Resilient Infrastructure Research Articles

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.

Experimental Investigation of Marine Soil Stabilization with Recycled Aggregates and MgO: Implications for CO<sub>2</sub> Sequestration

Dredged marine soils are increasingly recognized as a valuable resource amidst growing environmental concerns and the need for sustainable waste recycling. This study presents an innovative soil stabilization technique combining recycled aggregate (RA) and magnesium oxide (MgO), with a dual focus on enhancing soil properties and promoting carbon dioxide (CO2) sequestration. The stabilizing effects of RA and MgO were evaluated independently and synergistically under varied curing conditions and durations, with microstructural and mechanical properties analysed using scanning electron microscopy (SEM), X-ray diffraction (XRD), and uniaxial tests. Carbonation experiments quantified CO2 fixation potential, demonstrating that the formation of hydration and carbonation products, in conjunction with the dynamic moisture content and pH conditions, played a significant role in enhancing the structural reinforcement of the soil. The combined RA-MgO treatment achieved superior mechanical stability (1.28-3.02 MPa) and a CO2 sequestration capacity of up to 11 g/kg without compromising performance. This study highlights the dual environmental and structural benefits of utilising RA and low-content MgO for marine soil stabilization, offering a sustainable pathway to reduce carbon emissions, promote waste recycling, and support resilient infrastructure development.

Bio-Concrete and Its Self-Healing Capacity to Next-generation Sustainable Architecture: A Comprehensive Review

Concrete is commonly used as a foundation material in the construction sector. It's been around for over two centuries, and it looks like it'll keep being the material of choice for construction for the foreseeable future. Structural materials, long-term degradation creates microcracks which increases permeability and eventually lead to failure. Moreover, the identification and remediation of these fissures provide a formidable challenge, as they jeopardize the integrity and longevity of concrete buildings, particularly those subject to rigorous sealing criteria. Cement manufacturing and widespread usage both contribute to climate imbalance, with the former responsible for 5–7% of global carbon dioxide emissions. Biotechnology provides a viable alternative by allowing the culture of bacteria to produce calcium carbonate, a key ingredient in cement. Biomineralization and crystallization processes play a crucial role in the synthesis and transportation of concrete-compatible chemicals, enabling their delivery to fissure within concrete structures. This review paper discusses a methodology for advancing sustainable architecture in the future, utilizing the adaptable metabolic processes of microorganisms as a foundation. This involves integrating microbial technologies and materials created by microorganisms into the field of built environment design and construction. Currently, there is a growing presence in the market of innovative materials such as Bio-cement, which exhibit lower levels of embodied carbon compared to traditional materials. Furthermore, it discusses advancements in the synthesis and optimization of bio-concrete to address challenges in energy efficiency, structural integrity, and lifecycle performance. By bridging sustainability goals with cutting-edge technology, bio-concrete emerges as a cornerstone for resilient and eco-friendly infrastructure, laying the foundation for innovative architectural paradigms.

Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment

This paper introduces a framework to perform operational modal analysis (OMA) for structural health monitoring (SHM) by presenting the development and validation of a low-power, solar-powered wireless sensor network (WSN) tailored for bridge structures. The system integrates accelerometers and temperature sensors for dynamic structural assessment, all interconnected through the energy-efficient message queuing telemetry transport (MQTT) messaging protocol. Moreover, it delves into the details of sensor selection, calibration, and the design considerations necessary to address the unique challenges associated with bridge structures. Special attention is given to the solar-powered aspect, allowing for extended deployment periods without the need for frequent maintenance or battery replacements. To validate the proposed system, a comprehensive field deployment was conducted on an actual bridge structure. The collected data were transmitted through MQTT messages and analyzed by means of OMA. Comparative studies with traditional wired systems underscore the advantages of the solar-powered wireless solution in terms of sustainability, scalability, and ease of deployment. Results from the validation phase demonstrate the system’s capability to provide accurate and real-time data needed to assess the health state of the monitored asset. This paper concludes with insights into the practical implications of adopting such a solar-powered WSN, emphasizing its potential to revolutionize bridge health monitoring by offering a cost-effective and energy-efficient solution for long-term infrastructure resilience.

