Infrastructure Systems Research Articles

AI in Structural Health Monitoring for Infrastructure Maintenance and Safety

This study explores the growing influence of artificial intelligence (AI) on structural health monitoring (SHM), a critical aspect of infrastructure maintenance and safety. This study begins with a bibliometric analysis to identify current research trends, key contributing countries, and emerging topics in AI-integrated SHM. We examine seven core areas where AI significantly advances SHM capabilities: (1) data acquisition and sensor networks, highlighting improvements in sensor technology and data collection; (2) data processing and signal analysis, where AI techniques enhance feature extraction and noise reduction; (3) anomaly detection and damage identification using machine learning (ML) and deep learning (DL) for precise diagnostics; (4) predictive maintenance, using AI to optimize maintenance scheduling and prevent failures; (5) reliability and risk assessment, integrating diverse datasets for real-time risk analysis; (6) visual inspection and remote monitoring, showcasing the role of AI-powered drones and imaging systems; and (7) resilient and adaptive infrastructure, where AI enables systems to respond dynamically to changing conditions. This review also addresses the ethical considerations and societal impacts of AI in SHM, such as data privacy, equity, and transparency. We conclude by discussing future research directions and challenges, emphasizing the potential of AI to enhance the efficiency, safety, and sustainability of infrastructure systems.

AI in Network Infrastructure: Transforming Telecommunications with Intelligent Systems

AI technologies are drastically changing the backbone of telecommunications networks and bringing unheard-of security, network management, and optimization capabilities. From dynamic resource allocation to predictive maintenance and intelligent traffic management, this article shows how artificial intelligence and machine learning technologies are being used to solve important problems in modern telecommunications. By means of a thorough investigation of AI-driven solutions, this article shows that technologies are offering real-time network optimization, automatic threat identification, and improved service dependability while drastically lowering operating costs. While addressing important technical issues and operational considerations, the conversation covers pragmatic solutions across several network domains, including 5G infrastructure, edge computing, and autonomous networking systems. Examining real-world case studies and developing trends helps this article show how artificial intelligence is not only transforming present network operations but also opening the path for future telecommunications innovations, enabling more resilient, efficient, and intelligent network infrastructure that can adapt to changing technological demand and user expectations.

A Study on Predicting Vibration-Induced Damage of Overhead Oil Pipelines based on Abaqus

Abstract Earthquakes are sudden natural disasters that pose a serious threat to human lives and property. Lifeline systems are essential infrastructure and engineering systems that maintain the economic and social functions of cities or regions, including water supply and drainage systems, oil pipelines, gas systems, etc. In the event of a major earthquake, direct damage to lifeline systems can lead to significant economic losses and casualties. Therefore, exploring the deformation and damage characteristics of lifeline pipelines under seismic hazards and conducting risk assessments is of great practical significance. This study takes a typical overhead oil pipeline in Guangzhou as an example and uses the finite element software Abaqus to predict the vibration-induced damage. Ultimately, recommendations for disaster prevention and mitigation are proposed based on the research findings. The results can serve as a reference for seismic damage prediction, research, and disaster prevention and mitigation of urban lifeline systems.

Percursos de pedestres: um índice a partir das inter-relações entre caminhabilidade e conforto ambiental

Abstract The study of environmental comfort in open urban spaces has undergone constant updates in the last decades. In the meantime, the specific analysis of these aspects within the context of walkability stands out as an area with considerable potential to be investigated. In this context, this article aims to explore walkability in pedestrian routes and its relationship with environmental comfort, proposing a more systematized approach for its application in the strategic planning of cities. Therefore, we proposed the construction of a Pedestrian Walkability and Comfort Index (ICCP), composed of three partial indices that consider thermal, acoustic, and ergonomic aspects of pedestrian paths. The methodology is based on street visualization tools and Geographic Information System (GIS) tools to spatialize and systematize the interactions between walkability and environmental comfort. The research concludes that ICCP is a valid tool for measuring walkability at meso and micro scales, and it can be replicated in other urban contexts. Furthermore, the development and application of ICCP also highlight that rethinking walkability with environmental comfort in urban areas requires an integrated approach to the entire mobility system and urban infrastructure of cities.

