ML architecture, network operators can build a more intelligent, responsive, and resilient optical network infrastructure.

The humanitarian and public health crisis in Gaza: Impact, challenges, and international responses

Background: The humanitarian crisis in Gaza has escalated to unprecedented levels as of late September 2024, with devastating impacts on civilians and the healthcare infrastructure. Years of the blockade, exacerbated by ongoing military aggression, have led to the collapse of Gaza's health system anddeepened the region's public health crisis. Objective: The aim of this study was to examine the far-reaching public health consequences of the Gaza conflict, with a focus on the destruction of healthcare infrastructure, the rise in communicable diseases, and the psychological trauma affecting the population. Methods: A qualitative analysis was conducted using secondary data from peer-reviewed journal articles, reports from international humanitarian organizations, and official statistics. Thematic analysis identified key challenges, including the impact of infrastructural collapse, barriers to medical care, and the consequences of prolonged conflict on mental and physical health. Results: The findings indicate over 41,615 deaths, including approximately 16,500 children,and thousands are still missing. The destruction of hospitals, roads, and ambulances has severely disrupted medical services andleft tens of thousands without medical care. Chronic lack of medical supplies, clean water, and electricity further aggravates the crisis. Additionally, communicable diseases and psychological trauma have reached critical levels, disproportionately affecting children and vulnerable populations. Conclusions: The findings highlight the urgent need for a coordinated international response to address the healthcare crisis in Gaza. Emergency measures should include curbing military aggression, providing humanitarian aid, and rebuilding healthcare systems. Long-term efforts must focus on building resilient health infrastructure, ensuring access to essential medical care, and addressing the mental health needs of the population. Future research should explore the long-term public health outcomes of conflict and strategies to enhance healthcare resilience in similar settings.

Advancing bridge resilience: a review of monitoring technologies for flood-prone infrastructure

Floods pose a critical threat to bridge infrastructure, which plays an essential role in transportation networks and economic resilience. This review examines state-of-the-art Structural Health Monitoring (SHM) technologies tailored to mitigate flood risks, focusing on their real-world applications in flood-prone bridges. A central feature of this review is the extensive use of case studies, illustrating diverse SHM methods applied globally to monitor challenges such as debris accumulation, hydrodynamic forces, and scour—primary causes of bridge failures. These examples provide detailed insights into technologies like sonar-based devices, scour probes, photographic monitoring, rotation- and vibration-based techniques. By showcasing specific case studies—such as bridges monitored using smart magnetic rocks, Interferometric Synthetic Aperture Radar (InSAR), and fibre optic sensors—the review highlights practical outcomes, demonstrating how SHM systems enhance resilience through early detection and predictive maintenance. It also explores the challenges of implementing these systems, including environmental sensitivity, cost, and data complexity, while identifying gaps in integrating hydraulic and structural data for holistic risk assessments. This review advocates for multidisciplinary collaboration and advanced data-driven solutions, such as AI-based predictive maintenance, to address climate change impacts and increasing flood risks. By bridging cutting-edge research with real-world applications, this article provides actionable insights into scalable, adaptive SHM solutions, inspiring engineers and researchers to develop more resilient infrastructure for a changing world.

Unveiling the seismic sensitivity of the Himalayan tunnels: a comprehensive assessment through analytical and numerical exploration of P-wave dynamics

This study investigated the seismic sensitivity of tunnels in the Jammu Region (JR) of the northwestern Himalayas, a region characterized by significant seismic activity and complex geological conditions. The research combined both analytical and numerical approaches to assess the influence of site conditions, tunnel lining, and reinforcement properties on tunnel resilience. A key objective is to develop a more reliable seismic assessment method by adopting a P-wave-based approach, which is particularly suitable for mountainous tunnels prone to landslides. The study identified three seismic hazard zones, with peak ground accelerations (PGA) ranging from less than 0.3 g to greater than 0.5 g, providing vulnerability aspects. The major outcomes of this study include guidelines for the design and retrofitting of sustainable and resilient underground structures in the Himalayas, with broader implications for global projects in seismically active and geologically complex regions. The methodologies and insights can be applied to infrastructure projects worldwide, enhancing the safety of communities living in vulnerable areas. This work aligns with the United Nations Sustainable Development Goals (SDGs), particularly in promoting resilient infrastructure and sustainable development, contributing to both structural resilience and the geological safety of the Himalayan region.

