Urban Infrastructure Management Research Articles

SUSTAINABLE URBAN TRANSFORMATION:

The integration of electric mobility with Smart Grids represents a transformative strategy for the development of sustainable urban systems, characterized by a reduced carbon footprint and high energy efficiency. This article carries out a systematic review with a bibliometric approach on the intersection between these two concepts, exploring not only the operational and environmental advantages, but also the social and economic implications of this synergy in building smart cities. The results indicate that the combination of electric mobility and Smart Grids is a rapidly growing area of study, with a significant increase in publications in high-impact scientific journals. Authors from globally renowned institutions have contributed to the consolidation of this field of research, reinforcing its relevance in the context of sustainable urban policies. Furthermore, an analysis of the literature reveals that Smart Grids, by incorporating new communication and information technologies, not only enable the large-scale adoption of electric mobility, but also promote the integrated and efficient management of urban infrastructure. This interconnectivity between systems allows for better optimization of the use of resources, as well as supporting the transition to more sustainable and resilient urban mobility.

Improving the accuracy of weighing systems in motion using dynamic neural networks

The paper is devoted to solving the problem of weighing vehicles in motion as part of modern information technologies and automated intelligent systems for managing urban resources and infrastructure. The aim of the work is to improve the accuracy of measurements in systems for weighing vehicles in motion in heavy traffic as part of intelligent urban infrastructure management systems, thereby contributing to the efficiency and sustainability of urban processes. The scientific novelty is the use of models in the form of neural networks with time delays to process data from weighing sensors. The application of this approach makes it possible to improve the accuracy of mass measurement in weighing systems in motion under conditions of intense traffic by taking into account the dynamic and nonlinear properties of the weighing process. The practical usefulness of the developed method lies in the development of new innovative weighing systems as part of modern information technologies and automated intelligent systems for managing urban resources and infrastructure. Testing the method on a simulation model of the WIM process demonstrated advantages in weighing accuracy compared to traditional methods using static and linear models. Experimental data showed a reduction in the root mean square error compared to the traditional method based on Kalman filters, which confirms the effectiveness of the proposed approach.

Multimodal Large Language Models as Built Environment Auditing Tools

This research showcases the transformative potential of large language models (LLMs) for built environment auditing from street-view images. By empirically testing the performances of two multimodal LLMs, ChatGPT and Gemini, we confirmed that LLM-based audits strongly agree with virtual audits processed by a conventional deep learning-based method (DeepLabv3+), which has been widely adopted by existing studies on urban visual analytics. Unlike conventional field or virtual audits that require labor-intensive manual inspection or technical expertise to run computer vision algorithms, our results show that LLMs can offer an intuitive tool despite the user’s level of technical proficiency. This would allow a broader range of policy and planning stakeholders to employ LLM-based built environment auditing instruments for smart urban infrastructure management.

Assessment of Land Subsidence Pattern in Pokhara Valley Using Sentinel-1 InSAR Processing

This research examines the use of Sentinel-1's enhanced remote method and Interferometric Synthetic Aperture Radar (InSAR) processing to monitor ground subsidence in Pokhara Valley City, Nepal. It aims to comprehend the reciprocal effects of land sinking on environmental sustainability, infrastructure stability, and urban growth. The methodology encompasses the acquisition and processing of Sentinel-1 SAR data, along with rigorous interferometric analysis to detect and quantify ground deformation. Spanning from 2019 to 2022 AD and, -analyzing annual displacement trends, initially, a subsiding trend was observed in 2019-20 AD with an average rate of 0.037 m/year followed by 0.072 m/year in 2020-21 AD, and then escalating to 0.165 m/year in 2021-22 AD. These fluctuations in rates illustrate the intricate interplay of urbanization and geological dynamics affecting land stability in the region. The implications of this study extend to urban planning and infrastructure management, emphasizing the necessity of integrating land subsidence monitoring into the developmental policies and strategies of Pokhara Valley. Additionally, the potential of Sentinel-1 InSAR as a valuable tool for geotechnical investigations offers a non-invasive, cost-effective means to assess and monitor ground deformation risks. By leveraging the capabilities of Sentinel-1 InSAR, the study contributes to the body of knowledge in geotechnical and civil engineering fields, particularly within the context of sustainable urban planning and disaster risk reduction.