A multi-stage resilience analysis framework of critical infrastructure systems based on component importance measures

A multi-stage resilience analysis framework of critical infrastructure systems based on component importance measures

Applying the Stackelberg game to assess critical infrastructure vulnerability: Based on a general multi-layer network model.

Critical infrastructure systems (CIS) are closely related to human life. Attacks against CIS occur frequently, making accurate and effective protection of CIS essential. Vulnerability assessment is the primary issue to achieving this goal. The interconnected characteristic of CIS means that it is best represented by a multi-layer network, but a uniform model is absent. Game theory offers a suitable framework for researching intelligent confrontation. Previous research combining game theory and network science mainly focuses on a single-layer network and lacks a comprehensive assessment that combines qualitative and quantitative aspects of vulnerability. In this paper, we apply the Stackelberg game to the multi-layer network and comprehensively assess vulnerability based on game equilibrium to realize accurate protection. We first present a method for constructing a general model of the multi-layer network and introduce a multi-layer weighted factor to extend topological attribute metrics. Then, we design a Stackelberg game model for the multi-layer network. Furthermore, we qualitatively analyze the impact of multi-layer network characteristics on vulnerability and propose a method to quantify vulnerability. Experiments show that the vulnerability of the multi-layer network is greatly influenced by the multi-layer weighted factor, single-layer network type, and inter-layer coupling method. The quantitative value of network vulnerability does not rely entirely on the topology attributes on which the attack and defense costs depend but also relates to the resources available. Our work provides an adaptive model for CIS and gives a new approach to developing accurate protection based on comprehensive vulnerability assessment, which deserves further study.

The Green Infrastructure Model Based On Waterlogging Mitigation In The Street Landscape Of Sungai Penuh City, Jambi Province

Background: In many cases, the planning of public and social space in Indonesia is not accompanied by the planning of green open space, which is their functions as a healthy environmental balancer. One of the public spaces is the street landscape. This results in to the impact of natural phenomena such as floods, waterlogging in the rainy season, and hot weather in summer. The purpose of this study is to provide an overview of the Green Infrastructure model of roads, can also function as a place to anticipate waterlogging, flooding, air quality improvement, and human-friendly designation. Materials and Methods: The methodology was carried out through field inventory, mapping based on Shp ArcGis data, and a modified literature study. The location of the research is on roads spread across 8 subdistricts of Sungai Penuh City, Jambi Province. Results: Based on government administration and axis content, the public roads of Sungai Penuh City are categorized into class III Provincial Roads. The number of road sections in Sungai Penuh City is 49 (forty-nine) road sections. The details are that the primary collector road landscape consists of 11 road sections, the secondary collector road landscape consists of 12 road sections, and the local road landscape is very dominant, which is as many as 26 roads. The total length of the entire Sungai Penuh City road section is 51.43 km with an average width of 6 meters. Based on the level of urgency, the results of the assessment of priority sub-districts determined 31 road sections spread across Sungai Penuh District, Sungai Bungkal District, and Pondok Tinggi District as priority sub-districts for the implementation of the Green Infrastructure system. Based on hydrological calculations of rainfall for the last 20 years (2003-2023). Conclusion: The Green Infrastructure Model is prioritized for 3 sub-districts. Based on the assessment of the Green Infrastructure Implementation Indicator, two green infrastructure concepts are applied, namely Green Stormwater Infrastructure 1 (GSI 1) and Green Stormwater Infrastructure 2 (GSI 2) which are equipped with the Complete Street concept. The GSI 1 system is applied on 24 roads in Sungai Penuh City using the Stormwater Tree Trench and planter. The GSI 2 system consisting of Stormwater Bump-Out, Stormwater Tree Trench, and Stormwater Planter and the Complete Street concept will be applied on 7 roads in Sungai Penuh City

AI-enhanced predictive maintenance systems for critical infrastructure: Cloud-native architectures approach