Enhancing seismic risk assessment through probabilistic analysis of liquefaction potential index: a computational intelligence approach

ABSTRACT This study proposes a novel computational intelligence approach for enhancing seismic risk assessment through probabilistic analysis of the liquefaction potential index (LPI). The methodology leverages a hybridised model that combines backpropagation neural networks (BcNN) with an improved firefly algorithm (IFF) to predict LPI values accurately and efficiently. The BcNN-IFF model was trained and validated using a comprehensive dataset comprising the geotechnical and seismic parameters from various earthquake-prone regions worldwide. Feature importance analysis revealed that total stress (σv’) and peak ground acceleration (amax) were the most influential factors in the LPI prediction. The BcNN-IFF model demonstrated superior performance compared to other hybrid models, achieving 91% accuracy in the training phase and 85% accuracy in the testing phase, with low root mean square error and mean absolute error values. The practical implications of the BcNN-IFF model highlights its potential for reliable liquefaction susceptibility assessments, particularly in resource-constrained environments. Future research directions are outlined, emphasising the need for uncertainty quantification, data augmentation, and computational efficiency enhancements to improve the applicability of the model in real-world scenarios. This study advances state-of-the-art computational modelling and provides a foundation for integrating such tools into geotechnical engineering practices for effective seismic risk assessment and infrastructure resilience.

Analysis of The Application of Value Engineering In The Construction of Soil Retaining Walls on The Bendung-Bantengan Road Section In Mojokerto District, East Java

Mojokerto, as a key economic center in East Java, faces increasing demands for reliable and efficient road infrastructure to support its economic growth and regional connectivity. The lack of adequate retaining walls along the Bendung-Bantengan road section has resulted in significant road damages, including cracks and subsidence, which pose risks to road users and increase maintenance costs. This study investigates the application of Value Engineering (VE) as a strategic method to enhance the efficiency and cost-effectiveness of retaining wall construction in the area. Using secondary data from topographic surveys and triaxial soil tests, the study evaluates various design alternatives to identify cost savings, stability improvements, and impacts on project duration. The results demonstrate that adopting VE (Alternative 1) reduces construction costs by IDR 284,244,698 through optimized structural dimensions, while maintaining quality and stability standards. Additionally, the project completion period is shortened to 121 days, offering a faster turnaround compared to the initially planned six-month schedule. The findings emphasize VE's potential to optimize material use, minimize waste, and achieve environmental sustainability by reducing carbon emissions during construction. Moreover, VE enables enhanced collaboration among stakeholders, including consultants and contractors, to develop effective and efficient solutions for infrastructure challenges. This research underscores the importance of integrating VE during the planning stages of infrastructure projects, particularly in areas with high erosion risks. By applying VE principles, the Bendung-Bantengan retaining wall project not only meets safety and quality requirements but also contributes to long-term infrastructure resilience and economic efficiency in Mojokerto.

Optimization of Mechanical Performance of Full-Scale Precast Concrete Pipes with Varying Concrete Strengths and Reinforcement Using Factorial Design

The use of precast concrete pipes for water and sewage transportation systems is a very important element of a country’s infrastructure. The main aim of this study was to investigate the effects of concrete’s compressive strength and reinforcement levels on the mechanical performance of spun-cast full-scale precast concrete pipes in the local construction industries of developing countries. A test matrix was adopted using a full 32 factorial design. The studied concrete’s compressive strength was 20, 30, and 40 MPa, and reinforcement levels were 60%, 80%, and 100%, representing low, medium, and high levels, respectively. The medium level of reinforcement represented the reinforcement requirement of ASTM C76 in concrete pipes. A total of eighteen full-scale pipes of 450 mm diameter were cast in an industrial precast pipe unit using a spin-casting technique and were tested under a three-edge bearing load. The experimental results showed that the crack load and ultimate load of the tested pipes increased with higher levels of concrete strength and reinforcement levels. For example, an approximately 35% increase in the 0.30 mm crack load was observed when the concrete strength increased from 20 MPa to 30 MPa for all tested levels of reinforcement. Similarly, around a 19% increase in ultimate load was observed for pipes with 80% reinforcement compared to identical pipes with 60% reinforcement. It was found that the pipe class, as per ASTM C76, is highly dependent on the concrete strength and reinforcement levels. All of the pipes exhibited the development of flexural cracks at critical locations (crown, invert, and springlines). Moreover, concrete pipes cast with low-level strength and reinforcement also showed signs of crushing at the crown location near to the pipe failure. The analysis of variance (ANOVA) results showed that the main factors (compressive strength and reinforcement levels) were significantly affected by the cracking loads of precast pipes. No significant effect of the interaction of factors was observed on the crack load response. However, interaction factors, along with main factors, have significant effects on the ultimate load capacity of the concrete pipes, as indicated by the F-value, p-value, and Pareto charts. This study made an effort to illustrate and optimize the mechanical performance of pipes cast with various concrete strengths and reinforcement levels to facilitate the efficient use of materials for more resilient pipe infrastructure. Moreover, the exact optimization of concrete strength and reinforcement level for the desired pipe class will make the pipe design economical, leading to an increased profit margin for local spin-cast pipe fabricators without compromising the pipe’s quality.

