Future Infrastructure Research Articles

Validating Circular End-of-Life Strategies for Domestic Post-Consumer Materials in the Latin American Region: A Life Cycle Assessment Approach

This study examines the domestic solid waste management system in the LATAM region, using the city of Guayaquil in Ecuador as a case study. Through the life cycle assessment (LCA) methodology, the study compares domestic and external recycling processes, evaluating their effects on global warming potential, fossil resource scarcity, and terrestrial ecotoxicity. The results reveal that increasing recycling rates significantly reduces environmental impacts, with domestic recycling offering slightly higher environmental benefits than external options. A demographic analysis using machine learning techniques identifies distinct patterns of waste generation across different population clusters, highlighting the need for tailored waste management strategies. The study also emphasizes the importance of accurate local data and the integration of recycling initiatives with market realities, particularly in the light of policies mandating recycled content in products like PET bottles. A sensitivity analysis of the waste recovery indicator (WRI) demonstrates the potential for substantial environmental and economic benefits with higher recycling rates. The findings suggest that, to advance towards a circular economy, Latin American cities like Guayaquil must enhance their recycling infrastructure, refine waste management policies, and focus on demographic-specific strategies. This research contributes to the broader understanding of sustainable waste management in developing regions, offering insights for future policy and infrastructure development.

The Current State and Future of the Urban Cold Chain: A Review of Algorithms for Environmental Optimization

Cold chains are essential in providing people with food and medicine across the globe. As the global environmental crisis poses an existential threat to humanity and societies strive for more sustainable ways of life, these critically important systems need to adapt to the needs of a new era. As it is, the transportation sector as a whole accounts for a fifth of global emissions, with the cold chain being embedded in this old fossil-fuel-dependent infrastructure. With the EU is passing regulations and legislation to cut down on emissions and phase out polluting technologies like combustion engine vehicles, the next couple of decades in Europe will be defined by rapid infrastructural change. For logistics and cold transportation, this shift presents many opportunities but also highlights the need for innovation and new research. In this literature review, we identify pressing issues with the current urban cold chain, review the recent research around environmental optimization in urban logistics, and give a cross-section of the field: what the trending research topics in urban logistics optimization across the globe are, and what kind of blind spots are identifiable in the body of research, as well as changes arising with future green logistics infrastructure. We approach the issues discussed specifically from the point of view of refrigerated urban transportation, though many issues extend beyond it to transportation infrastructure at large.

Effectiveness of Generative AI for Post-Earthquake Damage Assessment

After an earthquake, rapid assessment of building damage is crucial for emergency response, reconstruction planning, and public safety. This study evaluates the performance of various Generative Artificial Intelligence (GAI) models in analyzing post-earthquake images to classify structural damage according to the EMS-98 scale, ranging from minor damage to total destruction. Correct classification rates for masonry buildings varied from 28.6% to 64.3%, with mean damage grade errors between 0.50 and 0.79, while for reinforced concrete buildings, rates ranged from 37.5% to 75.0%, with errors between 0.50 and 0.88. Fine-tuning these models could substantially improve accuracy. The practical implications are significant: integrating accurate GAI models into disaster response protocols can drastically reduce the time and resources required for damage assessment compared to traditional methods. This acceleration enables emergency services to make faster, data-driven decisions, optimize resource allocation, and potentially save lives. Furthermore, the widespread adoption of GAI models can enhance resilience planning by providing valuable data for future infrastructure improvements. The results of this work demonstrate the promise of GAI models for rapid, automated, and precise damage evaluation, underscoring their potential as invaluable tools for engineers, policymakers, and emergency responders in post-earthquake scenarios.