Support Vector Machine (SVM) based Detection for Volumetric Bandwidth Distributed Denial of Service (DVB-DDOS) attack within gigabit Passive Optical Network

The dynamic bandwidth allocation (DBA) algorithm is highly impactful in improving the network performance of gigabit passive optical networks (GPON). Network security is an important component of today’s networks to combat security attacks, including GPON. However, the literature contains reports highlighting its vulnerability to specific attacks, thereby raising concerns. In this work, we argue that the impact of a volumetric bandwidth distributed denial of service (DVB-DDOS) attack can be mitigated by improving the dynamic bandwidth assignment (DBA) scheme, which is used in PON to manage the US bandwidth at the optical line terminal (OLT). Thus, this study uses a support vector machine (SVM), a machine learning approach, to learn the optical network unit (ONU) traffic demand patterns and presents a hybrid security-aware DBA (HSA-DBA) scheme that is capable of distinguishing malicious ONUs from normal ONUs. In this article, we consider the deployment of the HSA-DBA scheme in OMNET++ to acquire the monitoring data samples used to train the ML technique for the effective classification of ONUs. The simulation findings revealed a mean upstream delay improvement of up to 63% due to the security feature offered by the mechanism. Besides, significant reductions for the upstream delay performance recorded at 63% TCONT2, 65% TCONT3, and 95% TCONT4 and for frame loss rate reduction for normal ONU traffic, respectively, were observed in comparison to the non-secure DBA mechanism. This research provides a significant stride towards secure GPONs, ensuring reliable defense mechanisms are in place, which paves the way for more resilient future broadband network infrastructures.

Towards Resilient Infrastructure: Identifying and Prioritising Important Risk Factors in PPP Road Projects

Towards Resilient Infrastructure: Identifying and Prioritising Important Risk Factors in PPP Road Projects

Porothermoelasticity of thermally shocked asphalt material under a multi-phase lag model.

This investigation represents porothermoelastic asphalt material with thermal shock due to multi-phase lag model of thermoelasticity. By applying proper boundary conditions to the normal mode approach, we were able to achieve the precise solution. The graphs provide numerical results for the physical quantities supplied in physical domain. The temperature, displacement, and stress distribution numerical results are given and graphically displayed. To explain the impacts of voids, tine, and phase-lags on the field quantities, comparisons have been done. This research is interesting because it applies the multi-phase lag model and porothermoelasticity to asphalt materials, especially when considering thermal shock. The potential to enhance the longevity, performance, and sustainability of asphalt infrastructure-roadways, bridges, and runways-makes the significance wide. This research provides a more nuanced and realistic modeling method, which is crucial for developing more resilient and effective infrastructure, by taking into account delayed thermal and mechanical reactions as well as the interaction between heat, mechanical stress, and fluid flow.

Navigating the Intersection of Digital Security, Resilience and Sustainability in Healthcare: A Theoretical Framework and Case Study of Ghana

This paper explores the intersection of digital security, resilience, and sustainability within healthcare systems, providing a theoretical framework and insights from a case study of Ghana. The findings reveal that integrating robust cybersecurity measures enhances the protection of sensitive patient data and ensures the reliability of clinical operations. These measures, supported by resilient infrastructures, mitigate disruptions and maintain service continuity, aligning healthcare systems with global ethical and security standards. The study further demonstrates that sustainable digital transformation strategies, such as adopting energy-efficient data centers and reducing reliance on paper-based workflows, significantly reduce environmental impacts while improving operational efficiency. These practices contribute to achieving Sustainable Development Goal 13 (Climate Action) and underscore the importance of aligning healthcare innovations with ecological stewardship. Lastly, the research highlights the critical role of environmental and social safeguards in fostering equitable healthcare digital transformation. Initiatives targeting compliance with environmental regulations and reducing digital disparities are shown to improve access to healthcare for underserved populations, advancing Sustainable Development Goal 10 (Reduced Inequalities). This study provides actionable recommendations for healthcare organizations and policymakers, emphasizing the need for secure, resilient, and sustainable digital ecosystems that enhance health outcomes while addressing global environmental and societal challenges.

