Assessment Of Infrastructure Research Articles

Design and assessment of energy infrastructure in new decarbonized urban districts: A Spanish case study

New urban districts have great potential to reduce greenhouse gas emissions by electrifying energy demand in various sectors, such as transportation and buildings. This paper proposes a novel approach that combines different energy models to plan the infrastructure for supplying the energy demand of new, decarbonized, and highly electrified urban districts on a real-world scale. First, a model for energy management in buildings is used to plan and operate equipment for heating, cooling, and solar photovoltaic distributed generation. Second, a model that plans electric vehicle charging infrastructure determines the number and types of charging stations. Then, a large-scale distribution network planning model designs a cost-efficient electricity distribution grid to supply the district. A case study is presented for a new urban district in Madrid, Spain, to demonstrate how this approach can be applied to energy infrastructure planning in a real-world context. This case study presents different scenarios of energy efficiency performances in buildings and penetration levels of solar installations, electric vehicles, and heating and cooling systems. The results show that even in a high electrification scenario, improving energy efficiency in buildings through a district heating network based on heat pumps can lead to a lower peak electricity demand. This peak load reduction allows for integrating more electric vehicles, avoiding further investments in the electricity distribution network. In addition, the results confirm that a highly electrified scenario, which combines energy-efficient buildings with high integration of solar and electric vehicles, significantly reduces non-renewable energy consumption and greenhouse gas emissions.

InSAR-based landslide hazard mapping and identification of at-risk infrastructures in a selected district of North Shewa Zone, Ethiopia.

The devastating nature of landslides demands a thorough understanding of their spatial distribution and the risks they pose to human settlements and infrastructural assets. In this study, we employed a combination of Interferometric Synthetic Aperture Radar (InSAR) and Geographic Information System (GIS) techniques to explore the western escarpment of the Main Ethiopian Rift, with a focus on selected districts within the northern Shewa Zone, Ethiopia. By analyzing the SAR data, we derived 28 displacement maps and utilized them to create a comprehensive landslide hazard zonation map. The results indicated significant ground displacement, particularly along the rift margins and areas characterized by rugged terrain. The hazard zones were classified based on their level of risk, with 44% classified as very low, 24% as low, 5% as moderate, 13% as high, and 14% as very high hazard zones. The accuracy of our results was evaluated using receiver operating characteristic (ROC) analysis, which was conducted utilizing landslide inventory data. The analysis demonstrated a remarkable area under the curve (AUC) value of 0.848, providing strong evidence for the validity of our findings. Additionally, our study involved a spatial and statistical assessment of major infrastructure, revealing that 20 to 28% of these properties were in hazard zones ranging from moderate to very high levels, which calls for efficient risk-reduction actions. Therefore, this finding enables stakeholders to identify high-risk areas, prioritize mitigation efforts, and minimize the impact of landslide disasters.

Disaster risk assessment of educational infrastructure in mountain geographies using PROMETHEE-II

This study delves into the crucial realm of disaster risk assessment pertaining to educational infrastructure in mountain geographies, employing the PROMETHEE-II (Preference Ranking Organization Method for Enrichment Evaluations) technique. The study was carried out in Central Kashmir which is susceptible to various hazards, making the assessment of risks to educational facilities imperative for ensuring the safety and resilience of these crucial establishments. The AHP technique was used to assess the seismic, flood and landslide vulnerabilities of the sample institutions in the study area. The vulnerabilities were then treated against the preparedness levels using the PROMETHEE-II model. The findings reveal that out of 620 sample institutions, about 215 institutions are having high disaster risk and only about 60 sample institutions fall in low-risk category. Through the application of this method, this research aims to provide a comprehensive understanding of the vulnerabilities and potential risks faced by educational infrastructure in mountainous areas. The outcomes of the study will contribute significantly to enhancing disaster preparedness, mitigation strategies, and resource allocation for safeguarding educational institutions in these challenging terrains.

A Simple Approach to the Risk Assessment of Strategic Infrastructure on the Colombian Caribbean Coast

A Simple Approach to the Risk Assessment of Strategic Infrastructure on the Colombian Caribbean Coast

Synergizing IoT-Based Gender-Responsive Security Surveillance to Gauge ICT Infrastructure Maturity

