Integrity Of Infrastructure Research Articles

Temperature-adaptive artificial neural network model for enhanced fault detection in railway tracks using acoustic emission sensor data

ABSTRACT Maintaining railway infrastructure integrity is essential for global trade and transportation, as heavy traffic and adverse environments heighten the risk of creep and fatigue-related failures. Acoustic Emission (AE) techniques, widely used in non-destructive testing (NDT), enable real-time health monitoring by detecting stress waves generated during fault nucleation. However, temperature variations significantly influence the wave propagation velocity, affecting the accuracy of fault localisation by altering the Time of Arrival (TOA). This study addresses these challenges by developing an Artificial Neural Network (ANN) model that incorporates dynamic temperature variations to enhance fault localisation accuracy. The ANN is trained using laboratory data at standard temperatures and tested on field data collected across a wide temperature range (28°C to 65°C). The initial results revealed discrepancies in fault prediction accuracy, prompting model refinement by including temperature as a critical input alongside conventional AE parameters. The enhanced model demonstrates significant improvements in fault localisation accuracy, emphasising the importance of accounting for temperature effects. This research contributes to the advancement of AI-driven solutions for reliable real-time fault detection, promoting safer and more efficient railway operations under diverse environmental conditions.

Investigate Advanced Techniques for Power System Protection, Fault Detection, and Restoration to Enhance the Reliability and Resilience of Electrical Grids

Investigate Advanced Techniques for Power System Protection, Fault Detection, and Restoration is a critical area of study aimed at enhancing the reliability and resilience of electrical grids. As modern power systems face increasing complexities due to the integration of renewable energy sources, aging infrastructure, and growing demand for electricity, innovative protection strategies have become essential to mitigate risks associated with electrical faults and disturbances. The significance of this topic lies not only in improving the operational efficiency of power systems but also in minimizing economic losses and ensuring public safety. Advanced techniques in power system protection encompass a range of methodologies, including the implementation of digital protective relays, circuit breakers, and sophisticated communication systems. These technologies enable real-time monitoring and rapid fault detection, which are vital for maintaining continuous power supply. The evolution of machine learning (ML) and artificial intelligence (AI) has further revolutionized this field by facilitating self-healing grids that can autonomously detect, diagnose, and restore service after outages, significantly reducing outage durations and enhancing reliability metrics such as the System Average Interruption Duration Index (SAIDI). Despite the advancements, challenges remain, particularly concerning cyber security risks, regulatory compliance, and the adaptation of protection schemes to accommodate fluctuating energy sources. These complexities necessitate ongoing research and development to create adaptive systems capable of responding to dynamic operational conditions while safeguarding infrastructure integrity. Moreover, the integration of emerging technologies must balance innovation with practical application, ensuring that protective measures not only meet current needs but also anticipate future demands within the energy sector. This investigation into advanced power system protection techniques is pivotal for addressing the pressing issues of system reliability and resilience in the face of evolving electrical grid dynamics. It highlights the critical role of technology in modernizing utility operations and lays the groundwork for future advancements in grid management and outage restoration strategies. Key Words: Power system protection, Fault detection, Distributed Energy Resources (DERs), Islanding, Grid reconnection, Fault localization, Microgrid protection, Fault current variability, System resilience

Development of a scaled‐down single‐viaduct model for comprehensive rail potential monitoring and stray current analysis

AbstractThis research investigates the intricate relationship between load variations, environmental conditions, and the resultant stray current generation within DC railway systems. A scaled‐down single‐viaduct model was employed to establish a controlled experimental environment for real‐time monitoring of voltage differentials between the railway tracks and the grounding structure. This research developed a stray current monitoring system for a scaled‐down single‐viaduct model. The system comprises of a power supply, voltage and current measurement capabilities at the power supply, and two levels of stray current measurements at each point. Data such as ambient temperature and humidity, current and voltage at the power supply, and voltage differentials between the railway tracks and the grounding structure are recorded using an Arduino Mega2560 microcontroller. It is hypothesised that stray currents, quantified as voltage differentials between the tracks and the grounding structure, vary in response to changes in load and environmental conditions. Experiments were conducted under various scenarios: variable load, constant load with rain, and constant load across different seasons. The results indicated that with a variable load, the voltage between the tracks increased. During constant load with rain, the average voltage between the tracks rose due to decreased insulation between the tracks and the grounding structure. Lastly, under constant load across different seasons, the most significant voltage change was observed during the rainy season. The findings demonstrate a significant correlation between these variables, with a pronounced influence of environmental conditions, particularly precipitation, on voltage fluctuations. These results underscore the critical importance of effective stray current mitigation strategies for safeguarding the integrity of railway infrastructure and ensuring the safety of personnel. The study offers a valuable contribution to the understanding of stray current phenomena and provides essential insights for the development of advanced protection systems.

