Critical Transportation Infrastructure Research Articles

Experimental modal identification of a pedestrian bridge through drive-by monitoring integrated with shared-mobility vehicles

In the context of improving resilience in transport infrastructure, an emerging approach of indirect structural health monitoring is gaining attention, known as drive-by monitoring, instrumenting a vehicle with sensors to evaluate the bridges it crosses. However, their effectiveness has predominantly been investigated in ideal scenarios, with actual real-world applications being quite scarce. The research presented in this paper explores the feasibility of combining two routes: (1) fleet emissions reduction and (2) transport resilience enhancement, through drive-by monitoring integrated with shared mobility, including electric mobility scooter and bicycle. The information from drive-by data collected from shared mobility can give valuable information on critical transport infrastructure (e.g., bridges) and such a drive-by database has the potential for network level infrastructure condition assessment. In this paper, two different drive-by roadmaps are investigated subject to the flexibility of the bridges, namely partially or fully drive-by monitoring. To validate the proposed roadmaps, a full-scale pedestrian bridge was chosen for drive-by testing, where smartphone sensors and specialised accelerometers are mounted on shared mobility for data acquisition. Experimental results demonstrate that (i) smartphone sensing can provide data with similar accuracy compared to specialised accelerometers, (ii) bridge frequencies can be easily obtained from temporarily parked shared mobility, with a maximum relative error of 1.05%, (iii) both the bridge frequencies and operational deflection shapes are successfully extracted from the moving shared mobility by using variational mode decomposition and filtering techniques, and shared mobility’s GPS data along with moving speeds are collected for potential vehicle positioning and drive-by database updating.

Risk and vulnerability analysis of road network in landslide prone areas in Munnar region, India

Catastrophes like landslides have the potential to impair critical transportation infrastructure, particularly road networks. The hilly regions in the state of Kerala in India are particularly prone to hazards and changing climate conditions. During the monsoon season, landslides are common in the Western Ghats, and the intensity of adverse impacts is more severe due to its densely populated regions. The study area of this research is in Idukki district of Kerala, where over 60 % of the land is prone to landslides. The monsoon rains bring with them a slew of disastrous landslides in the region. Most of the roadways in the study area are often disrupted due to landslides. Landslide risk assessment (LRA) is a crucial component of research on adverse impacts of landslide. The use of Geographical information System (GIS) and Multi Criteria Decision Analysis (MCDA) using Analytical Hierarchical Process (AHP) for susceptibility mapping and hazard risk assessment assists in the identification of disaster-prone areas. The data collected by field surveys were used to confirm the study results and the high-risk zones of the region were identified and the risk maps are prepared for the region as well as for the road network in the region. The risk of landslides may be described as the possibility of negative repercussions on road network thereby adversely impacting the inhabitants of the region. The landslide risk assessment of the road network in a hilly region is carried out which gives important insights for risk management and for future planning of resilient development in the region. This study enhances the knowledge for management of road network risk and vulnerabilities in intricate hilly region settings by developing a thorough vulnerability analysis framework. The research advances by giving transportation engineers a useful quantitative tool for identifying the risk and vulnerabilities of road network and directing design strategies and mitigation measures to lessen the possible negative effects of disruptive events like landslides on road infrastructure. The research findings could be an input for policy makers to plan for alternative resilient strategies for landslide risk management in road networks. The rational methodology adopted here could be replicated for carrying out risk and vulnerability assessment in other landslide prone areas.

The Impact of Blockchain on Critical Transport Infrastructure – The Case of Aviation

Abstract Critical Infrastructures produce critical goods and services for society and cover a wide range of sectors according to the governance frameworks in place in the United States, in the European Union (EU) and in EU Member States including Romania. Aviation is a particular case for the critical transport infrastructure, with a high degree of technical complexity, economic productivity and complex supply and production chains. Blockchain or Distributed Ledger is an already famous digital emerging technology with the possibility of disintermediating numerous system processes, thereby reducing costs, increasing security and reducing risk in a world where cumbersome and expensive intermediaries are required for trust in all sorts of transactions. This article provides a brief overview of the critical air transport infrastructure and of the blockchain technology, traces the possible uses of blockchain in aviation, gives real world examples and concludes with recommendations for practitioners moving forward.