Adoption of 4.0 technologies in MSMEs in the Caribbean: a multiple cases study

By employing the qualitative methodology of multiple cases study, the adoption of 4.0 technologies in MSMEs in Puerto Rico is explored, in terms of the drivers, benefits, problems, and challenges of the incorporation processes. The findings reflect that MSMEs belong mostly to the regulated sector and agree that the adoption of technologies allows them interoperability, data analysis, projections, efficiency and effectiveness in real time and close and frequent linkage with customers. Participants of the study highlight their dependence on power energy system as well as its instability, as the biggest obstacles, in addition to the lack of capital, among others. The resilient infrastructure component (energy as well as technology) evidences the need for future research on the subject from a geospatial approach, which imparts originality to this study and determines its importance in the development of public policies and resilient business models.

Criticality disaster risk assessment for urban rail transport in Indonesia

The increasing frequency of extreme weather events due to climate change poses significant risks to urban rail systems, especially in disaster-prone regions like Indonesia. Urban railways, with limited rerouting options, are highly vulnerable to natural disasters such as floods, landslides, and earthquakes. These disruptions can cause cascading economic impacts, including infrastructure damage, productivity loss, and prolonged travel times. This study develops a methodological framework to assess disaster risk and climate vulnerability in urban rail systems, with Bandung, Indonesia, as the case study. The framework integrates Climate Vulnerability and Risk Assessment (CVRA) with the Movement and Place (M&P) framework, leveraging Geographic Information System (GIS) technology to identify and prioritize high-risk areas. CVRA evaluates hazards, exposure, and vulnerabilities, while M&P assesses the functional significance of transport nodes based on movement patterns and land use density. The combined analysis produces a risk matrix, enabling targeted mitigation strategies that integrate engineering solutions, urban planning, and policy interventions. Findings highlight critical vulnerabilities in Bandung’s proposed LRT system, particularly at stations susceptible to earthquakes and landslides. Recommended mitigation measures include resilient infrastructure designs, strategic planning for high-risk zones, and stakeholder engagement for prioritization. This framework offers practical guidance for policymakers to enhance urban rail resilience, reduce climate-related risks, and ensure sustainable urban mobility. It serves as a scalable model for other cities in Indonesia and globally, supporting adaptive and sustainable transport systems.

Interdependency classification: A framework for infrastructure resilience

Abstract Critical Infrastructure Systems (CISs) provide essential services for nation-wide security, economy, and social well-being, and these systems are becoming increasingly interdependent to maintain those services. Recent and diverse disturbances worldwide have highlighted that interdependencies within CISs may increase the potential for cascading failures, amplifying the impacts of both large and small-scale disturbances into events of catastrophic proportions. The methodology involved conducting a literature search through scholarly databases and using a snowball approach to identify 12 relevant papers on infrastructure interdependencies. Thematic analysis was then applied to classify 50 interdependencies, which were synthesized into a unified classification system mapped to a foundational framework. The direct citation network visualization tool, Kumu, illustrated citation patterns and guided the analysis. Identifying and classifying various interdependencies, understanding their relationships, and assessing their role in CISs are crucial steps to prevent, mitigate, or manage unfavorable consequences and enhance system resilience. In this article, we compiled and identified classifications of infrastructure interdependencies and described a unified classification. To support the resilience of CISs in periods of stability and instability, infrastructure managers not only need consistent terminology but a shared understanding of interdependencies to ensure said interdependencies within and across CISs are appropriately accounted for throughout the life cycle of infrastructure, from early planning stages to management – reactive or proactive – for disturbances. These are critical steps toward understanding how CISs operate in concert so infrastructure managers may sense and anticipate potential disruptions, manage the impacts and consequences, and adapt to changing future conditions.