A Milestone Vision on The Applied Methodologies for Concrete Dam Health Monitoring

This research reviews the applied various methodologies for concrete dams’ health monitoring and assessment. The main type of dams focused on this review is the embankment fill dams that serves as an important function in water supply, irrigation, and hydroelectric power. The emphasis is devoted on the integration and assessment of static and dynamic methods for dams monitoring in order to improve safety and increase the life span of dams. Static methods are usually monitor changes that are prolonged over time and are critical for the assessment of the enduring structural health. Whereas, dynamic methods are considered as real-time data crucial to address immediate changes in the environment and operation. Mathematical models and structural health monitoring techniques were surveyed that improves the encompass of hydraulic, thermal, crack, and time (HTCT) model for extreme conditions. In addition, the use of artificial intelligence (AI) was explored and particularly for internal erosion monitoring. Comparison of such analyses reveals that static monitoring is efficient in its approach to assessment over the long term, while dynamic monitoring performs better in coping with instant changes. Based on the review findings, therefore, it could be concluded that an integrated approach with the use of static and dynamic methods, supported by advanced computational models, would enhance the effectiveness of monitoring systems. The future work shall focus on the development of such integrated systems to increase the resilience of dam infrastructures, while the monitoring technologies have to be adapted continuously to the environmental challenges and the complexity of dam systems.

Climate resilient urban regeneration and SDG 11 – stakeholders’ view on pathways and digital infrastructures

ABSTRACT Urban areas grapple with escalating challenges due to climate change, rapid urbanisation, and shifting demographics. Collaborative efforts and innovative applications of big earth data analytics and digital infrastructures are imperative in tackling the issues facing cities and advancing SDG 11. Modernising urban infrastructure is essential, coupled with a focus on people-centric urban regeneration to enhance the well-being of city residents. Urban resilience, encompassing diverse social, economic, environmental, and governance facets, lies at the core of this mission. A resilient city can endure and recover from disruptions, be they natural disasters or human-made crises. Stakeholder engagement, bridging top-down and bottom-up approaches, is pivotal in fostering the use of earth data to promote sustainability and urban resilience. A literature review and on-going research underscores digital infrastructure's potential in fortifying resilient, sustainable cities. Insights from an international forum on ‘Digital Infrastructure for Climate Resilience’ held in Melbourne, Australia in 2023 inform this perspective. Big earth data analytics combined with Urban Digital Twin technologies, emerge as potent tools for urban stakeholders and decision-makers. Despite challenges, opportunities abound to leverage data and digital platforms to bolster sustainable urban development. Effective leadership, government regulations, standards, and cross-sector collaboration are essential for realising this potential.

Predictive Modelling of Soaked California Bearing Ratio in Nano-Silica Stabilized Sub-grade Soil in Lesser Himalayan Region

This research employs predictive modelling techniques to comprehensively understand the soaked CBR, a key parameter indicating the soil's load-bearing capacity under wet conditions. Experimental investigations are conducted on Nano-Silica (NS) treated sub-grade soil samples collected from the Lesser Himalayan Region. These experiments encompass a range of Nano-Silica concentrations and soaking conditions to capture the diverse environmental factors prevalent in the region. Advanced statistical and machine learning tools are applied to analyze the data and develop predictive models. The objective is to establish correlations between Nano-Silica content, soaking conditions, and the resulting soaked CBR. The models are validated against independent datasets to ensure their reliability and generalizability. The findings of this study hold significant implications for sustainable infrastructure development in the Lesser Himalayan Region. A robust predictive model for soaked CBR in Nano-Silica stabilized sub-grade soil not only contributes to the understanding of soil stabilization mechanisms but also aids in optimizing construction practices for enhanced road performance in challenging terrains. This research bridges the gap between traditional geotechnical engineering and emerging nanotechnology applications in soil stabilization, paving the way for resilient and durable infrastructure in geologically complex regions.