Beyond the Firewall: The Revolutionary Promise of Avaya Secure Access

Unified Communication is transforming the way organizations operate, offering seamless integration of various communication tools into a single platform. At the forefront of this transformation is Avaya, an industry leader known for its innovative solutions that enhance productivity and security. One such groundbreaking innovation is the Avaya Secure Access Link (SAL) Gateway, a game-changer for secure remote support in Unified Communication environments. The SAL Gateway is designed to fortify your network by securely managing IP and Hostnames, ensuring that remote access is both safe and efficient. This technology not only protects valuable data but also simplifies the complexity of network security management. Avaya’s SAL Gateway acts as a robust intermediary, providing authorized access to your Unified Communication systems while keeping malicious entities at bay. With the rise of remote work, the necessity for secure, reliable communication solutions has never been more evident. Avaya’s approach to Unified Communication emphasizes the importance of security without compromising on performance. The SAL Gateway exemplifies this by offering unparalleled protection, reducing the risk of breaches, and maintaining the integrity of your communications infrastructure. In conclusion, Avaya’s Secure Access Link Gateway is more than just a security tool; it's a revolutionary promise for the future of Unified Communication. By securing IP and Hostnames, it ensures that your network remains resilient against potential threats, allowing your organization to thrive in an increasingly digital world.

Transmission line trip faults under extreme snow and ice conditions: a case study

Extreme weather events, particularly snow and ice storms, present significant threats to the stability and reliability of high-voltage transmission lines, leading to substantial disruptions in power supply. This study delves into the causes and consequences of transmission line trip faults that occur under severe winter conditions, with a focused case study in Inner Mongolia—an area frequently impacted by snow and ice hazards. By systematically analyzing field data collected during critical periods of ice accumulation, this research identifies and examines key factors contributing to faults, such as conductor galloping, insulator degradation, and structural fatigue. These issues are often exacerbated by prolonged exposure to low temperatures and high wind speeds, which further compromise the integrity of transmission infrastructure. In addition to field observations, comprehensive testing of the affected insulators and components reveals mechanical and electrical vulnerabilities that play a significant role in the occurrence of trip faults. To combat these challenges, the paper proposes a series of mitigation and prevention strategies. These include enhancing design specifications to ensure resilience against increased ice and wind loads, deploying real-time monitoring systems capable of detecting early indicators of conductor galloping and ice accumulation, and employing advanced de-icing technologies to reduce the risk of ice-related failures. Moreover, the integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI)-based fault detection tools presents promising opportunities for improving remote monitoring capabilities and enabling proactive maintenance interventions. By leveraging these innovative technologies, the resilience of transmission lines in harsh climates can be significantly enhanced. The findings of this study not only provide a comprehensive framework for minimizing the impact of extreme weather on transmission infrastructure but also contribute valuable insights toward fostering a more reliable and resilient power grid capable of withstanding the challenges posed by an increasingly volatile climate.

AN EVALUATION PROCEDURE FOR RIGID AIRFIELD PAVEMENTS

The evaluation of rigid airfield pavements is essential for ensuring their structural integrity under the heavy loads imposed by aircraft. Traditional methods, which often involve destructive testing, can cause significant operational disruptions. This paper presents a nondestructive testing (NDT) approach utilizing the Falling Weight Deflectometer (FWD) to assess pavement conditions rapidly and effectively without compromising airfield operations. Developed by the U.S. Air Force Engineering and Services Center (AFESC) and the U.S. Army Engineer Waterways Experiment Station, this methodology leverages wave propagation and surface deflection techniques to evaluate pavement structural health. The FWD method has proven superior in terms of speed, data quality, and operational efficiency. It enables the rapid collection of data points with high repeatability, reducing manpower requirements and enhancing the identification of potential failure points within the pavement structure. Additionally, the FWD's ability to measure load transfer across jointed concrete slabs offers a significant advantage in assessing pavement performance under aircraft loads. This paper outlines the development of the evaluation procedure, including the selection of the FWD system, the theoretical basis of the method rooted in multi-layered linear elastic theory, and the practical application of this approach in field conditions. A case study at Grissom Air Force Base, Indiana, illustrates the procedure's application, demonstrating its effectiveness in determining the structural capacity of heavily loaded airfield pavements and guiding maintenance and repair decisions. This innovative NDT approach marks a significant advancement in airfield pavement evaluation, offering a more efficient and less intrusive method for maintaining the structural integrity of critical aviation infrastructure. (Abstract generated by AI tool ChatGPT 4)