Effective planning and implementation of smart security surveillance ensures campus protection for all genders. Thus, the integration of a reliable ICT infrastructure as the core foundation for the security and privacy of the institutions leads to a substantial transformation in the way individuals interact, increasing the confidentiality of the stakeholders. Hence, the study aims to assess the current state of ICT infrastructure and identify the parameters in the design of an IoT-based gender-responsive security surveillance system for Colegio de San Juan de Letran Manaoag. The study implemented a comprehensive review of related literature about maturity models and their capabilities. A focus group discussion was assembled to administer a semi-structured interview on the current state of the ICT infrastructure and conducted an online survey for the assessment of the ICT infrastructure among the administrators and staff of the MIS Department in identifying the strengths and weaknesses of the current ICT infrastructure. Employing the IT Infrastructure Maturity Model (ITI-MM) as a tool in determining the maturity levels of its capabilities. Based on the results, the ITI-MM model determined the current state of the ICT infrastructure, which is currently operating at maturity level 2, and identified the gaps in achieving the requirements between Level 1 and Level 2. In conclusion, the study indicated that measuring of the maturity level is applicable in integrating the proposed system for developing an IoT-based gender-responsive security surveillance system to enhance the security measures of the institution.

Multimodal seismic assessment of infrastructures retrofitted with exoskeletons: insights from the Foggia Airport case study

AbstractAddressing the seismic vulnerability of infrastructures is critical, especially for those built before the introduction of the current seismic regulations. One of the primary challenges lies in retrofitting these buildings without interrupting their functionality. In this context, the use of exoskeletons for seismic retrofitting represents an effective solution. This approach increases the seismic resistance and ensures the continuous operation of the building during retrofitting. This advantage is especially crucial for critical infrastructures, such as airports. Nevertheless, traditional seismic assessment methods based on pushover analyses might not accurately predict the seismic capacity of complex infrastructures dominated by local vibration modes. To bridge this gap, the study proposes refining the multimodal pushover analysis tailored for seismic vulnerability assessments of large infrastructures with exoskeletons characterized by low modal participation ratios. The Foggia Airport case study exemplifies these points and highlights the practical applications of the discussed advancements. The authors compared two force distributions for push-over analysis, addressing the fine-tuning of exoskeletons to maximize their seismic resistance.

Enhancing performance evaluation of low-cost inclinometers for the long-term monitoring of buildings

The development of low-cost structural and environmental sensors has revolutionized monitoring practices across numerous fields, enabling cost-effective solutions for infrastructure and building health assessment. However, a critical challenge associated with these sensors is their long-term durability and reliability. Surprisingly, despite the significant interest in these low-cost devices, the literature does not present any solutions for ensuring their long-term performance. To address this gap, this study proposes an innovative artificial intelligence-based approach for evaluating the long-term performance of low-cost inclinometers using a low-cost adaptable reliable anglemeter. This method automatically compares the inclinations of actual onsite measurements with predicted values under real environmental conditions. Over time, if the discrepancies between both measurements surpass a predefined statistical threshold, it may signal potential inaccuracies in the low-cost inclinometer, thereby suggesting the need for recalibration or presence of structural anomalies. The effectiveness and applicability of the proposed tool were demonstrated through a long-term study conducted on a real steel frame in Spain.

A Pioneering Integration of Structural Health Assessments and Dynamic Analyses: Bridge Pier Responses to the Impact of Floating Objects during Extreme Floods

This study presents a transformative dynamic amplification factor for assessing the resilience of over-river bridges, informed by the real-world conditions of flood events. Through advanced finite element analysis, we unveil how the interplay between mass and velocity of floating objects significantly influences bridge pier responses, challenging conventional assessment methods. Our findings reveal potential inadequacies in current design standards, such as AASHTO and AS5100, and introduces a dynamic multiplier that enhances structural health assessment algorithms. The core contribution of this research is a data-driven analysis approach, which is critical for the proactive maintenance and risk assessment of bridge infrastructures in areas prone to flooding. By redefining the parameters for damage-level identification, our work advocates for a shift towards more resilient infrastructure systems in the face of global climate change.

Bicycle as a traffic mode: From microscopic cycling behavior to macroscopic bicycle flow

Cities allocate dedicated road space to bicycles in favor of active-mode road users. For urban environments with a mass bicycle volume, bicycle traffic congestion is likely to occur. Hence, a thorough understanding of bicycle traffic flow is necessary for the assessment of cycling infrastructure and the development of traffic management strategies considering cycling efficiency. This study aims to investigate bicycle flow characteristics using microscopic traffic simulation. As bicycle flow performance is subject to the non-lane-based movement strategy and the behavioral heterogeneity among cyclists, various scenarios with different microsimulation settings are evaluated. Ultimately, we derive the functional form fundamental diagrams and macroscopic fundamental diagrams using a curve-fitting approach and an analytical method, respectively. Important macroscopic traffic flow parameters, such as capacity, critical speed, critical density, backward wave speed, etc., are estimated. The results show that lane width, overtaking incentive, and desired speed distribution are factors that affect bicycle flow performance. The distinct features of bicycle flow under different traffic states are identified by discussing the simulation outcome and comparing the estimated flow parameters. The findings can be utilized by future research regarding large-scale bicycle traffic flow modeling and control.