Probabilistic Seismic Risk Assessment of Metro Tunnels in Soft Soils

Tunnels are of significant importance in the sustainable development of global urban areas, particularly in metropolitan areas. It is of the utmost importance to evaluate the seismic performance of tunnels across a wide spectrum of earthquake intensities. In order to address this, our study presents a framework for the assessment of seismic risk in tunnels. This study employs the city of Shanghai’s urban metro tunnels as case studies. The nominal values of seismic risk for the three main damage states—minor, moderate, and major—were calculated. Furthermore, the influence of utilizing disparate fragility functions on expected seismic risk assessments was investigated. In this framework, the probability density functions of the different fragility curve models are employed to treat the probability values associated with them as random variables. This approach aims to facilitate the propagation of IMV in seismic risk assessments. The results demonstrate that the Bayesian framework efficiently incorporates the full range of input model variability into risk estimation. The findings of this study offer a foundation for decision-making processes, seismic risk assessments, and the resilience management of urban infrastructure.

Contextual and socio-economic factors affected urban dwellers experiences of and vulnerability to ecosystem disservices

Peoples’ experiences of Ecosystem Disservices (EDS) are influenced by socio-economic and environmental characteristics, and this needs better understanding for informed planning and management of urban green infrastructure. The paper investigated the understudied area of how socio-economic and environmental factors influence people's experiences of EDS in urban green spaces, in four towns in South Africa. Employing a questionnaire survey, in 303 randomly selected households in three neighborhoods of different economic standing, alongside statistical analysis provided quantitative data on experiences and vulnerability to EDS. A list of reported EDS was compiled and the number of EDS/respondent was compared between towns, neighborhoods and places of experience, using Kruskal-Wallis test and nested ANOVAs. Respondents’ vulnerability to EDS was assessed by analyzing the magnitudes of experience and coping abilities. The influences of socio-economic and environmental characteristics on experiences of and vulnerability to EDS were assessed using PCAs. Twenty-three EDS were reported in total, with the highest numbers recorded in affluent areas and the lowest in the poorest areas. Irrespective of town and neighborhood, the reported EDS were mainly experienced in domestic gardens and surrounding streets. Consequently, only one EDS seems of high concern, while the others are of lower concern or well manageable by the respondents. Significant influences of socio-economic (education and income levels, source of income, upbringing, frequency of visits to green spaces (GS), length of residence in neighborhoods) and environmental (environmental worldview, garden ownership, membership to environmental NGO) characteristics were observed on the experiences and vulnerability to EDS. Therefore, such variations in and influences on peoples’ experiences and vulnerability to EDS should be considered for sustainable management of urban GS. Also, the creation of GS should be accompanied by actions to improve people relationships to nature as well as to ensure their maintenance and security, particularly public GS and commonages.

A Comparative Study to Explore the Variability of Heavy Metal Concentration in the Teeth of Residents of Non-gated Versus Gated Societies.

Human populations face increasing exposure to heavy metals, which pose significant health risks. Dental tissues, such as dentin and enamel, provide a reliable biomarker for assessing long-term heavy metal exposure due to their stable nature. This study aims to explore the variability of heavy metal concentrations in the teeth dentine of residents living in gated versus non-gated societies near the Yamuna Riveraround the Jamia Millia Islamia, New Delhi, India. Sixty-nine participants were enrolled, 27 from gated societies and 42 from non-gated societies. Participants underwent elective dental extraction,following whichheavy metal concentrations were measured in extracted tooth dentine using atomic absorption spectrophotometry. Demographic data including age, gender, tooth types, and drinking water sources were recorded. Statistical analysis included Mann-Whitney's test, Spearman correlation heatmap, and principal component analysis (PCA). Residents of non-gated societies exhibited significantly (p< 0.005) higher mean concentrations of heavy metals compared to gated societies except for cadmium (p = 0.495). Subgroup analysis based on drinking water sources revealed significant (p<0.001) variations in heavy metal concentrations, suggesting the influence of water quality on environmental exposure. PCA provided insights into underlying trends and correlations among heavy metal variables. This study provides valuable insights into heavy metal contamination among residents living near the Yamuna River, highlighting disparities in exposure based on residential environment and drinking water sources. Participants from non-gated societies exhibited higher mean concentrations of heavy metals compared to those from gated societies, emphasizing the influence of socio-economic factors, urban infrastructure and environmental management practices on heavy metal accumulation. The study's findings underscore the need for targeted interventions to address heavy metal exposure across diverse population groups, improve water quality standards, and enhance access to safe drinking water.

