Infrastructure Risk Research Articles

Risks for Public Health and Social Infrastructure in Russian Arctic under Climate Change and Permafrost Degradation

This study analyzes the risks to public health and life quality in the conditions of permafrost degradation caused by the ongoing climate change in the Russian Arctic. There are more than 200 Siberian anthrax cattle burial grounds in the Russian permafrost regions. Permafrost degradation poses the risks of thawing of frozen carcasses of the infected animals and propagation of infectious diseases. Permafrost degradation leads to infiltration of toxic waste in the environment. Such waste contains mercury, which migrates into the rivers and forms methylmercury (MeHg) in fish. Other risks associated with permafrost degradation include damage to the existing social infrastructure (housing, health-care facilities, roads, etc.). Various risks to public well-being that emerge because of permafrost degradation were addressed in this study. Relative hazard indices were developed and calculated to characterize the probability of outbreaks of Siberian anthrax in the future. These indices linked the rates of permafrost degradation and the number of Siberian anthrax cattle burials to the potential hazard of re-emergence of Siberian anthrax among local populations in 70 municipal districts under the ongoing warming. The expected damage to public housing, health-care facilities, and motorways was assessed. Accessibility of health care in various regions of the Russian Arctic was analyzed. The economic costs associated with various scenarios of possible destruction of residential buildings, health-care facilities, and roads built on permafrost were estimated.

Inflow Quantification in Urban Sewer Networks

The improper waterflow to wastewater treatment plants (WWTP) due to rainwater inflow, and infiltration is a growing concern due to the many problems it brings to the sector, ranging from infrastructure deterioration to environmental problems caused by untreated wastewater and to the eventual financial costs that these issues cause. The study was carried out at the Folhadela WWTP, Vila Real, Portugal, between May 2014 and May 2015, with the total effluent flows recorded every 2 min at the entrance of the WWTP. Rainfall data from the Vila Real Meteorological Station, corresponding to the same period, were used. The study allowed us to conclude that from the wastewater that flowed to the Folhadela WWTP, in the months of study, only 15% is domestic wastewater, and the remaining 85% were undesirable volumes. Of these, 47% were infiltration flows, and 38% were rainwater flows that are not taken into account when dimensioning networks and WWTPs. These flows also have the particularity of representing very high volumes in short periods of time, coinciding with heavy rains, representing a very high risk for drainage and treatment infrastructures. Regarding the infiltration flow rates, as a general rule, they are taken into account when dimensioning the networks as being a percentage of the total flow. However, it is necessary to take into account the magnitude and the evolution of these values according to the network age and state of conservation, as well as have straight regulations about the undue connections into the network.

Exploring the Impact of Climate Change on Arctic Shipping through the Lenses of Quadruple Bottom Line and Sustainable Development Goals

Climate change is everywhere, and the Arctic is no exception. The melting sea ice has caused renewed interest in expanding maritime shipping for potentially more accessible ocean routes. Canada emerges as a natural land bridge for trade between Asia, Europe, and the Americas. Plausibly, it is not a choice but an imperative to properly integrate the stakeholders (the environment, countries, remote communities, industrial partners) in opening the Arctic Circle to the global economy while considering the challenges. Keeping sustainability front and center and drawing on the extant literature and government policies, this interdisciplinary study offers a Canadian perspective on Arctic transportation routes over tribal lands and their quadruple bottom line (QBL) impacts on the environment, economy, society, and Indigenous cultures. Unlike the arguable premise that new transport corridors will increase trade traffic and enhance the economy in Northern Canada, the QBL approach enables a more holistic and realistic strategy for the Arctic region’s sustainable development regarding regional economies, rural logistics, supply chain efficiency, and social licensing. Drawing on an integrative literature review as methodology, we highlight the QBL framework and the United Nations Sustainable Development Goals as crucial policy tools. Such a holistic perspective helps stakeholders and decision makers frame better policies in identifying, assessing, adapting, and mitigating risks for transportation infrastructure exposed to climate change. We recap the impacts of Arctic Shipping (ArSh) on QBL pillars in an interaction matrix and emphasize that while ArSh may be complementary to economic development, it poses threats to the viability of the Indigenous cultures.