Accelerometer-aided millimeter-wave radar interferometry for uninterrupted bridge displacement estimation considering intermittent radar target occlusion

Monitoring bridge displacement is necessary to detect early signs of damage, prevent catastrophic failures, and maintain public confidence in the safety of critical transportation infrastructure. Radar interferometry is attractive for long-term continuous monitoring of bridge displacements, given its resilience to weather and lighting conditions. However, intermittent radar target occlusions caused by moving vehicles beneath bridges pose challenges during extended continuous monitoring periods. In this study, accelerometer-aided millimeter-wave radar interferometry was explored with an explicit consideration of intermittent radar target occlusion. Radar and accelerometer measurements were first recorded over a short period. Multiple good targets were selected among all radar-detected targets, and their direction-converting factors were estimated using short-period measurements. Subsequently, bridge displacement was continuously estimated by overcoming intermittent radar target occlusions. A displacement reconstruction algorithm was proposed for target occlusion periods, and a multitarget-based phase-unwrapping algorithm was proposed to remove the displacement drift caused by radar target occlusion. The proposed technique was experimentally validated through laboratory tests on a 10-meter-long bridge structure and field tests on two different spans of a footbridge. The technique accurately estimated displacements with overall errors below 0.1 mm, allowing it to be widely applied to bridges with millimeter-scale displacements.

A Discussion of Building a Smart SHM Platform for Long-Span Bridge Monitoring.

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure.

Predicting the hydraulic response of critical transport infrastructures during extreme flood events

Understanding the effects of extreme floods on critical infrastructures such as bridges is paramount for ensuring safety and resilient design in the face of climate change and extreme events. This study develops robust computational and predictive modeling tools for assessing the impacts of extreme floods on the hydraulic response and structural resilience of bridges. A computational fluid dynamic (CFD) model utilizing RANS equations and k-ω Shear Stress Transport (SST) for simulating supercritical flows is adopted to compute hydrodynamic pressures and the water levels on bridge piers of cylindrical and rectangular shapes during a flood event. The CFD model is validated based on the case study data obtained from the Haj Omran Bridge, built on the Khorramabad River in Iran. The numerical simulations consider hydrological conditions and exclude geotechnical parameters and abutment damages. The numerical results are evaluated based on well-established design guidelines. Machine learning techniques, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), optimized with Grid Search Cross-Validation (GSCV), are adopted to enhance the accuracy of hydrodynamic pressure forecasting at bridge piers. The XGBoost model exhibits superior performance (R2 = 0.908, RMSE = 0.0279, and E = 3.41%) compared to the RF and SVR models. All the estimated pressure data by XGBoost falls within ±6 percent error lines, highlighting the model's robustness for out-of-range hydrodynamic pressure prediction. Additionally, an optimized Long Short-Term Memory (LSTM) model is adopted to effectively predict free surface flow profiles (i.e. flood depth) over the bridge (R2 = 0.937 and RMSE = 0.083), demonstrating its potential for practical applications of flood depth predictions over bridge infrastructures. The proposed methodological framework outlined in this study can facilitate modeling the impacts of extreme floods on bridges, enabling robust climate resilience assessment of critical infrastructures.

THE EFFECTS OF ENVIRONMENTAL PECULIARITIES ON TRANSPORTATION INFRASTRUCTURE PERFORMANCE IN LAGOS METROPOLIS, NIGERIA: RESIDENTS' EXPERIENCES

Efficient transportation infrastructure is pivotal for the seamless functioning of global cities, with coastal cities facing unique challenges due to environmental peculiarities. This study delves into how Lagos' environmental peculiarities impact transportation infrastructure performance, shedding light on both positive and negative implications. A comprehensive survey involving 1284 residents within Lagos Metropolis was conducted using multistage sampling techniques. A combination of descriptive (percentage and mean-weighted analysis) and inferential (Fisher’s exact test and Phi Cramer's V Test) statistics was adopted for data analysis. Major findings revealed that the environmental peculiarities in Lagos foster the use of multimodal infrastructure options and concurrently exert adverse effects on various facets of transportation infrastructure performance, including travel cost, commuting time, fare charges, wear and tear of infrastructure, and the overall cost of maintenance and repairs. The results of the Fisher’s exact test underscore the undeniable impact of Lagos' environmental peculiarities on transportation infrastructure performance (p = 0.000 < 0.05). This study concludes that high water tables, as a representative environmental peculiarity, substantially influence transportation infrastructure performance, shaping the mobility needs of residents. In light of these findings, the study advocates for the full implementation of sustainable infrastructure solutions. Recommendations include establishing robust drainage systems, incorporating corrosion-resistant materials, and adopting innovative technologies to mitigate the repercussions of rising sea levels on critical transportation infrastructure, enhancing its resilience in the face of environmental challenges.