Nonlinear Interactions of Sea‐Level Rise and Storm Tide Alter Extreme Coastal Water Levels: How and Why?

AbstractSea‐level rise (SLR) increasingly threatens coastal communities around the world. However, not all coastal communities are equally threatened, and realistic estimation of hazard is difficult. Understanding SLR impacts on extreme sea level is challenging due to interactions between multiple tidal and non‐tidal flood drivers. We here use global hourly tidal data to show how and why tides and surges interact with mean sea level (MSL) fluctuations. At most locations around the world, the amplitude of at least one tidal constituent and/or amplitude of non‐tidal residual have changed in response to MSL variation over the past few decades. In 37% of studied locations, “Potential Maximum Storm Tide” (PMST), a proxy for extreme sea level dynamics, co‐varies with MSL variations. Over all stations, the median PMST will be 20% larger by the mid‐century, and conventional approaches that simply shift the current storm tide regime up at the rate of projected SLR may underestimate the flooding hazard at these locations by up to a factor of four. Micro‐ and meso‐tidal systems and those with diurnal tidal regime are generally more susceptible to altered MSL than other categories. The nonlinear interactions of MSL and storm tide captured in PMST statistics contribute, along with projected SLR, to the estimated increase in flood hazard at three‐fourth of studied locations by mid‐21st century. PMST is a threshold that captures nonlinear interactions between extreme sea level components and their co‐evolution over time. Thus, use of this statistic can help direct assessment and design of critical coastal infrastructure.

Post-cyclic behavior of Haikou marine clay and their predictions using back-propagation neural network model

The evaluation of post-cyclic behavior of soft clay is critical for the lifetime safety and performance assessment of various infrastructures built on/in it. In this study, by employing the marine clays collected from Haikou, China, a series of both conventional shear tests and post-cyclic shear tests under undrained condition were performed using triaxial setup to investigate the post-cyclic behavior of the clay, with the investigation foci placed on the post-cyclic shear stress and secant modulus. Some important factors, namely confining pressure, loading frequency, cyclic shear strain amplitude and post-cyclic shear strain, were taken into consideration. From both the results from the tests and the parameter sensitivity analysis using random forest method, the confining pressure and cyclic shear strain amplitude were found to be evidently influential on the post-cyclic behavior of the clay, while the influence of loading frequency was relatively insignificant. In addition, an intelligent model was established based on the back-propagation neural network method to predict the post-cyclic behavior of Haikou marine clay, which was proven to be more favorable as compared to other two prediction methods developed based on multi-variate regression analysis and random forest algorithm.

Unmanned Aircraft Systems in Road Assessment: A Novel Approach to the Pavement Condition Index and VIZIR Methodologies

This paper presents an innovative approach to road assessment, focusing on enhancing the Pavement Condition Index (PCI) and Visión Inspection de Zones et Itinéraires Á Risque (VIZIR) methodologies by integrating Unmanned Aircraft System (UAS) technology. The research was conducted in an urban setting, utilizing a UAS to capture high-resolution imagery, which was subsequently processed to generate detailed orthomosaics of road surfaces. This study critically analyzed the discrepancies between traditional field measurements and UAS-derived data in pavement condition assessment. The study findings demonstrate that photogrammetry-derived data from UAS offer at least similar or, in some cases, improved information on the collection of a comprehensive state of roadways, particularly in local and collector roads. Furthermore, this study proposed key modifications to the existing methodologies, including dividing the road network into segments for more precise and relevant data collection. These enhancements aim to address the limitations of current practices in capturing the diverse and dynamic conditions of urban infrastructure. Integrating UAS technology improves the measurement of pavement condition assessments and offers a more efficient, cost-effective, and scalable approach to urban infrastructure management. The implications of this study are significant for urban planners and policymakers, providing a robust framework for future infrastructure assessment and maintenance strategies.

Developing and applying the Hydrogen Economics and infRAstructure optimization model (HERA)

This study aims at developing the Hydrogen Economics and InfRAstructure optimization model (HERA) to support the technical-economic assessment of hydrogen supply chain infrastructures. It is developed at an hourly scale, in Mixed Integer Linear Programming (MIP), and includes the hydrogen production and delivery via road transportation or pipelines, as well as hydrogen and electricity storage options. To be tested, HERA was applied in a case study, to compare two different locations for hydrogen production. Findings show that the use of HERA can support the technical-economic assessment of a hydrogen infrastructure, highlighting the impact of the distance between the hydrogen production site and the demand, and the use of batteries and complementary renewable resources, to smooth the variability, on total costs. Besides, the proposed model was able to find out the optimal equipment combination for the supply chain by considering the modular (integer) nature of the problem to be solved.