Physical Layer Components Security Risks in Optical Fiber Infrastructures.

Optical fiber communications are essential for all types of long- and short-distance transmissions. The aim of this paper is to analyze the previously presented security risks and, based on measurements, provide the risk level evaluation. The major risk is the possibility of inserting a splitter into the optical distribution network and capturing a portion of the entire spectrum, i.e., all channels in the optical fiber. Another significant security risk is crosstalk on multiplexers in networks with wavelength division multiplexing. The paper covers the macrobend attenuation evaluation of fiber and back-reflection measurements. Based on the measurements, risks were evaluated for both point-to-point and point-to-multipoint networks and, lastly, the paper covers crosstalk measurements of an optomechanical switch. Finally, all individual risks are evaluated according to the severity, and a proposal for risk minimization is provided.

Agricultural Supply Chain Risks Evaluation with Spherical Fuzzy Analytic Hierarchy Process

The outbreak of the COVID-19 pandemic has impacted the development of the global economy. As most developing and third world countries are heavily dependent on agriculture and agricultural imports, the agricultural supply chains (ASC) in all these countries are exposed to unprecedented risks following COVID-19. Therefore, it is vital to investigate the impact of risks and create resilient ASC organizations. In this study, critical risks associated with ASC were assessed using a novel Analytical Hierarchy Process based on spherical fuzzy sets (SF-AHP). The findings indicated that depending on the scope and scale of the organization, supply risks, demand risks, financial risks, logistics and infrastructure risks, management and operational risks, policy and regulatory risks, and biological and environmental risks all have a significant impact on ASC. This research highlighted that the most significant criterion is specified as Transportation (TP), followed by Market (MA) and Policy (PO), respectively. Meanwhile, Technology (TL) is the least significant criterion. The study’s findings can help managers with a holistic view of the agriculture supply chain risk mitigation. Furthermore, this study may assist managers in sharing information about the processing of agricultural products from top to bottom to manage risk in the supply chain.

Mitigating Risks in the Disaster Management Cycle

Purpose. The disaster management cycle (DMC) is often considered part of the essential efforts to handle disaster risk and consists of four key phases, namely preparedness, response, recovery, and mitigation. The purpose of this conceptual article is two‐fold. The first is to identify dominant risks, and the second is to propose risk mitigation strategies for these four phases in the DMC. Design/Methodology/Approach. The study uses primary and secondary data to identify the dominant risks in each DMC phase. The primary data sources include responses from an online questionnaire and transcripts from three semi‐structured interviews with stakeholders in the humanitarian supply chain. The secondary data sources include practitioner reports and archival data triangulation. Findings. The findings reveal five dominant risk factors in the DMC and classify them within the DMC phases, which are (1) demand risk, (2) supply risk, (3) operational risk, (4) infrastructure risk, and (5) disruption risk. The severity and frequency of each risk vary in each DMC phase. We found that several supply chain strategies (SCSs), such as raising risk awareness and horizontal and vertical collaboration and coordination among the key stakeholders in the DMC, can be essential risk mitigation strategies that apply across the four DMC phases. Research Limitations/Implications. The study highlighted dominant risks and the appropriate SCSs for mitigating the risk factors within each DMC phase. These findings are encapsulated in a conceptual framework for guiding risk prioritisation, decision‐making, and policy‐making. Our study has several limitations. First, although we followed a systematic process in computing the risk scores based on the likelihood of occurrences and impacts, the scores are nonetheless considered subjective perceptions of the respondents. Second, the number of respondents was limited. Broader coverage of respondents across geographical regions will provide further insights into the perspectives on the relevant risk factors in the DMC phases. This leaves possibilities for future research, comparison with other risk computation methods, and evaluation. Originality/Value. This study is one of the few that collected multiple data to extend the knowledge of risk identification and mitigation within the DMC.