Adaptive Pathways Using Emerging Technologies: Applications for Critical Transportation Infrastructure

Hazards are becoming more frequent and disturbing the built environment; this issue underpins the emergence of resilience-based engineering. Adaptive pathways (APs) were recently introduced to help flexible and dynamic decision making and adaptive management. Especially under the climate change challenge, APs can account for stressors occurring incrementally or cumulatively and for amplified-hazard scenarios. Continuous records from structural health monitoring (SHM) paired with emerging technologies such as machine learning and artificial intelligence can increase the reliability of measurements and predictions. Thus, emerging technologies can play a crucial role in developing APs through the lifetimes of critical infrastructure. This article contributes to the state of the art by the following four ameliorations. First, the APs are applied to the critical transportation infrastructure (CTI) for the first time. Second, an enhanced and smart AP framework for CTI is proposed; this benefits from the resilience and sustainability of emerging technologies to reduce uncertainties. Third, this innovative framework is assisted by continuous infrastructure performance assessment, which relies on continuous monitoring and mitigation measures that are implemented when needed. Next, it explores the impact of emerging technologies on structural health monitoring (SHM) and their role in enhancing resilience and adaptation by providing updated information. It also demonstrates the flexibility of monitoring systems in evolving conditions and the employment of AI techniques to manage pathways. Finally, the framework is applied to the Hollandse bridge, considering climate-change risks. The study delves into the performance, mitigation measures, and lessons learned during the life cycle of the asset.

Uncertainty in flood risk assessment of linear structures: Why correlation matters

Estimation of the risk posed by inland fluvial floods to critical linear transportation infrastructures requires quantifying the damage inflicted by flooding on roads and railways. The estimated risk is often quantified in terms of the Expected Annual Damage [EAD] of the given linear structure. The aim of this article is to shed light on the estimation of this number and its uncertainty. The damage function models, which describe the degree of damage given flood intensity, represent a source of uncertainty. In this article we argue that this uncertainty needs to be embedded with a correlation structure in order to be evaluated for aggregated values. To this end, a novel methodology based on the use of Gaussian Processes to model the uncertainty associated with the damage function is introduced. Assuming a spatial correlation structure, parameterized by the so called decorrelation length scale, this framework is applied to estimate the EAD to roads in Portugal. The study shows that the application of an appropriate decorrelation length scale is decisive to the estimated uncertainty of the EAD. The proposed methodology may also be applicable to other types of hazards, if represented by some hazard intensity parameter associated with a damage function.

Point cloud generation for critical transportation infrastructure through Bézier curve

While Light Detection and Ranging (LiDAR)-based sensors exhibit considerable potential for transportation infrastructure management, not all infrastructure elements can be comprehensively captured by point clouds, leading to the formation of undesirable “holes” due to both temporary and permanent occlusions. It is imperative to devise mechanisms for identifying and predicting the missing data within these “holes” to ensure the continuous acquisition of critical inventory information. This paper describes a method for generating point clouds based on Bézier curves, which effectively fills the voids within the infrastructure. This method comprises three integral processes, including angle-based boundary detection, identification of principal elevation change, and Bézier curve-based hole filling. The method demonstrates promising results on different roadside surfaces and at different ranges of scales of “holes”. Case studies on the sidewalk, and sound barrier inventories show that the proposed method can significantly improve the quality of the point cloud data for subsequent measurements.

"Ankara Batıkent Mesa Bölgesinde Zemin Deformasyon Kontrolü: Metro Hattı Kontrolü

Subway systems in large cities play a crucial role in enhancing the sustainability and livability levels of urban areas by offering a solution to heavy traffic and transportation problems. Subway tunnels are critical transportation infrastructures that are constructed to serve residential areas, and thus, they have a direct relationship with residential areas in terms of construction and operation. This study is designed to generate precise data by analyzing ground deformation of metro systems using the Persistent Scatterer (PSI) Interferometry method and utilizing free dataThe Batıkent-Törekent metro line in Ankara is identified with the M3 code. At the location where M3 metro passes, ground deformation has been detected. In the analysis of the relevant location, both ascending and descending Sentinel-1 satellite datasets were used. The StaMPS (Stanford Method for Persistent Scatterers)/PSI (approach was utilized in the study. It was found that the results obtained from the ascending datasets were more sensitive. Observations were made in two different periods using ascending datasets: 2015-2016 and 2017-2022. Deformations were observed in the Line of Sight (LOS) direction in the locations where rail systems and tunnels were present. Eight months after the observation, due to the ground deformation in the area, passenger safety was at risk, and a ground strengthening work was carried out, resulting in a 16-day shutdown of the metro system in this location. The results of this study provide concrete contributions to the literature on the possibility of accurate ground measurement using free data