Dynamic Wireless Charging of Electric Vehicles Using PV Units in Highways

Transitioning from petrol or gas vehicles to electric vehicles (EVs) poses significant challenges in reducing emissions, lowering operational costs, and improving energy storage. Wireless charging EVs offer promising solutions to wired charging limitations such as restricted travel range and lengthy charging times. This paper presents a comprehensive approach to address the challenges of wireless power transfer (WPT) for EVs by optimizing coupling frequency and coil design to enhance efficiency while minimizing electromagnetic interference (EMI) and heat generation. A novel coil design and adaptive hardware are proposed to improve power transfer efficiency (PTE) by defining the optimal magnetic resonant coupling WPT and mitigating coil misalignment, which is considered a significant barrier to the widespread adoption of WPT for EVs. A new methodology for designing and arranging roadside lanes and facilities for dynamic wireless charging (DWC) of EVs is introduced. This includes the optimization of transmitter coils (TCs), receiving coils (RCs), compensation circuits, and high-frequency inverters/converters using the partial differential equation toolbox (pdetool). The integration of wireless charging systems with smart grid technology is explored to enhance energy distribution and reduce peak load issues. The paper proposes a DWC system with multiple segmented transmitters integrated with adaptive renewable photovoltaic (PV) units and a battery system using the utility main grid as a backup. The design process includes the determination of the required PV array capacity, station battery sizing, and inverters/converters to ensure maximum power point tracking (MPPT). To validate the proposed system, it was tested in two scenarios: charging a single EV at different speeds and simultaneously charging two EVs over a 1 km stretch with a 50 kW system, achieving a total range of 500 km. Experimental validation was performed through real-time simulation and hardware tests using an OPAL-RT platform, demonstrating a power transfer efficiency of 90.7%, thus confirming the scalability and feasibility of the system for future EV infrastructure.

INTEGRATING LAND USE ANALYSIS WITH WATER DEMAND ESTIMATION: A CASE STUDY OF PUTRAJAYA, MALAYSIA

The challenges posed by population growth, urbanization, and changing land use patterns on sustainable water resource management are significant. This paper puts forth an integrated framework aimed at assessing future water demand in Putrajaya, Malaysia. The proposed framework combines population projections, estimations of water demand, and analyses of land use activities. Through an examination of demographic trends and land use patterns, the framework predicts population growth and identifies areas with high water demand. Daily water use patterns in homes and businesses (temporal analysis) inform the designing future water infrastructure, incorporating temporal aspects. Statistical and spatial analysis techniques are then utilized to merge these projections with water demand estimations to quantify water requirements in various zones and types of land use. This study has unveiled two daily peaks in water demand, which align with household schedules. Residential areas emerge as the primary consumers of water, displaying an evening peak distinct from the midday peak seen in businesses. The current water demand in Putrajaya is estimated at 94 million litres per day, with domestic usage surpassing non-domestic usage in a ratio of 3:2. Projections based on future land use plans foresee a 19% increase in demand, underscoring the urgency for proactive water management strategies. Spatial analysis has highlighted residential areas as the main users of water, with demand levels varying throughout the city. By comprehending these temporal and spatial patterns, water authorities can strategically target interventions, optimize infrastructure siting, and forecast future demand trends. These proactive measures are essential for securing a sustainable water future for Putrajaya.

Presenting the behavioral model of citizens in selection of trip vehicle with emphasis on how to go to work

Understanding the factors that influence individual vehicle choice behavior will reveal changes in preferences and attitudes. These changes can provide insights into travel patterns, improve transportation planning, prepare for future infrastructure needs and services, and inform better design and implementation of sustainable and inclusive transportation. The purpose of this research is to provide a behavioral model of citizens in choosing a travel vehicle with an emphasis on how to go to work in Shiraz, Iran. The statistical method used in this research is the logit mathematical model. SPSS software was used to analyze the research results. Different hypothetical scenarios were investigated according to the final vehicle selection model and the possibility of choosing different travel vehicles in each of the hypothetical scenarios. The results of the comparison of logit models for all travel destinations showed that the behavior of Shiraz citizens in choosing a means of transportation is not nested. For this reason, the final model was chosen for business trips. The results obtained from the hypothetical scenarios of the business travel selection tool in Shiraz showed that if none of the demand management policies and the creation of high-speed buses are applied, the share of private cars is 38%, the share of buses and subways is 32%, the share of taxi travel is 30%, and the share of walking will be 33 %. Also, social conditions and demographic characteristics do not have a significant effect on the choice of vehicle and travel behavior of citizens to the workplace.