Enhancing infrastructure project outcomes through optimized contractual structures and long-term warranties

PurposeThis study delves into the influence of contractual frameworks on infrastructure project timelines and evaluates the role of long-term warranty agreements in maintenance efficacy. It underscores the correlation between construction contract structures and prevalent project delays, advocating for a revision in the allocation of responsibilities to mitigate such delays effectively.Design/methodology/approachWhile previous research has explored individual aspects of construction management, such as contractor incentives and risk allocation, our study uniquely integrates these elements to develop a comprehensive model that includes the effects of long-term warranty agreements and penalty clauses.FindingsThe findings advocate for contract revisions that entail clearly articulated responsibilities and thorough impact assessments, aimed at enhancing the efficiency of project execution and optimizing infrastructure investment returns. Concrete examples are provided from large-scale infrastructure and public works maintenance projects, illustrating the benefits of well-defined penalty clauses in curbing delays and ensuring sustained quality through long-term warranties. Our results demonstrate that optimized contractual structures can significantly reduce project delays and enhance maintenance effectiveness.Originality/valueThis study addresses significant gaps in understanding construction contract management dynamics, especially in transportation infrastructure. It rigorously analyzes how penalty clauses and long-term warranties impact contractor behavior and project outcomes. Key findings show that the benefits of long-term warranties, including social advantages, depend heavily on the strictness of penalty clauses. Innovatively, it employs a First-Price Sealed-Bid Auction framework with empirical data from various case studies, enhancing contract structure optimization for better stakeholder alignment and infrastructure integrity. These insights notably advance construction contract management methodologies.

Securing the Future of Railway Systems: A Comprehensive Cybersecurity Strategy for Critical On-Board and Track-Side Infrastructure.

The growing prevalence of cybersecurity threats is a significant concern for railway systems, which rely on an extensive network of onboard and trackside sensors. These threats have the potential to compromise the safety of railway operations and the integrity of the railway infrastructure itself. This paper aims to examine the current cybersecurity measures in use, identify the key vulnerabilities that they address, and propose solutions for enhancing the security of railway infrastructures. The report evaluates the effectiveness of existing security protocols by reviewing current standards, including IEC62443 and NIST, as well as case histories of recent rail cyberattacks. Significant gaps have been identified, especially where modern and legacy systems need to be integrated. Weaknesses in communication protocols such as MVB, CAN and TCP/IP are identified. To address these challenges, the paper proposes a layered security framework specific to railways that incorporate continuous monitoring, risk-based cybersecurity modeling, AI-assisted threat detection, and stronger authentication methodologies. The aim of these recommendations is to improve the resilience of railway networks and ensure a safer, more secure infrastructure for future operations.

Portulaca oleracea as a Green Dual-Action Biocide and Corrosion Inhibitor Against Thiosulfate-Reducing Bacterial Biofilms on Carbon Steel

Microbially influenced corrosion poses a significant threat to the integrity and longevity of carbon steel infrastructure, particularly in environments conducive to biofilm formation by thiosulfate-reducing bacteria (TRB) to carbon steel. This study explores the potential of Portulaca oleracea, an edible plant species, as a dual-action biocide and green corrosion inhibitor for mitigating MIC adhesion. Through a comprehensive suite of experimental and analytical techniques, including electrochemical analysis, microbial analysis, gravimetric methods, and surface characterization, the efficacy of Portulaca oleracea extract is evaluated for its ability to inhibit TRB growth and biofilm formation while concurrently providing corrosion protection to carbon steel substrates. The electrochemical analyses reveal the extract’s capacity with the anodic reaction inhibition achieving 80%, thereby reducing the overall corrosion rate of carbon steel in the presence of TRB biofilms. Complementary microbial analyses, such as viable cell counting using test kits, elucidate the biocidal action of the extract, effectively suppressing TRB growth and biofilm development, with the presence of 20 ppm of the extract reducing bacterial growth. Surface characterization techniques provide insights into the adsorption behavior of the extract’s constituents on the carbon steel surface, forming a protective film that mitigates corrosion and biofilm adhesion. The adsorption of the extract at the interface between mild steel and the formation water adheres to Langmuir isotherm. Overall, the biocorrosion issue we are addressing in this work is crucial for ensuring the sustainability and efficiency of equipment, pipelines, and other metal-based systems.