Unravelling flash flood dynamics of Song watershed, Doon Valley: key insights for floodplain management

The Himalayan foothills are highly prone to rainfall induced flash floods. This research focuses on the August 19–20, 2022 flash flood event in Song watershed of Doon valley, Uttarakhand caused significant damages to buildings and a road bridge. The study aims to assess the flood intensity through flood simulation in a semi-distributed hydrological model by utilizing rainfall data, land use and soil data. Further, the flood hydrographs generated through hydrological modelling were used to simulate hydrodynamic model to estimate flood depth. Pre and post-flood inundation assessments were conducted using PlanetScope and Sentinel-1 imagery. Furthermore, development activities on river courses were analyzed utilizing Google earth and Bing maps high resolution imagery. Cumulative rainfall observations revealed 344 mm rainfall in Rishikesh and 225 mm in Sahastradhara on 19–20 August for the 24 hrs, contributed in a peak flood discharge 2679 m3/s at the Rishikesh outlet. The simulated flood depth depicted 4.81 m flood depth at the damaged Thano-Bhogpur bridge. The PlanetScope satellite imagery showed 182 m expansion in the cross-sectional width of river at Maldevta after the flood. A 5.36 sq. km. flood area observed throughout the entire Song catchment in two days post event Sentinel-1 imagery. Analysis of high-resolution imageries revealed increasing development activities in floodplains of the catchment, which got affected by flood. The findings indicate urgent need of floodplain management by implementing comprehensive flood risk management plans including early warning systems, land-use regulations based on flood hazard zonation and flood resilient infrastructure to mitigate future flood exposure to society.

Deep learning in cybersecurity: Enhancing threat detection and response

Deep learning (DL) has changed the cybersecurity domain by providing sophisticated tools for detecting and mitigating an evolving landscape of cyber threats. This study explores the application of deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in real-time threat detection and response. These models excel in identifying patterns and anomalies within vast and complex datasets, enabling accurate detection of malware, phishing attempts, and insider threats. Their ability to autonomously learn from diverse sources, such as network traffic, user behaviour, and system logs, enhances the efficacy of cybersecurity systems. Despite these advancements, the field faces significant challenges, including adversarial attacks designed to exploit vulnerabilities in deep learning algorithms. These attacks manipulate input data to deceive models, potentially bypassing security mechanisms and compromising critical systems. Addressing this issue requires a multi-faceted approach, integrating robust training methods, data augmentation, and defensive mechanisms such as adversarial training and gradient masking. Furthermore, explainability and interpretability of deep learning models remain crucial for building trust and improving decision-making in security operations. The paper also emphasizes the importance of a proactive, layered defense strategy to counteract sophisticated cyber threats. This includes combining deep learning with traditional cybersecurity measures and incorporating threat intelligence to enhance system resilience. By bridging the gap between state-of-the-art DL methodologies and practical applications in cybersecurity, this research provides a roadmap for improving threat detection and response capabilities, ultimately contributing to the development of secure, adaptive, and resilient cyber infrastructures.