Analyzing the Connectivity of Combined Statistical Areas in Different Census Regions Using ArcGIS

The study aims to analyze the spatial relationship between critical infrastructure and socioeconomic regions in Texas cities, identify areas at risk, and develop effective emergency response strategies. It also focuses on analyzing the connectivity of Combined Statistical Areas (CSAs) in different census regions through various modes of transportation, identify areas that require infrastructure upgrades, and inform policy decisions aimed at improving transportation infrastructure and increasing connectivity. The goal is to provide a comprehensive understanding of the complex relationship between critical infrastructure, emergency response, and transportation infrastructure in Texas cities, and inform policy decisions aimed at addressing disparities and improving connectivity.

Sustainability of Bridges: Risk Mitigation for Natural Hazards

Bridges serve as essential parts of transportation infrastructure, facilitating the movement of people and goods across rivers, valleys, and other obstacles. However, they are also susceptible to a wide range of natural hazards, including floods, earthquakes, and landslides, which can damage or even collapse these structures, leading to severe economic and human losses. A risk index has been developed to address this issue, which quantifies the likelihood and severity of natural hazards occurring in a specific location. The application of risk indices for natural hazards in bridge management involves a data collection process and mathematical modelling. The data collection process gathers information on bridges’ location, condition, and vulnerability, while mathematical modelling uses the data to assess the risk of natural hazards. Overall, risk indices provide a quantitative measure of the vulnerability of bridges to natural hazards and help to prioritize maintenance and repair activities. Mitigation measures are then evaluated and implemented based on the risk assessment results. By using this tool, the UBMS research group has developed an algorithm for risk assessment which will be essential in the decision-making process, specifically focused on enhancing Fund Optimization, Deterioration Modelling, and Risk Analysis. These developments effectively fulfill the primary objectives associated with addressing and mitigating hazards. This development also helps bridge managers understand the potential threats posed by natural hazards and allocate resources more efficiently to ensure the safety and longevity of critical transportation infrastructure.

A standards-based approach to enhancing interoperability of low-cost industrial IoT flood sensors for transportation system resilience

Transportation infrastructure assets are particularly vulnerable to natural hazards, which are becoming more frequent and severe due to climate change and extreme weather conditions. Floods and flash floods are among the deadliest natural hazards, accounting for 50% of vehicle-related fatalities. This underscores the need for timely transportation flood detection systems adoption. However, current flood detection technologies are inadequate in terms of coverage, speed, geographical specificity, and interoperability, making it difficult for emergency managers to respond effectively to flood events. To address this issue, we propose a high-resolution network of low-cost Industrial Internet of Things (IIoT) sensing devices deployed at flood-prone transportation assets. These sensors collect location-specific data, which is then published in standardized formats, interfaces, and protocols, enabling other systems to generate flood forecasts, nowcasts, and warnings. A framework for Incident Management Systems (IMS) was also discussed to highlight the need for system interoperability during disaster management operations. Our solution employs standards-based interoperability, using the OIIE™ OpenO&M ecosystem architecture, to enable seamless interaction between interdependent systems and manages the risk to critical transportation infrastructure. The technology was tested at a microscale level to evaluate its performance. The model architecture supports scalable systems of systems interoperability for standardized use cases and common asset classes used in transportation, energy, facilities, and other critical infrastructure.

A comparative analysis of University Sustainable Travel Plans – Experience from Australia

Travel Demand Management (TDM) initiatives are widely applied by transport planners to establish and enable appropriate use of critical transport infrastructure. Less attention has been given to the specific case of TDM in an education precinct (university) context. Travel Plans have been promoted as a means for an organisation to encourage sustainable travel choices by their employees, visitors and customers. This paper offers an empirical contribution to the literature through a comparative qualitative evaluation of selected University Sustainable Travel Plans (USTPs) in Australia to identify the most important questions that a USTP should address explicitly. The evaluation comprised identification of a set of evaluation questions, completion of a template for each USTP considered and application of a simple scoring exercise. We also identify TDM measures that have been introduced as part of a USTP in response to the typical travel patterns exhibited in university settings. A contribution of this paper is to create a means of comparison of USTPs and to establish the components of a comprehensive travel plan.