Economic development promoted through public-private partnerships in the European Union and Colombia from a budgetary perspective

Without acknowledging that Public-Private Partnerships (PPPs) are a contracting model that allows economic development, it is necessary to investigate the budgetary measures adopted in the case of the European Union and Colombia to prevent its use as a tool to transgress the budgetary limits of public indebtedness. To this end, in this article a qualitative and analytical methodology will be used to: i) identify the importance of establishing and complying with budget limits for economic development, ii) expose the dilemma that exists between the satisfaction of public needs and the fulfillment with budget restrictions, iii) analyze PPPs as an alternative to solve the aforesaid problem and iv) point out the budgetary measures adopted by the European Union and Colombia that impact the PPP contracting model. According to the information gathered it will be shown that in Colombia, despite the existence of some budgetary limitations for PPPs developed by territorial entities, the same restrictions are not foreseen for national entities and it will be confirmed that in the European Union there is the possibility of including in debt service the PPPs when the transfer of risk and rewards of infrastructure relies on the public partner, while in Colombia this possibility has not been considered.

A methodology for the characterization of urban road safety through accident data analysis

As is known, road accidents essentially depend on four interrelated factors: human behaviour, vehicle efficiency, environmental conditions and the characteristics of the infrastructure. Although the vast majority of accidents is attributable to the first three factors, almost always attributable to improper user behaviour, it is of fundamental importance to try to reduce that part of the risk attributable to the infrastructure. In this research, the problem concerning the assessment of urban road safety in existing roads, in order to identify the dangerous sections on which to concentrate resources to make the functional adjustments deemed necessary, is addressed through the analysis of the accident rate found in operation. In particular, the research proposes a new model that characterizes the intrinsic risk of a urban road infrastructure through the assessment of accidents, disaggregated into different types, with a risk index that is a function of the two most significant variables that represent the accidents: the frequency with which they occur and the severity of the damages produced. The study also provides three aspects for achieving improved urban road safety. The first identifies the critical road sections (blackspots), through the application of the new model to the classic methods of accident assessment. The second defines the functional adjustments necessary to reduce the causes of accidents by comparing the risk of accidents, determined with the new methodology for each type of accident, and the technical characteristics of the road network in question. The third establishes the intervention priorities, based on an economic planning linked to the available budget, among the functional adjustments identified to reduce the risk of accident.

A Bayesian-Driven Monte Carlo Approach for Managing Construction Schedule Risks of Infrastructures Under Uncertainty

A Bayesian-Driven Monte Carlo Approach for Managing Construction Schedule Risks of Infrastructures Under Uncertainty

Zero Trust Architecture (ZTA): A Comprehensive Survey

We present a detailed survey of the Zero Trust (ZT) security paradigm which has a growing number of advocates in the critical infrastructure risk management space. The article employs a descriptive approach to present the fundamental tenets of ZT and provides a review of numerous potential options available for successful realization of this paradigm. We describe the role of authentication and access control in Zero Trust Architectures (ZTA) and present an in-depth discussion of state-of-the-art techniques for authentication and access control in different scenarios. Furthermore, we comprehensively discuss the conventional approaches to encryption, micro-segmentation, and security automation available for instantiating a ZTA. The article also details various challenges associated with contemporary authentication mechanisms, access control schemes, trust and risk computation techniques, micro-segmentation approaches, and Software-Defined Perimeter, that can impact the implementation of ZT in its true sense. Based upon our analysis, we finally pinpoint the potential future research directions for successful realization of ZT in critical infrastructures.

Understanding hazards: Probabilistic cyclone modelling for disaster risk to the Eastern Coast in Bangladesh

Cyclones and their associated wind and water hazards result in the largest number of fatalities. Coastal inundations due to cyclonic storm surges are an increasing threat to the lives and livelihoods of people in low-lying, highly populated coastal areas. Understanding hazards and the probability and intensity of of their occurrence are essential for determining the risk to life and infrastructure, as it allows the relevant agencies to plan for and respond to disasters. This paper developes a synthetic cyclone modelling (Category 4) to understand probable maximum impacts of a tropical cyclone and its cascading and compounding hazards in the Cox's Bazar area in Bangladesh. These models are used to develop contingency plans and strategies for disaster response. The probabilistic cyclone hazard modelling provides scenarios based hazards information that could benefit humanitarian agencies and future infrastructure risk assessments.