Critical infrastructure (CI), such as power grids, transportation systems, and telecommunications networks, is becoming increasingly complex, requiring sophisticated maintenance strategies and procedures to guarantee optimal performance and system durability. This paper examines the transformational potential of AI-driven predictive maintenance systems, highlighting their ability to prevent system failures, minimize downtime, and enhance resource efficiency. Integrating machine learning algorithms with real-time data analytics allows predictive maintenance frameworks to accurately foresee equipment failures, facilitating timely interventions that reduce the risk of catastrophic infrastructure breakdowns. This study primarily examines the development of cloud-native architectures, which include containers, microservices, and orchestration tools like Kubernetes, to facilitate the scalability, flexibility, and resilience required for contemporary CI maintenance systems. These designs facilitate the seamless integration of predictive maintenance solutions across geographically dispersed infrastructure, enabling effective administration of extensive datasets produced by Internet of Things (IoT) sensors, operational logs, and edge computing nodes. The document examines the essential function of intelligent data orchestration in facilitating the prompt gathering, processing, and analysis of operational data, which is vital for AI models to provide precise predictions. The amalgamation of AI-driven predictive maintenance with 5G and forthcoming 6G networks is poised to transform real-time system monitoring, diminishing latency and enhancing decision-making efficacy. Utilizing AI and cloud-native technologies substantially enhances system reliability, cost-effectiveness, and comprehensive infrastructure optimization. This article thoroughly analyses how AI, cloud-native platforms, and intelligent data orchestration may be utilized to tackle the changing maintenance issues of critical infrastructure by examining real-world case studies from sectors like power grids, telecommunications, and transportation. Integrating AI, cloud computing, and IoT in predictive maintenance improves system reliability and prepares critical infrastructure for future autonomous management and optimization developments. The study finishes by discussing new trends, such as the integration of digital twins and the synergies between AI and cloud-native solutions, which will enhance predictive maintenance capabilities.

Revolutionizing Knowledge Management in Transportation Agencies: The Role of Generative AI and Scalable Frameworks

The increased interconnectedness of the globe has led to a fast-paced expansion of transportation systems and, consequently, to the creation of effective Knowledge Management Strategies. By simply put, the ways of decision making, operations management, and solving problems related to the complex infrastructure systems have all improved. Knowledge management is made easier by the use of Generative Artificial Intelligence (AI) which enables all the processes of data generation, data synthesis and even gives room for a real-time insight within a transport department. There is also the availability of Structure and Generative Theory which allows agencies to cope with needs that keep changing without losing the quality and the speed of the flow of the knowledge. In this sense, the present contribution focuses on the perspective of generative AI and structural frameworks in transportation agencies understanding knowledge management and its future prospects in terms of technology usefulness (Chen & Li, 2021). In these external environments, transportation agencies manage vast and complex datasets that change rapidly and are often dispersed. For instance, Generative AI has applications in natural language processing, predictive analytics and intelligent reporting, which assist agencies in solving hurdles posed by conventional knowledge management. When these AI systems are given, scalable frameworks guarantee that the knowledge management systems will address the challenges that come with increased complexity in operations. This research examines how generative artificial intelligence can be applied in various knowledge management situations, with particular focus on how it can be used to deal with mundane processes, enhance responses and help to mitigate problems in the field of transportation (Nguyen et al., 2023). The results underscore the possible benefits of using generative AI within efficient KM systems, such as lower costs, better accuracy, and improved accessibility to the users. On the other hand, the concerns such as data quality, costs of implementation, and other factors like the presence of easy to use interface for non-technical personnel, are also brought up by the authors. These perspectives will be useful for transportation agencies that seek to transform their KM activities. Overcoming these obstacles, generative AI and scalable frameworks can promote radical transformation and development of dynamic and sustainable knowledge systems that meet the needs of transport management in the case of Rahman and Smith, 2023.