Public History: Quantum Computing as Imagined Infrastructure.

This review essay examines an art exhibition that envisions the future infrastructure of quantum computing and the sociopolitical and aesthetic visions it might bring about. Visitors are encouraged to reflect on past concepts of computing and the transformative power of technology. The exhibit tackles the challenge that computer-based infrastructures-the infrastructure of infrastructures-poses for the historiography of computing. This review places the exhibit's perspective of quantum computing infrastructures within the broader context of computing and information history.

Public History: Infrastructural Imaginaries and the Production of Affective Power.

This section of Public History explores "infrastructural imaginaries"-shared visions of future infrastructure that are collectively held and either publicly enacted or resisted. These imaginaries are deeply rooted in both present realities and past dreams of what could have been. For historians of technology, understanding who imagines this infrastructure, the impacts of these imaginings, and the emotional forces driving these processes is essential. The contributions in this section explore artistic interventions that blend utopian ideas, visual languages, political ambitions, and radical actions. These interventions challenge us to rethink the contours of our infrastructure-dominated world and invite further historical analysis in these public discourses.

Public value in the making of automated and datafied welfare futures

Across Europe, the public sector is expanding its efforts to introduce data-driven decision-support and intelligent systems in the administration of welfare. The growing body of research that highlights the impact and harms these systems have on citizens’ lives, raises questions about the guiding ideas, values, and norms that are at the heart of current transformations in welfare. Research in critical data studies contends a silent marketisation of social protection and governance. It implies an intricate entanglement between the procedures and output of service provision on the one hand and the normative outcomes in terms of the well-being and flourishing of society as a whole on the other hand. Thinking with the capabilities approach and buen vivir as well as with the concept of data justice, our paper asks how to conceptualise this relation. It discusses how (in)justice may become a sociomaterial component in automated welfare and emphasises that welfare outcomes and welfare practices, including their procedural aspects, are rather co-produced than just interdependent. We conclude that whether future infrastructures of welfare will create public value or not, depends on the institutional arrangements, policies, and norms built to distribute the emerging benefits and risks equally in society.

MODERN TRENDS AND INNOVATIONS IN THE MODERNIZATION OF RAILWAY LINES BASED ON EXPERIENCE IN THE REPUBLIC OF BULGARIA

The modernization of the Bulgarian railways has been underway for over 20 years. Hundreds of kilometres have been rehabilitated with funds from the European Union. The aim is to join the main Trans-European network, according to Regulation No. 1315/2013 [1] of the EU. Given the complex mountainous relief, the specific engineering objects are now the design and construction of a large number of tunnels (in the Sofia-Plovdiv section alone, over 16 tunnels are planned, the longest of which is 6 km long); the design and construction of railway bridges, some of which have unique dimensions; the rehabilitation of bridges; and the construction of drainage facilities, including drains, ditches, and culverts (some of the culverts have a unique length of about 80 m). The report analyses the main railway projects with a critical analysis of strengths and weaknesses, according to the EU cost-benefit analysis methodology. The analysis presented in this report is unique for Bulgarian railways, as it includes a large volume of projects in the field of designing and building railway infrastructure according to European and world rules, which have not been used in Bulgaria until now. This is only the first stage of scientific research, which involves collecting data, examining the data, and building a working hypothesis. After the completion of the construction (supposedly by around 2029), there will be measurements of the railway and a diagnosis of its condition. At this time, data will be collected again, and the credibility of the presented hypotheses will be verified. The conclusions will serve for the more adequate preparation of the technical assignments for future infrastructure projects in the field of railways.