Unmanned Aerial Vehicle-Based Structural Health Monitoring and Computer Vision-Aided Procedure for Seismic Safety Measures of Linear Infrastructures.

Computer vision in the structural health monitoring (SHM) field has become popular, especially for processing unmanned aerial vehicle (UAV) data, but still has limitations both in experimental testing and in practical applications. Prior works have focused on UAV challenges and opportunities for the vibration-based SHM of buildings or bridges, but practical and methodological gaps exist specifically for linear infrastructure systems such as pipelines. Since they are critical for the transportation of products and the transmission of energy, a feasibility study of UAV-based SHM for linear infrastructures is essential to ensuring their service continuity through an advanced SHM system. Thus, this study proposes a single UAV for the seismic monitoring and safety assessment of linear infrastructures along with their computer vision-aided procedures. The proposed procedures were implemented in a full-scale shake-table test of a natural gas pipeline assembly. The objectives were to explore the UAV potential for the seismic vibration monitoring of linear infrastructures with the aid of several computer vision algorithms and to investigate the impact of parameter selection for each algorithm on the matching accuracy. The procedure starts by adopting the Maximally Stable Extremal Region (MSER) method to extract covariant regions that remain similar through a certain threshold of image series. The feature of interest is then detected, extracted, and matched using the Speeded-Up Robust Features (SURF) and K-nearest Neighbor (KNN) algorithms. The Maximum Sample Consensus (MSAC) algorithm is applied for model fitting by maximizing the likelihood of the solution. The output of each algorithm is examined for correctness in matching pairs and accuracy, which is a highlight of this procedure, as no studies have ever investigated these properties. The raw data are corrected and scaled to generate displacement data. Finally, a structural safety assessment was performed using several system identification models. These procedures were first validated using an aluminum bar placed on an actuator and tested in three harmonic tests, and then an implementation case study on the pipeline shake-table tests was analyzed. The validation tests show good agreement between the UAV data and reference data. The shake-table test results also generate reasonable seismic performance and assess the pipeline seismic safety, demonstrating the feasibility of the proposed procedure and the prospect of UAV-based SHM for linear infrastructure monitoring.

Categorization of tweets for damages: infrastructure and human damage assessment using fine-tuned BERT model.

Identification of infrastructure and human damage assessment tweets is beneficial to disaster management organizations as well as victims during a disaster. Most of the prior works focused on the detection of informative/situational tweets, and infrastructure damage, only one focused on human damage. This study presents a novel approach for detecting damage assessment tweets involving infrastructure and human damages. We investigated the potential of the Bidirectional Encoder Representations from Transformer (BERT) model to learn universal contextualized representations targeting to demonstrate its effectiveness for binary and multi-class classification of disaster damage assessment tweets. The objective is to exploit a pre-trained BERT as a transfer learning mechanism after fine-tuning important hyper-parameters on the CrisisMMD dataset containing seven disasters. The effectiveness of fine-tuned BERT is compared with five benchmarks and nine comparable models by conducting exhaustive experiments. The findings show that the fine-tuned BERT outperformed all benchmarks and comparable models and achieved state-of-the-art performance by demonstrating up to 95.12% macro-f1-score, and 88% macro-f1-score for binary and multi-class classification. Specifically, the improvement in the classification of human damage is promising.

A regional-scale approach to assessing non-residential building, transportation and cropland exposure in Central Asia

Abstract. Critical infrastructure has a paramount role in socio-economic development, and its disruption can have dramatic consequences for human communities, including cascading impacts. Assessing critical-infrastructure exposure to multiple hazard is therefore of utmost importance for disaster risk reduction purposes. However, past efforts in exposure assessment have predominantly concentrated on residential buildings, often overlooking the unique characteristics of critical infrastructure. Knowing the location, type and characteristics of critical infrastructure is particularly challenging due to the overall scarcity of data and difficulty of interacting with local stakeholders. We propose a method to assess exposure of selected critical infrastructure and demonstrate it for Central Asia, a region prone to multiple hazards (e.g., floods, earthquakes, landslides). We develop the first regionally consistent exposure database for selected critical infrastructure and asset types (namely, non-residential buildings, transportation and croplands), assembling the available global and regional datasets together with country-based information provided by local authorities and research groups, including reconstruction costs. The method addresses the main known challenges related to exposure assessment of critical infrastructure (i.e., data scarcity, difficulties in interacting with local stakeholders) by collecting national-scale data with the help of local research groups. The analysis also includes country-based reconstruction costs, supporting regional-scale disaster risk reduction strategies that include the financial aspect.