Security and safety concerns in the age of AI

Artificial Intelligence (AI) is transforming industries at an astonishing rate, reshaping how we live, work, and interact with technology. Yet, as AI becomes more pervasive, it brings urgent questions about security and safety. This article explores these critical issues, drawing a clear distinction between AI security and AI safety—two concepts that are often misunderstood but are crucial for responsible AI deployment. AI security focuses on protecting systems from external threats like data breaches, adversarial attacks, and unauthorized access. As AI systems increasingly handle sensitive data and control critical operations, securing them against such risks is essential. A breach or failure could compromise not only privacy but also the integrity of critical infrastructures. On the other hand, AI safety extends beyond technical defenses to the broader societal implications of AI. Issues like algorithmic bias, ethical decision-making, and unintended consequences of AI systems highlight the risks to human well-being. As AI becomes more autonomous, its alignment with human values and societal norms becomes paramount. Furthermore, the existential risks posed by advanced AI—such as loss of control or unintended outcomes—raise profound questions about the future of human-AI coexistence. This article delves into real-world case studies of AI failures and near-misses, offering tangible insights into the potential consequences of unchecked AI growth. It also explores strategies for mitigating these risks, balancing the pursuit of innovation with the need for transparency, accountability, and ethical oversight. As we look to the future, international cooperation and robust regulatory frameworks are essential to managing AI’s growing influence. By examining both technical and ethical dimensions, this article equips readers with a comprehensive understanding of AI security and safety, urging a proactive approach to managing the risks and harnessing the potential of this powerful technology.

Three-Dimensional Model of Ionic Species Concentration and Flux During Localised Corrosion of Steel in Marine Environment

Localized corrosion, exemplified by pitting and crevice corrosion, presents a significant challenge in industrial and marine settings due to the presence of chloride ions in seawater, brines and industrial fluids. It initiates and spreads at specific points on metal surfaces and its hidden rapid metal degradation beneath intact surface layers complicates detection and monitoring, increasing the risk of unforeseen structural failures and jeopardizing critical infrastructure integrity. Factors influencing its severity include chloride concentration, pH levels, temperature variations and aggressive ion presence, which degrade protective oxide layers and expose metal to accelerated corrosion. This study aims to enhance predictive capabilities to effectively manage corrosion in chloride-rich environments. Using COMSOL Multiphysics software, this study models a microscale geometry representing a micropit and simulates chloride-induced localized corrosion in steel, by integrating Nernst-Planck equations to incorporate electrochemical reactions, ion transport dynamics and three-dimensional geometrical features. Key parameters such as chloride concentration, pH and material properties are crucial in the modelling approach to simulate the formation and growth of corrosion sites. For 0 to 800 seconds simulation, the model displays the flux of ionic species into and out of the pit. At 800 second, the results demonstrate a peak of Fe2+ concentration of approximately 2.53×103 mol/m3 at the deepest section, the Cl- of 7×103 mol/m3 and an increase in H+ inside the pit, reducing the pH from 8 to about 4.9. These concentration changes indicate substantial metal dissolution is actively occurring inside the pit, making COMSOL Multiphysics software a versatile platform for coupling multiphysical phenomena, enabling comprehensive analysis and 3D visualization of corrosion processes.

Investigation of band-beam slabs subjected to close-in blast loading - Experimental and numerical study

Investigation of band-beam slabs subjected to close-in blast loading - Experimental and numerical study

Towards prompt tuning-based software vulnerability assessment with continual learning

Towards prompt tuning-based software vulnerability assessment with continual learning

Legal Measures to Support the Process of Asset Recovery in the State-Owned Enterprises Banking Sector Post-Corruption Cases