Multi-hazard susceptibility mapping of landslides and earthquakes in Bhagirathi Valley region of Uttarakhand Himalaya, India

ABSTRACT This study addresses impact of increasing frequency of natural hazards, particularly in mountainous regions like the Himalayas. The research develops a comprehensive multi-hazard susceptibility mapping methodology. . The integrated methodology involving majorly machine learning, remote sensing and GIS tools is applied to generate the multi-hazard susceptibility map of the test site. This integrated approach offers a nuanced understanding of hazard interactions, providing a robust framework for disaster risk reduction, government and non-government agencies in planning future developmental projects and infrastructures in the study area. The study’s findings show that 11.30% of the area is labeled as highly susceptible to multi-hazards.

Characteristics of air temperature on the southern and southeastern slopes of the Greater Caucasus in the modern period

In the paper, the characteristics of air temperature in the south part of the Greater Caucasus region and the effects of regional climate changes on this area in the modern period were studied. In the research, the air temperature observation data of 8 hydrometeorological stations operating in the region for the years 1961-2022 were used, and the modern classification of the average monthly, seasonal, annual and altitude time-space distributions of temperature was given.Based on the data of hydrometeorological stations in the south and southeast piedmonts of the Greater Caucasus Mountains, an electronic map was prepared for the distribution of average annual air temperature indicators on the territory, and for determining the indicators of areas without data. DEM (Digital Elevation Model) files obtained fromthe decoding of satellite images (satellite data) were used in the prepared map. The research shows that July and August are the hottest months when the temperature is 22.80C, while January is the coldest by temperature about 0.50C in this region. In this part of the Greater Caucasus province, in the cold months of the year, the average monthly temperature is indicating below 00C in the area located above the moderately mountainous part of the region. The dynamics of the average indicators of temperature of the multi-year period (1961-2022) are expressed by a positive trend. The average annual temperature distribution map of the territory shows that the air temperature is 15.00C in the low mountainous part of the region, while it decreases upwards and, according to the adiabatic law, equals 00C at altitudes above 3500 m. According to the climate studies conducted in the region, compared to the years 1981-2010, it was found that there was a temperature increase of 1.20C in the mountainous region in 2011-2022. Also, a comparative analysis of 10-year averages of air temperature trends, using the Holt- Winters model, shows that temperatures in 2100 are expected to increase by 3.00C compared to 2030. In the region, climate changes that worsen every year lead to water scarcity, desertification, drought, etc. and can expand the area of recurrence of environmental crises and destructive natural events. This research can be used in future climate change mitigation , climate atlases, infrastructure, agricultural and industrial projects in the region.

Enhanced Rock Weathering for Carbon Removal-Monitoring and Mitigating Potential Environmental Impacts on Agricultural Land.

Terrestrial enhanced rock weathering (ERW) is the application of pulverized silicate rock to soils for the purposes of carbon removal and improved soil health. Although a geochemical modeling framework for ERW in soils is emerging, there is a scarcity of experimental and field trial data exploring potential environmental impacts, risks, and monitoring strategies associated with this practice. This paper identifies potential negative consequences and positive cobenefits of ERW scale-up and suggests mitigation and monitoring strategies. To do so, we examined literature on not only ERW but also industry, agriculture, ecosystem science, water chemistry, and human health. From this work, we develop recommendations for future research, infrastructure, and policy needs. We also recommend target metrics, risk mitigation strategies, and best practices for monitoring that will permit early detection and prevention of negative environmental impacts.