Critical Infrastructure Risk Assessment Based on Criticality of its Elements

Critical Infrastructure Risk Assessment Based on Criticality of its Elements

A Formal Model and Verification for HESTIA: An Automated, Adversary-Aware Risk Assessment Process for Cyber Infrastructure

Due to the characteristics and connectivity of today’s critical infrastructure systems, cyber-attacks on these systems are currently difficult to prevent in an efficient and sustainable manner. Prevention and mitigation strategies need accurate identification and evaluation of: system vulnerabilities, potential threats and attacks, and applicable hardening measures. Furthermore, the ability to prioritize hardening measures based on accurate assessments of risk is needed. In addition, the consideration of the availability, applicability, and cost of potential mitigation strategies is also needed. To address this challenge we created HESTIA: High-level and Extensible System for Training and Infrastructure risk Assessment. In this article we present a formal model of the HESTIA system. We then also present a formal verification of the HESTIA semantic model.

Tree Species Classification Using High-Resolution Satellite Imagery and Weakly Supervised Learning

Knowing vegetation type in an area is crucial for several applications, including ecology, land use management, and infrastructure risk assessment. In combination with recent advancements in image processing, remote sensing technology has been used to perform fast vegetation type estimation and reduce the need for intensive and time-consuming field-based surveys. This paper proposes a weakly supervised method based on deep learning to estimate tree species relying on multi-spectral high-resolution satellite images. We tested the approach against noisy labels, which often occur in real-world datasets. We validate our approach for a study area in Norway and in Italy using images taken in different periods of the year. Our method significantly enhances the quality of the available forestry inventory dataset.

Risk-Informed Decision-Making for Predisaster Risk Mitigation Planning of Interdependent Infrastructure Systems: Case Study of Jamaica

Hazards cause huge social disruptions and economic losses each year. Community disaster resilience relies on the capacity of critical infrastructure systems to maintain acceptable levels of functionality during a disruptive event and to recover promptly after the event. A proper predisaster risk mitigation planning framework can aid communities by working in advance to understand the preexisting local needs, identify the leaders, form partnerships, establish resources, and seize opportunities to increase community disaster resilience. Although numerous theoretical studies exist on developing models to support infrastructure predisaster risk mitigation, these models have not been properly incorporated into the decision-making process in existing studies. This lack of incorporation impedes the transfer of theoretical knowledge to practical policymaking. High-level predisaster infrastructure risk mitigation decisions for multiple infrastructure systems are especially in need of such an illustration due to the complexity introduced by the scale. This paper defines the interdependent infrastructure risk mitigation (IIRM) problem in a risk-informed decision-making framework and illustrates the entire decision-making process using a case study on predisaster risk mitigation planning of the interdependent critical infrastructure systems in Jamaica. This region is vulnerable to natural hazards but has received little attention in the literature.