Availability-based maintenance prioritization for data centres: a dynamic programming approach

Digital infrastructures and Data Centres (DCs) have played a significant role in society for several years in conducting digital transformation. Recently after the global COVID-19 crisis, the importance of DCs has greatly increased because of facing new realities. High demand for digital commerce, online meetings, and other online and cloud services in the digitisation process has given the DC industry special attention. Regarding the critical components of DCs, special requirements, such as minimum availability, reliability, quality, and performance levels, should be addressed in DC operations. Therefore, implementing optimised maintenance management is beneficial for these infrastructures to reduce the risk of failure and ensure a minimum level of reliability and availability while minimising maintenance costs. This paper presents a novel, availability-based model for optimising maintenance prioritisation in DCs. The model leverages a Multiple 0-1 Knapsack problem, solved using Dynamic Programming (DP), to determine the optimal set of components for maintenance actions, considering both DC availability requirements and budget limitations. By incorporating reliability and availability analysis, the DP-based approach generates a maintenance prioritisation plan that maximises DC availability within budgetary constraints. This study provides a practical framework for enhancing maintenance decision-making in DCs, ultimately contributing to more efficient and resilient digital infrastructures.

Experimental study on pressure relief blasting in rock-blast tunnel

Rock burst is a difficult and urgent problem during the construction process of underground engineering in a zone with high surrounding rock pressure. Pressure relief blasting is one of the most effective methods for solving the problem. An in-situ blasting test was carried out in a tunnel under excavation, and the geology stress (geo-stress) of the surrounding rock was tested before and after the pressure relief blasting. The experimental results will be effective for further study of the law of pressure relief blasting and rock-burst prediction. An explicit stress analytic function is also employed to analyze the stress distribution of the tunnel surrounding rock after pressure relief blasting based on the propagation of the blasting stress wave. Furthermore, the stress attenuation coefficient and the variation of the geo-stress distribution of surrounding rock under the pressure relief blasting were discussed in detail with the in-situ blasting test data. Highlights. The main contribution of this paper is focused on: 1. This study measures the in-situ geo-stress of surrounding rock in the ‘Sang zhu ling’ tunnel, both before and after the pressure relief blasting. This data is crucial for ensuring the safety resilience of urban infrastructure. 2. A two-dimensional explicit equation of stress attenuation was derived based on the characteristics of stress propagation, providing a more accurate analytical tool for engineers and researchers, 3. The traditionally obtained stress attenuation coefficient is shown to require modification through further in-situ experiments. Future studies will refine this coefficient, contributing to the development of more reliable methods for safeguarding urban lifelines. These contributions align with the journal’s focus on advancing urban science and engineering by promoting innovative, data-driven solutions for urban safety.

Hacking Exposed: Leveraging Google Dorks, Shodan, and Censys for Cyber Attacks and the Defense Against Them

In recent years, cyberattacks have increased in sophistication, using a variety of tools to exploit vulnerabilities across the global digital landscapes. Among the most commonly used tools at an attacker’s disposal are Google dorks, Shodan, and Censys, which offer unprecedented access to exposed systems, devices, and sensitive data on the World Wide Web. While these tools can be leveraged by professional hackers, they have also empowered “Script Kiddies”, who are low-skill, inexperienced attackers who use readily available exploits and scanning tools without deep technical knowledge. Consequently, cyberattacks targeting critical infrastructure are growing at a rapid rate, driven by the ease with which these solutions can be operated with minimal expertise. This paper explores the potential for cyberattacks enabled by these tools, presenting use cases where these platforms have been used for both offensive and defensive purposes. By examining notable incidents and analyzing potential threats, we outline proactive measures to protect against these emerging risks. In this study, we delve into how these tools have been used offensively by attackers and how they serve defensive functions within cybersecurity. Additionally, we also introduce an automated all-in-one tool designed to consolidate the functionalities of Google dorks, Shodan, and Censys, offering a streamlined solution for vulnerability detection and analysis. Lastly, we propose proactive defense strategies to mitigate exploitation risks associated with such tools, aiming to enhance the resilience of critical digital infrastructure against evolving cyber threats.

A Hybrid Approach to Multi-Objective Task Scheduling in Cloud Computing: Merging Estimation of Distribution and Genetic Algorithms

In the evolving landscape of cloud computing, efficient task scheduling plays a pivotal role in optimizing resource utilization and enhancing performance. This paper presents a novel hybrid algorithm that combines the strengths of Estimation of Distribution Algorithms (EDA) and Genetic Algorithms (GA) to address multi-objective task scheduling challenges. The proposed approach aims to balance conflicting objectives such as minimizing execution time, energy consumption, and cost while maximizing resource utilization. By leveraging the probabilistic modeling capabilities of EDA and the evolutionary search efficiency of GA, the hybrid algorithm achieves a more diverse solution set and faster convergence compared to traditional methods. Extensive simulations demonstrate the effectiveness of the proposed algorithm across various cloud environments and task complexities. The results highlight significant improvements in achieving Pareto-optimal solutions, offering a robust framework for cloud service providers to enhance scheduling efficiency. This study underscores the potential of hybrid metaheuristic techniques in addressing the dynamic and complex nature of cloud computing task scheduling, paving the way for more resilient and adaptive cloud infrastructures. Keywords: Cloud Computing, Task Scheduling, Estimation of Distribution Algorithm, Genetic Algorithm, Multi-Objective Optimization, Load Balancing, Resource Utilization.