This study aims to identify obstacles and formulate strategic measures to optimize asset recovery in the State-Owned Enterprises Banking sector post-corruption cases. This research employs a normative legal research method with a comparative approach, where the collected legal materials are analyzed qualitatively to describe the problems and answer the research objectives. The results show that asset recovery faces multidimensional challenges, including weaknesses in the legal framework, limitations in the capacity and integrity of law enforcement officers, lack of facilities and infrastructure, and efforts by the accused to avoid asset recovery. Optimizing asset recovery requires a comprehensive strategy encompassing the enactment of the Bill on Criminal Asset Forfeiture, strengthening the legal and institutional framework, enhancing the capacity and integrity of law enforcement, improving facilities and infrastructure, optimizing the civil route, and strengthening international cooperation. Therefore, it is recommended that the Government, Directors of State-Owned Enterprises Banking, the Asset Recovery Center, and law enforcement agencies undertake various optimization efforts, such as accelerating the enactment of the Bill on Criminal Asset Forfeiture, increasing the budget, strengthening corruption prevention systems, enhancing the competence of law enforcement officers, and strengthening international cooperation. Furthermore, the Public Prosecutor is expected to maximize the use of Article 18 of Law Number 31 of 1999 in indictments, enabling judges to issue optimal rulings for asset recovery in the State-Owned Enterprises Banking sector post-corruption cases.

A Hybrid Deep Learning Model for Enhanced Structural Damage Detection: Integrating ResNet50, GoogLeNet, and Attention Mechanisms.

Quick and accurate structural damage detection is essential for maintaining the safety and integrity of infrastructure, especially following natural disasters. Traditional methods of damage assessment, which rely on manual inspections, can be labor-intensive and subject to human error. This paper introduces a hybrid deep learning model that combines the capabilities of ResNet50 and GoogLeNet, further enhanced by a convolutional block attention module (CBAM), proposed to improve both the accuracy and performance in detecting structural damage. For training purposes, a diverse dataset of images depicting both structural damage cases and undamaged cases was used. To further enhance the robustness, data augmentation techniques were also employed. In this research, precision, recall, F1-score, and accuracy were employed to evaluate the effectiveness of the introduced hybrid deep learning model. Our findings indicate that the hybrid deep neural network introduced in this study significantly outperformed standalone architectures such as ResNet50 and GoogLeNet, making it a highly effective solution for applications in disaster response and infrastructure maintenance.

Climate Change Impact on the Stability of Soil Slopes from a Hydrological and Geotechnical Perspective

Climate change (CC) is expected to cause significant changes in weather patterns, leading to extreme phenomena. Specifically, the intensity of precipitation extremes is continuously escalating, even in regions with decreasing average precipitation levels. Given that CC leads to long-term shifts in weather patterns and may affect the precipitation characteristics (i.e., frequency, duration, and intensity) directly related to groundwater table fluctuations and soil erosion phenomena, it has the potential to significantly affect soil slope instabilities. In turn, slope stability and the structural integrity of nearby structures and infrastructure will be affected. Accordingly, the present paper focuses on the impact of CC on the geohazard of soil slope instability by considering both hydrological aspects, i.e., the impact on rainfall intensity on the groundwater table and the geotechnical aspects of this complex problem. The findings reveal that the impact of CC on potential slope instabilities can be detrimental or even beneficial, depending on the specific site and water conditions. Therefore, it is essential to do the following: (a) collect all the available data of the area of interest, (b) assess their variations over time, and (c) examine each potentially unstable slope on a case-by-case basis to properly mitigate this geohazard.

Prevention and Control of Biofouling Coatings in Limnoperna fortunei: A Review of Research Progress and Strategies.

The increasing environmental concerns of conventional antifouling coatings have led to the exploration of novel and sustainable solutions to address the biofouling caused by Limnoperna fortunei. As a rapidly expanding invasive species, the fouling process of Limnoperna fortunei is closely associated with microbial fouling, posing significant threats to the integrity of aquatic infrastructure and biodiversity. This review discusses recent progress in the development of non-toxic, eco-friendly antifouling coatings that are designed to effectively resist biofouling without using toxic chemicals. Recent research has focused on developing novel non-toxic coatings that integrate natural bioactive components with advanced material technologies. These formulations not only meet current environmental standards and exhibit minimal ecological impact, but also possess significant potential in preventing the attachment, growth, and reproduction of Limnoperna fortunei. This review aims to provide scientific guidance by proposing effective and sustainable solutions to address the ecological challenges presented by Limnoperna fortunei. The insights gained from current research not only reveal novel antifouling methods, but also identify key areas for further investigation aimed at enhancing performance and environmental compatibility.