The Necessity of Updating IDF Curves for the Sharjah Emirate, UAE: A Comparative Analysis of 2020 IDF Values in Light of Recent Urban Flooding (April 2024)

In the arid Arabian Peninsula, particularly within the United Arab Emirates (UAE), the perception of rainfall has shifted from a natural blessing to a significant challenge for infrastructure and community resilience. The unprecedented storm on 17 April 2024, exposed critical vulnerabilities in the UAE’s urban infrastructure and flood management practices, revealing substantial gaps in handling accumulated precipitation. This study addresses the necessity of updating the Intensity–Duration–Frequency (IDF) curves for the Sharjah Emirate by utilizing recent precipitation data from 2021 to April 2024, alongside previously published 2020 data. By recalibrating the IDF curves based on data from three meteorological stations, this study reveals a substantial increase in rainfall intensities across all durations and return periods. Rainfall intensities increased by an average of 36.76% in Sharjah, 26.52% in Al Dhaid, and 17.55% in Mleiha. These increases indicate a trend towards more severe and frequent rainfall events, emphasizing the urgent need to revise hydrological models and infrastructure designs to enhance flood resilience. This study contributes valuable insights for policymakers, urban planners, and disaster management authorities in the UAE and similar regions worldwide.

Enhancing explainability of deep learning models for point cloud analysis: a focus on semantic segmentation

ABSTRACT Semantic segmentation of point clouds plays a critical role in various applications, such as urban planning, infrastructure management, environmental analyses and autonomous navigation. Understanding the behaviour of deep neural networks (DNNs) in analysing point cloud data is essential for improving segmentation accuracy and developing effective network architectures and acquisition strategies. In this paper, we investigate the traits of some state-of-the-art neural networks using indoor and urban outdoor point cloud datasets. We compare PointNet, DGCNN, and BAAF-Net on specifically selected datasets, including synthetic and real-world environments. The chosen datasets are S3DIS, SynthCity, Semantic3D, and KITTI. We analyse the impact of different factors such as dataset type (synthetic vs. real), scene type (indoor vs. outdoor), and acquisition system (static vs. mobile sensors). Through detailed analyses and comparisons, we provide insights into the strengths and limitations not only of different network architectures in handling urban point clouds but also of their data structure. This study contributes to going beyond the mere and unconditional use of AI algorithms, trying to explain DNNs behaviour in point cloud analysis and paving the way for future research to enhance segmentation accuracy and develop possible guidelines both for network design and data acquisition in the geomatics field.

Webbing 3D map from UAV images in Da Lat City, Lam Dong Province

Abstract Worldwide, big cities are accelerating to fulfill the establishment of Spatial Data Infrastructure (SDI), which is a key task towards smart city. The mainstay of SDI is to create an important foundation of data capacity to provide necessary and essential information related to the requirements of urban and smart city management. With the advantages of high accuracy, low cost, and flexibility in 3D data creation, UAV (Unmanned Aerial Vehicle) images have been widely applied for the purpose of digital transformation purposes. This article aims to firstly utilize the new opportunities of using UAVs in 3D mapping and sharing 3D map data through the WebGIS platform. Secondly, processing techniques will be described after collecting the UAV images to create orthomosaics, point clouds, and Digital Surface Model (DSM). A classification of ground points was conducted to create a Digital Terrain Model (DTM) in combining with DSM to form a calibrated surface numerical model (Canopy Height Model, CHM). The images map was then digitalized coupled with the object height extracted from the CHM model and assigning attributable data to each object. The results suggested that ArcGIS Online allows users to share information widely, targeting multiple users, and to reduce the cost of GIS software investment in building thematic classes for urban infrastructure management.

A GIS-Based emergency response and management support framework for Earthquake crisis: A case study of Antakya and Kahramanmaras earthquake in Turkey

Earthquakes can cause significant damage, including loss of lives, collapsed buildings, and blocked roads. However, the severity and frequency of earthquakes cannot be accurately predicted. To mitigate these effects, successful disaster management, particularly in improving post-earthquake road network connectivity, is crucial. Therefore, this study presents a novel satellite imagery-based approach for identifying post-earthquake road blockage and to assess its impact on urban infrastructure and emergency response management. This study utilised real-world pre- and post-earthquake satellite images of the earthquake that struck Turkey on February 6, 2023 using ArcGIS Pro. The results demonstrated that in the scenario when people in affected areas (incidents) tried to escape to emergency gathering zones, out of the initial 126 incident locations in Antakya, 101 of them experienced alterations in the selected routes after the earthquake. In Kahramanmaras, out of 35 incidents, 28 experienced changes in the selected routes. In the case of emergency vehicles (firefighting, medical aid, etc.) traveling to affected areas, some emergency vehicles were assigned a great number of incidents, whereas some vehicles had no incidents assigned to them. The findings of this study suggest that the developed framework can identify road blockage and inefficiencies in an emergency response framework with high accuracy. Therefore, the approach presented in this paper can be implemented to develop a practical geographic information system (GIS) based model to enhance the emergency response management for potential future earthquakes. Thereby, this study can facilitate the level of resilience of urban environments to withstand and adapt to future crises.