The Role of Zero Trust Architecture in Modern Cybersecurity: Integration with IAM and Emerging Technologies

As cyber threats continue to evolve, organizations are increasingly adopting advanced security frameworks such as Zero Trust Architecture (ZTA) to protect their digital assets. ZTA operates under the principle of "never trust, always verify," acknowledging that both internal and external networks may be compromised. Integrated with Identity and Access Management (IAM), ZTA offers a dynamic security model, requiring continuous authentication and authorization for every access request. This integration ensures that sensitive resources are protected by enforcing multifactor authentication, behavioral analysis, and micro-segmentation, which collectively reduce the risk of attacks. A critical aspect of ZTA is its focus on continuous authentication and the principle of least privilege. Unlike traditional IAM systems, which grant static access, ZTA dynamically adjusts access based on real-time conditions, such as user behavior, location, and the sensitivity of data. This reduces the attack surface and minimizes lateral movement within the network. Additionally, ZTA promotes network segmentation, using IAM frameworks to enforce strict access control for each network segment. This limits the potential damage if an attacker compromises one segment. Studies have shown that ZTA is highly effective in mitigating insider threats, reducing the attack surface, and enhancing the resilience of infrastructure. While challenges remain in terms of scalability and integration complexity, the security benefits are undeniable. The increasing use of AI, machine learning, and quantum computing is expected to further strengthen ZTA's security capabilities. As organizations face more sophisticated cyber threats, ZTA is likely to become a standard in cybersecurity strategies, providing a robust, adaptable solution to protect valuable digital assets.

Unconventional Coastal Structures (Kemit Armor Layer)

ABSTRACTThe growth of the marine sector induces substantial demand to develop new types of coastal structures that have minimal impact on neighboring ecosystems and reduce the environmental footprint. This study aims to introduce and check the efficiency of an innovative armor layer for coastal protection towards fostering sustainable coastal management practices. The proposed truncated cube (named Kemit) design incorporates features such as two hollow opposite elbows, half rings, and incomplete pyramidal shapes to enhance wave absorption and promote marine biodiversity. The proposed armor unit has been investigated physically in the wide wave flume of the Coastal Research Institute Egypt. The armor layer performance was introduced through wave reflection, wave dissipation coefficients, and wave overtopping. Six wave conditions (wave height and wave period) were performed to the armor layer in two states of submergence. A comparative analysis with traditional cube‐armored breakwaters under the same circumstances (wave height, wave period, and water depth) reveals superior performance of the proposed “Kemit” armor layer, exhibiting lower wave reflection coefficients (0.56 to 0.68) and lower overtopping, particularly evident in medium water depths. However, in case of lower water depth, the traditional cubes show better efficiency, lower reflection coefficient (0.46 to 0.68), and higher dissipation (0.74 to 0.89). Moreover, the proposed Kemit armor layer decreased the wave overtopping and run‐up over the breakwater cross‐section due to the Kemit steps and porosity. Furthermore, the adoption of innovative technologies can help policymakers increase the resilience of coastal infrastructure and ecosystems.

Improving Power Quality and Fault Ride-Through (FRT) in Grid-Connected Hybrid Energy Systems: Design and Optimization of Dynamic Voltage Restorers

The integration of renewable energy sources (RESs) into grid-connected systems has become pivotal in addressing climate change and building a sustainable, carbon-neutral society. However, challenges like power quality issues, grid stability, and fault ride-through (FRT) requirements remain significant. This study explores strategies to enhance FRT capabilities and improve power quality in hybrid energy systems. It emphasizes the role of advanced devices such as Dynamic Voltage Restorers (DVRs), EV charging stations, and energy storage solutions. The effectiveness of these technologies is analyzed under various fault scenarios, highlighting improvements in transient response, voltage stability, and harmonic reduction. Additionally, hybrid energy systems, combining multiple RESs and backup sources, are proposed as viable solutions to address intermittency and system reliability. MATLAB/SIMULINK simulations validate the proposed approaches, showcasing their effectiveness in mitigating grid disturbances and maintaining operational efficiency. The findings underline the importance of innovative control strategies and hybrid configurations in ensuring a resilient and sustainable energy infrastructure.