Ensuring Quality and Consistency in OPC-53 Cement Testing: A Comprehensive Proficiency Testing Evaluation

The proficiency testing (PT) program is a critical part of quality assurance in laboratories, especially within industries like construction where cement testing is essential. This study provides a comprehensive evaluation of the fifth PT scheme for Ordinary Portland Cement (OPC) grade 53, conducted by the Nodal Laboratory. The key parameters assessed in this program were standard consistency, compressive strength, and soundness using the Le-Chatelier method. Twenty-four laboratories participated, and their performance was analyzed using advanced statistical methods, adhering to ISO 17043:2010 and ISO 13528:2015 standards. The results showed that most laboratories performed satisfactorily, with only one outlier identified in the soundness test. This paper covers all aspects of the PT program, from sample preparation to the distribution of materials, as well as the homogeneity and stability tests conducted on the samples. Statistical evaluation was carried out using Z-scores, which allowed the laboratories to be ranked according to their performance in each parameter. This method highlights discrepancies and provides a clear framework for participating laboratories to improve their testing procedures. Proficiency testing is essential for maintaining high standards in laboratory performance. It helps standardize testing methods across multiple laboratories, ensuring that OPC-53 cement meets strict quality and performance specifications. The program enables laboratories to identify performance gaps and take corrective actions, ensuring the reliability and accuracy of results. Participation in PT programs is a step toward continuous improvement, keeping laboratories aligned with international standards, ultimately benefiting the broader construction industry by ensuring the structural integrity of buildings and infrastructure. This study underscores the importance of regular PT participation. It not only provides a benchmark for laboratory performance but also ensures compliance with ISO standards. This fosters the consistent quality of cement used in large-scale construction, reducing the risk of structural failures and contributing to long-term safety and durability. The findings demonstrate the positive impact of PT programs on overall testing accuracy and laboratory reliability, emphasizing their role in advancing best practices in the construction industry.

CONFORMIDADE DE PAINÉIS ELÉTRICOS COM A NR-10 E NBR 5410

As modern industry integrates into the era of automation 4.0, characterized by information exchange and connectivity, there are still industries with outdated processes and equipment. The NBR 5410 and NR-10 standards play crucial roles, stipulating requirements and safety measures for low-voltage electrical installations and protecting the health and safety of workers. Electrical panels, fundamental in this context, regulate industrial processes automatically and without human intervention, ensuring not only efficiency and productivity, but also safety and effective maintenance of operations. In view of the above, the study aims to analyze how electrical panels can be configured and adjusted to comply with Brazilian standards NR-10 and NBR 5410. This study adopts bibliographic methodology, extensively reviewing academic literature, articles, technical standards and relevant related documents the installation of electrical panels. A careful selection of primary and secondary sources will be made to ensure a comprehensive understanding of current and emerging practices in the field. The process includes an analysis of extracted data to identify patterns, challenges and opportunities for innovation, providing a solid basis for discussion and conclusions in the study. In this way, the application of these standards contributes to a cycle of continuous improvement in the electrical industry. By establishing safe and efficient procedures, standards not only protect the individuals directly involved, but also benefit society as a whole by ensuring the integrity and security of critical infrastructures.

Recent Advances (2018–2023) and Research Opportunities in the Study of Groundwater in Cold Regions

ABSTRACTIncreasing greenhouse gas levels drive extensive changes in Arctic and cold‐dominated environments, leading to a warmer, more humid, and variable climate. Associated permafrost thaw creates new groundwater flow paths in cold regions that are causing unprecedented environmental changes. This review of recent advances in groundwater research in cold environments has revealed that a new paradigm is emerging where groundwater is at the center of these changes. Groundwater flow and associated heat and solute transport are now used as a basis to understand hydrological changes, permafrost dynamics, water quality, integrity of infrastructure along with ecological impacts. Although major advances have been achieved in cold regions' cryohydrogeological research, the remaining knowledge gaps are numerous. For example, groundwater as a drinking water source is poorly documented despite its social importance. Lateral transport processes for carbon and contaminants are still inadequately understood. Numerical models are improving, but the highly complex physical‐ecological changes occurring in the arctic involve coupled thermal, hydrological, hydrogeological, mechanical, and geochemical processes that are difficult to represent and hamper quantitative analysis and limit predictive capacity. Systematic long‐term observatories where measurements involving groundwater are considered central are needed to help resolve these research gaps. Innovative transdisciplinary research will be critical to comprehend and predict these complex transformations.