Disaster Resilience Through Rainwater Flood Management: The Case of Zaatari Refugees Camp, Jordan

Rainwater floods significantly affect urban areas with vulnerable infrastructure and social systems, worsened by climate change. This study examines flood risk management and its potential for agricultural development in the Zaatari Refugee Camp and surrounding areas. Using GIS-based mapping, hydrology analysis, rainfall data review, and analytical hierarchy process weighting, the research identifies flood risk zones and proposes rainwater harvesting solutions at macro and micro levels. At the macro level, the area is predominantly moderately risky (61.1 percent), with high-risk (22.42 percent) and very high-risk (0.84 percent) zones. Rainwater harvesting structures can reduce runoff to the camp by 83.3 percent (1.211 million m3 [MCM]) overall and 45.6 percent (0.824 MCM) at intervention sites, easing pressure on drainage systems and improving adaptability. Micro-level solutions target high-risk zones, including rooftop rainwater harvesting, which could collect 98,616 m3 annually. The findings highlight the potential of integrating flood risk reduction with water resource development, offering a replicable framework for disaster resilience and sustainable rainwater management. The study bridges gaps between urban and environmental science, moving beyond partial solutions to deliver comprehensive, adaptable strategies.

EXOLIFE: Detecting Exoplanets and Predicting Habitability Using Machine Learning

Exoplanets are among the most studied and remarkable topics in astronomy. Over the years, various methods have emerged for exoplanet detection, allowing for the identification of numerous exoplanet types. In this context, remote sensing and machine learning, which are central to our research, have significantly accelerated the detection process by leveraging algorithms. Our study involved training several machine learning models, including XGBoost, Random Forest, Multilayer Perceptron, K-Nearest Neighbor, Logistic Regression, and Support Vector Classifier, to compare their performance in both habitability assessment and exoplanet detection. The research utilized machine learning models trained on space observation data obtained from NASA, with the Python programming language serving as the foundation for the system's infrastructure. Our hypothesis was that "The detection of exoplanets and their evaluation within the scope of the habitability criterion can be increased to high accuracy rates with machine learning." Unlike merely detecting exoplanets, this study specifically aimed to identify Earth-like exoplanets. The XGBoost algorithm emerged as the most successful model in determining habitability, achieving an accuracy rate of 97.46% and demonstrating high precision and sensitivity. For exoplanet detection, all models achieved a main test accuracy rate of 96%; however, when considering sensitivity and precision, XGBoost was again the most effective. This research, following the synthesis and analysis of these two parameters, achieved a very high success rate compared to previous studies and made a significant contribution to the astronomy/astrophysics literature. Additionally, a Graphical User Interface (GUI) was developed, making the tested models functional through an application. The study successfully reached its goal of contributing important findings to the field.

A study of liquidity cross-chain models based on convex optimization

Blockchain after several years of hustle, bustle, precipitation, and sublimation, with the landing application step by step towards maturity and development, gradually spawned the outreach needs of interacting with other applications. At this stage, only the mobility cross-chain interoperability technology is the infrastructure system that can perfectly solve such needs. To achieve a cross-chain system that defines and realizes user-free liquidity interoperability according to the cross-chain environment and cross-chain requirements, this paper designs a convex optimization-based liquidity contract pair cross-chain model. This model can realize a uniform distributed NFT ID construction identification method for the whole chain, and at the same time use the convex optimization model to obtain liquidity contract pairs that have and only have the global optimal matching, and in this way lock the shortest cross-chain time required, with AMM bonding curve management to achieve immediate liquidity effect, and realize the interconnection and interoperability of users’ assets with free liquidity. Finally, the time-consumption, robustness, and radius of convergence of the models are tested by comparing the model experiments and the proposed model’s proposed cross-chain incentive experiments yield results that verify that the proposed convex optimization-based liquidity contract is efficient, applicable, and secure for cross-chain models.

Contextualizing algorithmic literacy framework for global health workforce education