Applications of Lidar Technology in Urban Geophysical Surveys

This research examines the utilization of mobile LiDAR technology for comprehensive urban geophysical surveys, with a specific emphasis on monitoring infrastructure and urban planning. The study challenge focuses on the requirement for precise and detailed geographical data to assist in making informed urban development and management decisions. The approach utilized a transportable LiDAR device with a laser pulse frequency of 200,000 pulses per second at 1,200 meters. Data collection employed precise GPS and IMU for positional and orientation accuracy, respectively. Statistical analysis included noise filtering and classification metrics to evaluate data quality. Results from the study demonstrated the significant capabilities of LiDAR technology: the survey captured over 10.000 points per square meter with a point accuracy of 0.150 meters. Noise filtering processes retained an average of 85% of data points, with ground classification achieving accuracies above 95%. Statistical tools included standard deviation calculations for elevation models, revealing mean elevations ranging from 13.750 to 19.400 meters across surveyed areas. In addition, LiDAR technology could identify structural deformations of up to 0.200 meters in metropolitan buildings. The study highlights the efficacy of LiDAR in offering accurate and extensive geospatial data for urban planning and infrastructure management. This research adds to the current understanding by showing how LiDAR may be used to improve decision-making and increase the ability of cities to withstand and recover from challenges.

Correction: Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: A Review

Correction: Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: A Review

Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: Concept, State-of-the-Art, Challenges and Opportunities

Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: Concept, State-of-the-Art, Challenges and Opportunities

Urban Traffic Dominance: A Dynamic Assessment Using Multi-Source Data in Shanghai

This study redefines the evaluation of urban traffic dominance by integrating complex network theory with multi-source spatiotemporal trajectory data, addressing the dynamic nature of various transportation modes, including public transit and shared mobility. Traditional traffic studies, which focus predominantly on static road traffic characteristics, overlook the fluid dynamics integral to urban transport systems. We introduce Relative Weighted Centrality (RWC) as a novel metric for quantifying dynamic traffic dominance, combining it with traditional static metrics to forge a comprehensive traffic dominance evaluation system. The results show the following: (1) Both static and dynamic traffic dominance display core-periphery structures centered around Huangpu District. (2) Dynamically, distinct variations in RWC emerge across different times and transport modes; during the early hours (0:00–6:00), shared bicycles show unique spatial distributions, the subway network experiences a notable decrease in RWC yet maintains its spatial pattern, and taxis exhibit intermediate characteristics. Conversely, the RWC for all modes generally increases during morning (6:00–12:00) and evening (18:00–24:00) peaks, with a pronounced decrease in subway RWC in the latter period. (3) The integration of dynamic evaluations significantly modifies conventional static results, emphasizing the impact of population movements on traffic dominance. This comprehensive analysis provides crucial insights into the strategic management and development of urban traffic infrastructure in Shanghai.

Knowledge-based decisions for asset management of urban water infrastructure in Iran and Brazil using a nominal-based multi-criteria group decision method

ABSTRACT Asset management of the Urban Water Infrastructure (UWI) is typically faced with limitations regarding uncertain operational data. This shortcoming can be improved using the knowledge-based decisions of experienced experts. However, since knowledge-based decisions depend on decision-makers’ knowledge and experience, verifying their results in various regions is essential. This subject was addressed in this research. Hence, four groups of experts in Iran and Brazil collaborated to assess the criteria effective in the Maintenance-Rehabilitation Planning (MRP) of urban water pipes. For this purpose, a nominal-based multi-criteria group decision-making method was developed. To verify the results of this method, the viewpoints of non-weighted and weighted decision-makers were investigated and compared pairwise. The findings of this research improved the asset management of the UWI in Iran and Brazil by identifying the criteria applicable to the MRP of the pipes based on knowledge-based decisions. Likewise, it was found that weighting the experts has no decisive effect on the results and is only effective in groups where the decision-makers’ weights are identically distributed. Moreover, it was shown that there is a relationship between the similarity of results and the similarity of experts’ profiles in knowledge-based decision groups of various countries such as Iran and Brazil.