Future cities and sustainable development: Integrating renewable energy, advanced materials, and civil engineering for urban resilience

This review focuses on the integration of renewable energy, advanced materials, and civil engineering to foster urban resilience in future cities. As urban populations continue to grow, the demand for sustainable solutions to mitigate environmental impact and enhance quality of life becomes increasingly urgent. This review explores how the convergence of renewable energy sources, innovative materials, and civil engineering practices can contribute to the development of resilient cities capable of meeting the challenges of the 21st century. The integration of renewable energy sources, such as solar, wind, and hydroelectric power, offers opportunities to reduce dependence on fossil fuels and mitigate greenhouse gas emissions. Advancements in renewable energy technologies allow for the generation of clean, reliable energy within urban environments, promoting energy independence and reducing carbon footprints. Additionally, the utilization of advanced materials in urban infrastructure can enhance durability, efficiency, and sustainability. Nanotechnology, for example, enables the development of high-performance materials with superior strength, flexibility, and resilience. These materials can be used in construction, transportation, and water management systems to improve infrastructure resilience and longevity. Furthermore, civil engineering plays a crucial role in designing and implementing sustainable urban infrastructure. From green building practices to resilient urban planning, civil engineers are instrumental in creating cities that can withstand environmental hazards and adapt to changing conditions. By integrating renewable energy, advanced materials, and innovative engineering solutions, cities can enhance their resilience to climate change, natural disasters, and other challenges. This review examines case studies and examples of successful integration of renewable energy, advanced materials, and civil engineering in urban development projects worldwide. It also discusses the challenges and opportunities associated with implementing these solutions, including technological barriers, financial considerations, and policy frameworks. The integration of renewable energy, advanced materials, and civil engineering is essential for building resilient and sustainable cities of the future. By embracing innovation and collaboration across disciplines, cities can enhance their capacity to thrive in the face of environmental, social, and economic challenges, ultimately improving the quality of life for urban residents while preserving the planet for future generations.

Modeling peak ground acceleration for earthquake hazard safety evaluation

This paper presents a ground motion prediction (GMP) model using an artificial neural network (ANN) for shallow earthquakes, aimed at improving earthquake hazard safety evaluation. The proposed model leverages essential input variables such as moment magnitude, fault type, epicentral distance, and soil type, with the output variable being peak ground acceleration (PGA) at 5% damping. To develop this model, 885 data pairs were obtained from the Pacific Engineering Research Center, providing a robust dataset for training and validation. The ANN architecture comprises 4 nodes in the input layer, two hidden layers each containing 25 nodes, and a single-node output layer, resulting in 750 unknown weight and bias values that the model must optimize. Following the model assessment, a genetic algorithm (GA) was integrated with the ANN model to enhance its predictive capabilities. This integration aimed to forecast 20 potential earthquake scenarios, a crucial step in validating the model’s effectiveness. The results were promising, as the ANN-GA successfully predicted earthquake occurrences in 15 out of 20 scenarios. These findings underscore the model’s potential in accurately forecasting seismic events, thereby contributing to the development of more resilient infrastructure and better-informed urban planning strategies.

Prospects of application of composite reinforcement in the construction industry

Problem. In the context of the ongoing conflict in the country, the need for metals in military applications highlights the importance of exploring alternative materials for the construction sector. This paper addresses the problem of corrosion in traditional metal reinforcement, which significantly affects the durability and safety of structures. Goal.The goal of this study is to evaluate the prospects of using composite reinforcement in construction, focusing on its mechanical properties, corrosion resistance, and overall performance compared to conventional metal reinforcement. Methodology. The methodology employed includes a comparative analysis of the physical and mechanical properties of metal and composite reinforcement, as well as a review of relevant literature on corrosion mechanisms and protection methods for building materials. Results indicate that composite reinforcement not only offers superior corrosion resistance but also significantly reduces weight, enhancing the overall efficiency of construction projects. Originality.The originality of this study lies in its comprehensive examination of the benefits and challenges of implementing composite materials in the construction industry, especially in regions facing resource constraints due to war. Additionally, the findings underscore the potential of composite reinforcement to improve infrastructure resilience while decreasing reliance on traditional metal resources. Practical value. The practical value of this research is evident in its implications for building practices, providing guidelines for the integration of composite reinforcement into new construction projects. By highlighting the advantages of composite materials, this study contributes to the ongoing discourse on sustainable construction practices and resource-efficient engineering solutions, ultimately supporting the development of more resilient infrastructure in challenging environments.