With the rapid and accelerating advancement of generative artificial intelligence (AI), research is lagging on how to ensure that the health workforce becomes and stays AI-literate. This paper describes a way forward specifically toward establishing an AI-augmented curriculum within global health workforce education. By global health workforce education, we refer to the academic staff or faculty and students in medicine, nursing, global public health, and other health science fields. AI, unlike other technological advancements, is constantly changing. Therefore, the adoption of specific tools for health workforce education has to be analyzed in the context of the educational setting for shaping a sustainable and equitable AI-augmented global health workforce curriculum. This necessitates an integration of AI algorithmic literacy within academic curricula. In this paper, we propose the algorithmic literacy framework (ALF) for global health workforce education to address individual and organizational readiness. At the individual level, ALF examines one’s knowledge of and skills needed to implement AI within the context of their respective health education expertise. At the organizational level, ALF examines readiness across five areas: infrastructure and support systems, institutional support, Information and Communications Technology technical expertise, student engagement, faculty engagement, and analytics technical expertise. ALF offers universities and health workforce training institutions a way of organizing their approach to algorithmic/AI literacy readiness that embraces their organization’s values and, at the same time, urging them to act.

A graph-empowered agent-based simulation: Impacts of coordination schemes on critical infrastructures resilience

Critical infrastructure systems are governed by several sectors working together to maintain social, economic, and environmental well-being. Their cyber–physical interdependencies influence their performance and resilience to routine failures and extreme events. To balance investment and restoration decisions in the face of disruptive events, mostly centralized mathematical formulations and solutions were presented in the literature. However, not all physical and dynamic characteristics of infrastructure systems and their decision makers can be modeled via mathematical models. In this study, we take a different approach and utilize agent-based modeling to simulate city-scale interdependent infrastructure networks as a complex adaptive system. In specific, we design a flexible modular (object-oriented) simulation tool capable of capturing the dynamic behaviors of various networks. We first model each infrastructure as a weighted graph with relevant geospatial attributes. Decision makers for each infrastructure sector are represented by intelligent agents. We then define three information and coordination schemes among agents, including no communication, leader–follower, and decentralized coalitions. To show the applicability of the approach, we use publicly available interdependent water distribution and road networks for the City of Tampa, FL, which is prone to hurricanes. We simulate different magnitudes of physical, cyber, and cyber–physical failures, evaluate resource allocation decisions, made by agents under each coordination scheme, and quantify the aggregated resilience. The simulation platform will help municipalities in various cities to quantify the impact of their collective decision making and identify the best coordination structures when interdependencies are modeled in infrastructure systems.

Advancing civil engineering: The transformative impact of neuromorphic computing on infrastructure resilience and sustainability

The resilience and adaptability of civil infrastructure are paramount for societal well-being and economic stability. This paper examines the transformative impact of neuromorphic computing on enhancing the resilience of infrastructure systems and advancing sustainable engineering practices. Drawing inspiration from the human brain's processing capabilities, neuromorphic computing has been demonstrated to significantly enhance the efficiency of real-time data analysis, learning, and prediction of structural integrity. We detail the fundamental principles of neuromorphic computing, its practical applications within civil engineering, and notable implementations, underscoring its substantial contributions to predictive maintenance, disaster preparedness, and environmental conservation. Our findings reveal that neuromorphic systems, through their capability to continuously learn and adapt, have effectively improved the accuracy of predictive models for infrastructure behavior under various environmental stressors, thereby facilitating more robust disaster response strategies and reducing maintenance costs by up to 30 %. Despite facing technological and integration challenges, neuromorphic computing emerges as a pioneering force in the field of civil engineering, offering a future where infrastructure is not only more durable but also inherently intelligent and responsive to changing conditions. The integration of neuromorphic computing holds the promise of revolutionizing civil engineering practices by fostering greater interdisciplinary collaboration and driving a commitment to environmental sustainability. This study advocates for broader adoption of neuromorphic technologies to cultivate a resilient, adaptive, and sustainable built environment.

Impacts of economic sanctions on population health and health system: a study at national and sub-national levels from 2000 to 2020 in Iran