Experimental Study on Automated Controller of Street Light Management System using IoT

In order to ensure public safety and support nighttime activities, street lighting is crucial to the urban landscape, and this study starts by contextualizing its importance. It then goes on to analyze the shortcomings of the current street lighting systems, which are typified by their static control methods and poor environmental response.These shortcomings underscore the urgent need for a paradigm shift towards more intelligent and adaptive solutions. The core proposition of this project lies in the design and implementation of an advanced street light management system empowered by IoT technology. At its heart, the system comprises a sophisticated network of interconnected devices, including sensors, actuators, and a central control unit. These components collaborate synergistically to capture, analyze, and act upon real-time data gleaned from the surrounding environment. Through the application of cutting-edge data analytics and machine learning algorithms, the system autonomously adjusts lighting levels in accordance with fluctuating parameters such as ambient light levels, traffic density, weather conditions, and pedestrian activity. By dynamically optimizing energy usage while ensuring adequate illumination, the system not only enhances operational efficiency but also contributes to cost reduction and environmental sustainability. Moreover, the proposed system incorporates proactive maintenance features, enabling early fault detection and remote diagnostics. This capability minimizes downtime and enhances system reliability, thereby mitigating the operational challenges associated with traditional street lighting infrastructure. The project further explores the conceptual framework of IoT, elucidating its transformative potential in revolutionizing urban infrastructure management. By seamlessly integrating physical devices with digital intelligence, IoT facilitates the creation of interconnected ecosystems wherein data-driven decision-making and automation converge to drive unprecedented efficiency and innovation

Underpass Water Logging System Using IOT and ML

In recent years, the surge in population growth has exacerbated numerous challenges for both humans and the environment, including issues like inadequate infrastructure and frequent occurrences of waterlogging in underpasses. During heavy rainfall, underpasses often become inundated, posing significant risks to passing vehicles and exacerbating traffic congestion. This is primarily due to ineffective drainage systems and flawed designs for diverting rainwater away from the underpasses. To address these pressing issues, this paper proposes an IoT-based solution aimed at monitoring and managing heavy traffic within underpasses. The primary objective is to establish a comprehensive framework capable of continuously monitoring the water levels within underpasses. By leveraging IoT technology, this framework can activate warning signs and implement traffic control measures in real-time to mitigate the risks posed by waterlogging and alleviate traffic congestion. This innovative approach not only offers a proactive solution to the immediate challenges faced by underpasses but also lays the foundation for a more sustainable and efficient urban infrastructure management system. Through the integration of IoT sensors and intelligent control mechanisms, this framework promises to enhance the resilience of underpasses to adverse weather conditions while optimizing traffic flow, ultimately contributing to safer and more livable urban environments . Keywords — Internet of Things, sensors, Wi-Fi module, LCD, cloud architecture.

IoT-Enabled Wireless Sensor Networks and Geospatial Technology for Urban Infrastructure Management

This study is the first to evaluate how effectively urban infrastructure may improve using an innovative approach that combines IoT-enabled wireless sensors, geospatial technology, and machine learning. Smart healthcare monitoring and management is a particular focus. We conducted a series of experiments and evaluations to prove the effectiveness of this approach in actual urban environments. In this research, a series of sensor networks is developed and deployed to collect real-time health data from patients. It is sent to a central controller, and then on up to the cloud for analysis. Various machine learning algorithms are used - such as ANN, LR, DT, and SVM - to predict patient health status on the basis of sensor readings. The results of our experiments show that the ANN model achieved an accuracy rate surpassing all others at 98.65%. Geospatial technology is also looked at in the research as a way to visualize and analyze urban health data. This is necessary for informed decision-making by healthcare providers and urban planners. This research paves the way for smarter, more resilient, and sustainable urban environments by using the latest technology and data-driven methods to advance urban infrastructure Management.

A Novel Underground Culvert Detection and Positioning System Projected along the Public Vertical Line

The limitations of existing procedures make it difficult to locate and identify old subterranean culverts in urban infrastructure management. In order to effectively manage urban infrastructure, subterranean pipe culverts must be accurately located and detected. In this research, we investigate the method of computing the shortest distance from the point to the ellipse and propose a pipeline collision detection method based on the projection of the direction of the common perpendicular. In the positioning accuracy test, we simulate the detection of straight and curved paths and obtain satisfactory results; the experimental results show that the detection errors are within acceptable limits for different azimuth and bending angles; in the correctness test, we compared with AutoCAD and ArcGIS, and found that the algorithm in this paper shows superiority in collision detection, especially when dealing with complex spatial relationships and large amounts of data, with evident efficiency advantages. Through theoretical analysis and experimental verification, we demonstrate the effectiveness and reliability of the method.