Predicting road blockage due to building damage following earthquakes

Transportation infrastructure supports the social and economic activities of communities. One of the impacts of roads’ disruption is the obstruction of emergency services (e.g., ambulance, firefighting, evacuation). Furthermore, the recovery process of a community following an extreme event (e.g., a natural hazard) depends on the functionality of the transportation infrastructure. Therefore, conducting a risk and resilience analysis of transportation infrastructure is critical to help communities minimize the initial impact of hazards and promote a rapid recovery. Current approaches model the probability of road blockage due to building damage using high-resolution optical satellite images and aerial photographs collected after past events. However, the data used by these methods are limited, and few data have been collected before 2010. Besides, data may not be available for specific regions that have not experienced recent earthquakes. Thus, a probabilistic predictive method is needed for risk and resilience analysis of roads. This paper proposes a probabilistic model using the data from the 2010 Haiti Earthquake and calibrated by Bayesian approach to predict the debris distance from undamaged buildings (e.g., the distance debris can reach from the footprint of the undamaged building). The model is then used to construct fragility curves that give the conditional probability of road blockage at a given road section for a given seismic intensity. The proposed model considers the relevant factors affecting the road blockage probability, including building types, damage level, and road characteristics. The probability of road blockage at a given road section is estimated for the four general road section types, considering buildings on only one side of the road or both sides, and with or without a raised traffic median. The probability of road blockage for an entire road is then calculated by system and parallel reliability analysis. The proposed models apply to any general urban area without the dependence on historical data from past earthquakes.

Delivering blood components through drones: a lean approach to the blood supply chain

ABSTRACT This research explores how drones can improve the blood supply chain (BSC) in the delivery of blood components. This analysis is done considering the lean principles, and the aim is to understand challenges and wastes present in the BSC and map the drone advantages. Through a literature review and the use of in-depth interviews, data is collected from seven BSC experts. Main challenges were identified related to high wastage level, ability to predict demand for blood, low level of centralisation in developing countries, unexpected short-term changes in demand, and sanitary, environmental, and infrastructural risks. Wastes were identified mainly due to improper temperature during transportation, compromising blood bags’ safety, lack of equipment maintenance, leading to higher depreciation rates, and waiting time for the delivery of biomolecular testing results. The use of drones can represent gains in delivery time, allowing a ‘just-in-time’ model of distribution of blood, operating through a ‘pull’ system, reducing the need for inventories and costs.

Numerical Modelling Of Flood Induced Seepage Under Levees

Flood creates the most complex problems of engineering hydrology and extreme flood contains the crucial risk for urban areas, infrastructure, industry and agriculture. The aim of this paper is to study the transient flow caused by flood for levee of Filyos River. Numerical modeling based on finite element method was performed in the analyses. PlaxFlow which is an add-on module to Plaxis 2D, is used for the time variation of seepage in several points of interest within the levee. Exit velocity at several points of interest within the levee and degree of saturation of levee and hydraulic gradient were investigated. In addition, under seepage of water through different soil types underneath Filyos levee was examined. The results of transient flow analyses when piping occurred and sand boil formed were presented for different soil types.

Investigating the Factors Affecting Delays in Infrastructure Projects

Time and expense overruns are regular occurrences in the construction industry all around the world but these are a major concern in developing countries such as India, and their consequences can be extremely harmful when public infrastructure projects are concerned. In India as per the MoS & PI report, as of October 2019, there were 552 delayed construction projects, and the mean time overrun in these 552 delayed projects is29.07 months. Many studies in the literature review previously, have concentrated on a variety of infrastructure risk management issues but there have only been a few studies that have looked into the overall dynamics of infrastructure and how a project's timeline can be affected by changing risk interactions. This article aids in the investigation of the variables and causes of construction delays in infrastructure projects.

LOCATION OF SPEED CONTROL CAMERAS ON HIGHWAYS: A GEOSPATIAL ANALYSIS

This research proposes a methodology to identify critical sections of highways where the location of speeding control may be beneficial. The method relies on a spatial and statistical analysis of infrastructure risks, along with traffic accident frequency and severity. A relevant feature of this methodology is related to its potential to be used in areas where there are no detailed historical records about traffic crashes, which is common in Global South countries. We applied the methodology to a rural road network in Colombia, where a recent law established that technical criteria should support the location of speed cameras. The case study uses accident information from six years, and risk data from a road safety audit carried out in the area under study. Even though historical records of accidents in the area were not fully available, the methodology allowed prioritising speed camera installations in the zone and identifying the relevant variables to define camera location. The relevant variables were the geometric characteristics of the road, traffic flows, risk factors, and proximity to populated centres. The use of speed controls should be part of a road safety management system, which allows defining camera location according to robust technical criteria.