An efficient lightweight railway track segmentation network for resource-constrained platforms with TensorRT

Abstract Accurate and rapid railway track segmentation is fundamental for foreign object intrusion detection, inspection, online monitoring, and non-destructive assessment of transportation infrastructure. Recently, vision-based track segmentation algorithms have demonstrated strong performance. However, most existing models struggle to meet the real-time requirements of resource-constrained edge devices. Considering this challenge, we propose an edge-enabled real-time railway track segmentation algorithm, which is optimized to be suitable for edge applications by optimizing the network structure and quantizing the model after training. Initially, Ghost convolution is introduced to reduce the complexity of the backbone, thereby achieving the extraction of key information on the region of interest at a lower cost. To further reduce the model complexity and calculation, a new lightweight detection head is proposed to achieve the best balance between accuracy and efficiency. Subsequently, we introduce quantization techniques to map the model’s floating-point weights and activation values into lower bit-width fixed-point representations, reducing computational demands and memory footprint, ultimately accelerating the model’s inference. Finally, we draw inspiration from GPU parallel programming principles to expedite the pre-processing and post-processing stages of the algorithm by doing parallel processing. The approach is evaluated with the public and challenging dataset RailSem19 and tested on Jetson Nano. Experimental results demonstrate that our enhanced algorithm achieves an accuracy level of 83.3% with 25 FPS inference speed when the input size is 480$\times $480. The code can be found at: https://github.com/ccl-1/light-yolov8-seg-quantization-tensorrt.

Comparing the application of different justice theories in equity analysis of transit projects: A case study of the Lisbon Metro Circular Line

Although issues of equity and accessibility have already been addressed in transportation, especially with regard to the distribution of costs and benefits, there is no consensus on which concept and metric of fairness would be most appropriate for the evaluation of transportation infrastructure proposals. Normally, a utilitarian perspective is adopted, where issues of unequal distribution of costs and benefits are not the main focus. This paper aims to incorporate the assumptions of other justice theories, namely egalitarianism, communitarianism, and Capability Approach (CA), into the equity assessment of transportation infrastructures, and by doing so, pay closer attention to those who are less advantaged or more open to social exclusion. These theories are critically reviewed considering their contribution to the assessment of equity in terms of transportation infrastructure accessibility impacts. Based on the reviewed theories, accessibility indicators are built and used to assess the equity impacts of the Lisbon Metro expansion project. The findings support the importance of adding other justice perspectives to assessing transportation projects. The CA and Maximax support a need to establish minimum or acceptable distribution standards of accessibility. However, the results from the CA are strongly dependent on the assumptions as to the maximum acceptable travel times.

Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment

Remotely sensed imagery has increased dramatically in quantity and public availability. However, automated, large-scale analysis of such imagery is hindered by a lack of the annotations necessary to train and test machine learning algorithms. In this study, we address this shortcoming with respect to above-ground storage tanks (ASTs) that are used in a wide variety of industries. We annotated available high-resolution, remotely sensed imagery to develop an original, publicly available multi-class dataset of ASTs. This dataset includes geospatial coordinates, border vertices, diameters, and orthorectified imagery for over 130,000 ASTs from five labeled classes (external floating roof tanks, closed roof tanks, spherical pressure tanks, sedimentation tanks, and water towers) across the contiguous United States. This dataset can be used directly or to train machine learning algorithms for large-scale risk and hazard assessment, production and capacity estimation, and infrastructure evaluation.

UAS-based methodology to create digital models of bridges and viaducts

The use of Unmanned Aerial Systems allows to exploit the aerial photogrammetry technique to obtain high-resolution orthophotos with large images, owing to latest generation payloads. This approach allows to acquire metric data of a structure through the analysis of pairs of stereometric frames. Aerial photogrammetry forms the basis for the creation of point cloud data models used to develop real-time digital replication of objects (or Digital Twins systems)to monitor, analyze and simulate their behavior in the real world, along with Finite Element models for structural analysis, material simulation and design of complex components. In addition, BIM models are developed that integrate detailed information on infrastructure geometry, materials, and performance into a single collaborative 3D model, for improved management and planning during the bridge lifespan. The results obtained by applying these techniques to an Italian bridge will be examined, providing crucial support to experts in infrastructure assessment and monitoring.