LiDAR and CAD Reconstruction of Queen's University Ontario Hall

The emergence of highly accurate LiDAR (Light Detection and Ranging) technology has opened new avenues for engaging with and understanding cultural heritage sites. How do we convert buildings like Ontario Hall into editable 3D models to engage various stakeholders in a campus setting? This initiative developed from the goal of MobArch (Queen's Mobile Laboratory for the study of Architecture and the Built Environment) to use non-invasive visualization technologies to understand the social, developmental, and environmental impacts upon our built environment and architectural past. Current initiatives involving LiDAR for heritage conservation investigate hidden aspects of sites or address structural disrepair. The Ontario Hall project uniquely documents the state of a heritage building before structural concerns arise. This project creates an accurate digital record of Ontario Hall for future infrastructure initiatives and cultural preservation. In addition, this project tests the limits of LiDAR with 3D reconstruction in CAD (Computer Aided Design) software. This project used the Leica RTC360 3D Laser Scanner to create over 300 scans with a point cloud density of 6mm. The resulting cloud was optimized using Leica Cyclone Register 360 Plus software and was imported to Sketch Up where a 3D model was manually reconstructed based on the cloud data. The result of this project is a SKP file containing the 3D model, and the point cloud. The cloud is a highly accurate resource for future infrastructure projects. The 3D model contains room volumes, windows, staircases, and external architectural elements in a more edit-friendly software environment allowing diverse stakeholders to creatively reimagine Ontario Hall. The model can be integrated into archives, websites, games, and VR. Heritage architecture can contribute to the marketing appeal and visual identity of Queen’s. Moreover, awareness of community heritage improves social wellbeing and addresses the need to connect with our built history.

An overview of the future smart charging infrastructure for electric vehicles

An overview of the future smart charging infrastructure for electric vehicles

Real-Time Surveillance with AI: A Comprehensive Approach to Security and Monitoring

The last ten years have been quite phenomenal for artificial intelligence and computer vision. For the past few years, it was unstoppable, with outstanding breakthroughs that moved towards advanced real-time surveillance systems-that wholesome approach to real-time surveillance through the power of AI in technological advancement for accuracy, scalability, and responsiveness. The system integrates facial recognition, object detection, and anomaly detection algorithms to give a very stable, adaptive response to the various facets of surveillance needs, such as public safety, institutional monitoring, and private security. Through deep learning models extracted from comprehensive datasets, the system is able to identify known individuals in real time and trace the movements and unusual behaviors of these people. A discussion of challenges in data privacy, computational efficiency, and false positives goes hand in hand with an in-depth analysis of system architecture, which includes thorough hardware and software configuration descriptions. The experimental results show the potential of the system in real-world environments as an alternate scalable solution for future security infrastructures.

Financial risk analysis of Public-Private Partnership (PPP) projects case study on Pandaan - Malang Toll Road

This study aims to analyze the financial risks faced by the Pandaan-Malang Toll Road PPP (Public-Private Partnership) project in Indonesia. Data collection techniques include semi-structured in-depth interviews with parties related to the PPP project, including the Directorate General of Infrastructure Financing, the Tolling Road Management Agency (government), and Business Entities (PT Jasamarga Pandaan Malang). The collected data will be analyzed to determine the financial risk factors faced by PPP projects and the financial controls carried out to overcome financial risks in PPP. The results show that the risk of errors in forecasting in the calculation of demand, cost, and revenue projections is a risk that almost often occurs. The Government emphasizes the importance of optimizing the role of PII (Persero) or Indonesia Infrastructure Guarantee Fund (IIGF) as an Infrastructure Guarantee Business Entity (BUPI) to ensure financial sustainability and minimize the impact of sudden shocks on the State Budget due to the provision of Government Guarantees for cooperation projects. This study provides insights or frameworks that can be applied to similar projects, helping to improve risk assessment and decision-making processes in future infrastructure developments.