BackgroundFormal evidence regarding the effects of sanctions on population health status and the health system is scarse in Iran. Given the intricate and multifaceted nature of sanctions, a nuanced understanding of their impact is imperative. We aimed to investigate the magnitude and effects of sanctions on population health and healthcare system during the last two decades in Iran.DesignThis is a mixed-methods research. We quantified the impact of sanctions using 28 indicators, i.e. macroeconomic, healthcare resources and health outcomes indicators from 2000 to 2020. The concurrent qualitative study aimed to explore the pathway of the effect by considering perceptions of both patients and health policy makers towards sanctions; Interview data was analyzed using content analysis.SettingThis study was conducted in Iran at both national and sub-national levels.ResultsOur findings revealed that the trend of 11 indicators (39.2%) had changed after the change point in 2009; four indicators (14.2%) significantly deteriorated after the change point. Further, five indicators revealed significant increases during the sanctions period: Out-of-Pocket payment (OOP), household expenditure on food and mortality rates due to Chronic Obstructive Pulmonary Disease (COPD), thalassemia and hypertension. Our qualitative analysis revealed that patients identified availability, cost and quality of medicines and healthcare services as the most significant challenges that compromised population’s health. From the policy makers’ perspective, the effect of sanctions on health system functions and infrastructures, i.e. economic, political, social, educational and research had significant repercussions on population health.ConclusionWe could not find compelling evidence to establish significant associations between the imposition of sanctions and the trend of population health. Nevertheless, our qualitative study revealed people’s deteriorating life experiences, e.g. increasing catastrophic health expenditure, limited access to necessary medicine, medical equipment, procedures and interventions, imposed by sanctions, with ultimate reducing impact on the overall quality of life. It seems that sanctions have negatively affected financial and physical access to medication and healthcare services. Nonetheless, Iran has demonstrated remarkable resilience against their highly detrimental effects, maybe due to its established economic infrastructure and healthcare system.

Investigation of the effects of mainshock-aftershock sequences on the dynamic responses of pipeline considering soil-pipeline interaction

Pipelines are important structural elements that are frequently used today to meet many infrastructures needs such as drainage, natural gas or water transmission. In this context, the usability of such structures, which are important elements of infrastructure systems, especially after disasters such as earthquakes, is of great importance. For this reason, within the scope of this study, a parametric investigation of the seismic behaviors of a natural gas pipeline system under mainshock-aftershock sequences have been carried out, specifically taking into account the soil-natural gas pipeline interaction (SNGPI) in the help of finite element model (FEM) proposed. Before developing the model of SNGPI system proposed using solid element, the fundamental mode frequencies of the pipeline system modeled using the solid element for the verification have been compared with those of obtained from the pipeline system modeled using the beam element and the analytical solutions. After verification of proposed model is demonstrated, SNGPI system has been modeled and its fundamental modes have been compared with mode frequencies of soil stratum obtained from well-known simple analytic solutions. After this stage, the dynamic analyses of natural gas pipeline (NGP) system in the time domain have been carried out using four different soil systems and four different mainshock-aftershock sequences. The results of the nonlinear time-history analyses have been investigated in terms of the stress and the displacement responses. Parametric evaluations show that the greatest displacements and the stresses occurring at the considered nodes of NGP system may be importantly affected from mainshock-aftershock sequences and soil stiffness changes. As the soil stiffness decreases, both the peak stresses and displacements increased significantly. On the other hand, the same responses obtained under mainshock loadings, which have relatively lower peak ground acceleration (PGA)/peak ground velocity (PGV) ratio compared to aftershock loadings, are generally larger than those obtained under aftershock loadings.

Digital transformation: an investigative study of the performance of digitalization in governance

Purpose Corruption in Nigeria is a wicked issue that is threatening the performance of governance. However, the potentials of digital transformation in governance have proven to resolve similar issues in many countries. This study seeks to understand the reasons why the digital transformation of governance in Nigeria is not ending corruption and the lack of accountability and transparency but is creating new opportunities of corruption. This is because Nigeria continues to decline in its ranking in the corruption perception index – yearly. The pupose of this study is to identify the various factors responsible for this and proffers adequate solutions. Design/methodology/approach Within this study, the direct observatory methodology was used. This is a form of unstructured observation, whereby the complexity and context of a behavior or interaction are examined. Two government organizations that have integrated digitalization into their processes and services were investigated; these are the Federal High Court and the Nigeria Immigration Service all based in Abuja, Nigeria. Findings The primary factors responsible for the poor performance of the digital transformation of governance within the two organizations studied – include the adoption of a semi-automated system for the digital transformation of public governance, lack of a proper audit system (internal and external), inherent culture of corruption, in-addition to the individual and organization value systems that supports corruption, lack of adequate laws, policies and regulation to prevent, manage and punish corruption and the lack of publicity on the transformation of governance within these organizations, the poor usage of the system and inadequate digital public infrastructure. Research limitations/implications This research focused on investigating two public organizations, while there are several public organizations. The results of this study may or may not apply to other organizations, but it is impossible to know at this time. This is because of the nature of this study. This represents a limitation of this research. It is possible that few public organizations within the public sector are getting the benefits of digitalization and lessons can be derived from them to improve those that are not. On the other hand, the results obtained in this study may not apply to the private sector. This needs to be examined in future studies. Hence, future studies that should investigate the impact of digital transformation of governance in both the private and public sectors – using a mixed approach are encouraged. Originality/value This study posits itself as one of the earliest studies to deploy the direct observatory methodology in understanding digital transformation of governance in Nigeria. Within this study, clear loopholes were identified with verifiable empirical evidences. Unlike previous studies that reviewed literatures and used surveys, this study used an un-biased and real-life approach to gather evidence. This study bridged the gap between theory and practice of how digital transformation in governance works in Nigeria.

Recovery Resiliency Characteristics of Interdependent Critical Infrastructures in Disaster-Prone Areas

When Hurricane Maria struck the island of Puerto Rico in September, 2017, it devastated the island’s critical infrastructures, including the well-documented total loss of electric power systems. The strong interdependencies or associations among critical infrastructures in modern society meant that the failure of power systems propagated to and exacerbated the failure of other infrastructure systems. Moreover, these associations impact systems recovery just as they impact system failure. This study is a follow-up of previous research by the first author on Hurricane Maria. In this research authors extracted and quantified the recovery associations of Hurricane Fiona (September 2022) made landfall in Puerto Rico and inflicted considerable damage to its critical infrastructures. The recovery efforts following the disaster provided an opportunity to follow up on the previous research and examine the recovery associations. Significant money and efforts have gone into upgrading the infrastructures of Puerto Rico to make them more resilient to natural disasters such as hurricanes or tropical storms following Hurricane Maria. This paper explores the new recovery resiliency characteristics of Puerto Rico’s critical infrastructure systems (CISs) that the recovery efforts following Hurricane Fiona illustrate. This research shows that the power systems and other CISs of Puerto Rico are much more resilient when compared to their state of resiliency in 2017. Moreover, examining the recovery interdependencies reveals that some of the CISs are strongly dependent on power systems recovery. Outcomes of this study suggest that CIS relationships based on recovery data from Puerto Rico, are transferable to similar disaster-prone areas such as the Caribbean islands or other island nations, as they have similar characteristics and challenges.

Influence of building collapse on pluvial and fluvial flood inundation of metro stations in central Shanghai

Abstract. Urban flooding poses a significant threat to vulnerable underground infrastructure systems, such as metro stations. Building collapse induced by earthquakes alters urban building layout and coverage, consequently influencing flood inundation and propagation patterns. This study employs GPU-accelerated hydrodynamic simulation to investigate the mechanisms by which building collapse affects subsequent pluvial or fluvial flooding in the Huangpu District of Shanghai. Massive building collapse layouts are randomly generated, on which hydrodynamic simulations are performed and the inundation process of the metro stations is analyzed. The results reveal that pluvial floods are strongly influenced by localized topography distributed across the city. Consequently, building collapse has a more substantial impact on pluvial flooding when more buildings have collapsed. In contrast, fluvial floods are sensitive to the source location (e.g., location of levee breach) and the long travel route. Building collapse can either positively or negatively influence fluvial flooding by constricting or blocking the flow path. This work highlights the complex mechanism of earthquake–flood multi-hazard processes, emphasizing the importance of performing local-to-local analysis when both the hazard (e.g., individual building collapse, fluvial flood) and the hazard-bearing body (e.g., metro station) are localized. To better serve urban disaster prevention and mitigation, more efforts should be directed to developing physics-based high-resolution urban earthquake–flood simulation methods, as well as to acquiring data to drive such simulations.