Spatiotemporal Changes in Air Pollution within the Studied Road Segment

Environmental protection is a pivotal element of sustainable development, both essential and indispensable in the construction of smart, green cities. Road transport contributes significantly to atmospheric pollution, accounting for as much as 25% of annual emissions within the European Union (EU). To combat the adverse effects of road transport, the EU has set targets to reduce greenhouse gas emissions from both passenger and commercial vehicles. Consequently, sustainable air pollution management has become a focal point for numerous researchers. This study continues the investigation into the distribution of air pollutants along a specific highway segment in Poland. The article addresses two primary research questions: first, the temporal and spatial variations in air pollution adjacent to a major highway in Poland, and second, whether emission levels exhibit significant annual differences and if there is a correlation between pollutant concentrations and the distance from the roadway. The findings offer valuable data on one of the principal substances polluting the air along EU transportation routes. Moreover, the analysis provides recommendations for future road infrastructure renovation projects and strategies to protect the public from harmful traffic-related pollutants, thereby supporting the development of green cities in accordance with sustainable development principles.

Runoff Control Performance of Three Typical Low-Impact Development Facilities: A Case Study of a Community in Beijing

The development of sponge cities advocates for sustainable urban rainwater management, effectively alleviating urban flood disasters, reducing non-point-source pollution, and promoting the recycling of rainwater resources. Low-Impact Development (LID) serves as a key strategy in this context, providing essential support for urban rainwater control and pollution reduction. To investigate the runoff control effects of LID measures and to reveal the relationship between facility runoff control performance and installation scale, this study focuses on a sponge community in Beijing. A SWMM model was constructed to analyze the rainwater flood control and pollutant load reduction effects of different LID facilities, including bio-retention cells, green roofs, and permeable pavements. Using evaluation indicators such as surface runoff, node overflow, and pollutant control rates, this study examined how facility performance varies with installation scale under different rainfall conditions. The combination scheme of LID equipment optimal configuration is designed by using multiple criteria decision analysis (MCDA) and cost–benefit theory. The results indicate significant differences in performance among the various LID facilities across different rainfall scenarios. Specifically, the optimal installation proportion for runoff and overflow control of permeable pavements were found to be between 30% and 70%. Green roofs demonstrate superior performance in handling extreme rainfall events, while bio-retention cells exhibit significant effectiveness in controlling Total Suspended Solids (TSSs). Through comprehensive performance evaluation, this study identified the optimal combination scale under a 3-year rainfall recurrence interval as 30% permeable pavements, 20% green roof, and 60% bio-retention cells. This combination effectively leverages the strengths of each facility, ensuring system stability and efficiency while also demonstrating optimal management efficiency in cost–benefit analyses. The findings of this research provide valuable insights for future urban water management and infrastructure development.

Enhancing Firewall Packet Classification through Artificial Neural Networks and Synthetic Minority Over-Sampling Technique: An Innovative Approach with Evaluative Comparison

Firewall packet classification is a critical component of network security, demanding precise and reliable methods to ensure optimal functionality. This study introduces an advanced approach that combines Artificial Neural Networks (ANNs) with various data balancing techniques, including the Synthetic Minority Over-sampling Technique (SMOTE), ADASYN, and BorderlineSMOTE, to enhance the classification of firewall packets into four distinct classes: ‘allow’, ‘deny’, ‘drop’, and ‘reset-both’. Initial experiments without data balancing revealed that while the ANN model achieved perfect precision, recall, and F1-Scores for the ‘allow’, ‘deny’, and ‘drop’ classes, it struggled to accurately classify the ‘reset-both’ class. To address this, we applied SMOTE, ADASYN, and BorderlineSMOTE to mitigate class imbalance, which led to significant improvements in overall classification performance. Among the techniques, the ANN combined with BorderlineSMOTE demonstrated superior efficacy, achieving a 97% overall accuracy and consistently high performance across all classes, particularly in the accurate classification of minority classes. In contrast, while SMOTE and ADASYN also improved the model’s performance, the results with BorderlineSMOTE were notably more balanced and reliable. This study provides a comparative analysis with existing machine learning models, highlighting the effectiveness of the proposed approach in firewall packet classification. The synthesized results validate the potential of integrating ANNs with advanced data balancing techniques to enhance the robustness and reliability of network security systems. The findings underscore the importance of addressing class imbalance in machine learning models, particularly in security-critical applications, and offer valuable insights for the design and improvement of future network security infrastructures.