Transportation Networks in the Face of Climate Change Adaptation: A Review of Centrality Measures

This paper presents a comprehensive review of centrality measures and their usefulness in transportation networks in the face of climate change adaptation. The focus is on understanding the importance of transportation nodes in the event of extreme weather events and climate-related disasters that may render them inoperable. The paper argues that if critical nodes can be identified, they can be better protected, while resources can be allocated to ensure their functioning in the event of such events. The paper assesses 17 centrality measures, including degree, closeness, betweenness, eigenvector, and Katz, and evaluates their usefulness and usability in transportation networks. The review highlights the need to reformulate these measures to take into account traffic- and transport-related parameters and variables. Without this reformulation, centrality measures only reveal node importance in a topological or structural way and fail to capture the true significance of the nodes in a transportation network. The reformulation enables the centrality measures to be properly applied in a transportation network and to expose the significance of their elements. This work has important implications for transportation planners and policy-makers in ensuring the resilience of critical transportation infrastructure in the face of climate-related disasters.

Design and Implementation of a Quantitative Risk Assessment Model for UK Road Tunnels

One of the critical transportation infrastructures is road tunnels and fire safety is one of the important aspects of their operation. Interaction between the fire, tunnel users, traffic, and fire safety measures influences fires in road tunnels. Therefore, a complex model is required to analyse the risk and quantify the consequences. In this paper, a novel quantitative risk analysis model developed for UK road tunnels is presented consisting of a quantitative consequence analysis model and a quantitative frequency frequency analysis model. The proposed quantitative consequence analysis model is provided through three sub-models; queue model, distribution model, and egress model. The frequency analysis is via an event tree that takes into account the tunnel fire rate in UK road tunnels. After a brief description of this model, the proposed method is illustrated through a case study of an urban road tunnel. The effect of different emergency ventilation systems on societal risk and sensitivity of the model to pre-movement time, accident frequencies involving Heavy Good Vehicles (HGVs), tenability threshold temperature, and different burning vehicles were studied in this case study.

Automated vision-based post-earthquake safety assessment for bridges using STF-PointRend and EfficientNetB0

Bridges are critical transportation infrastructure in Taiwan that are at high risk of structural damage during major earthquake incidents. The structural health monitoring method currently used to assess bridge safety nationwide is labor-intensive and expensive to implement. In this study, a novel automated bridge safety assessment system was developed to facilitate post-earthquake bridge inspections using symbiotic organism search-transfer learning-PointRend (STF-PointRend) for component and damage type detection, EfficientNetB0 for damage level detection, and earthquake resistance index for safety assessment. In the case studies, the STF-PointRend model obtained good testing results, with global mean Intersection over Union values of 76.31 and 75.47% for bridge-component and damage-type detections. Furthermore, the EfficientNetB0 model obtained an average F1 score of 0.86 for damage-level detection in the testing results. The developed model was used to conduct a safety evaluation of two bridges (Lempenge I and Luk I) that had suffered damage in a 2018 earthquake in Palu, Indonesia. The earthquake-resistance scores of 56.15 and 53.21 respectively earned by the two bridges indicate that both require immediate maintenance.

Identification of Resilience Dimensions in Critical Transportation Infrastructure Networks

Identification of Resilience Dimensions in Critical Transportation Infrastructure Networks

A Vulnerability Assessment Approach for Transportation Networks Subjected to Cyber–Physical Attacks

Transportation networks are fundamental to the efficient and safe functioning of modern societies. In the past, physical and cyber space were treated as isolated environments, resulting in transportation network being considered vulnerable only to threats from the physical space (e.g., natural hazards). The integration of Internet of Things-based wireless sensor networks into the sensing layer of critical transportation infrastructure has resulted in transportation networks becoming susceptible to cyber–physical attacks due to the inherent vulnerabilities of IoT devices. However, current vulnerability assessment methods lack details related to the integration of the cyber and physical space in transportation networks. In this paper, we propose a new vulnerability assessment approach for transportation networks subjected to cyber–physical attacks at the sensing layer. The novelty of the approach used relies on the combination of the physical and cyber space, using a Bayesian network attack graph that enables the probabilistic modelling of vulnerability states in both spaces. A new probability indicator is proposed to enable the assignment of probability scores to vulnerability states, considering different attacker profile characteristics and control barriers. A probability-based ranking table is developed that details the most vulnerable nodes of the graph. The vulnerability of the transportation network is measured as a drop in network efficiency after the removal of the highest probability-based ranked nodes. We demonstrate the application of the approach by studying the vulnerability of a transportation network case study to a cyber–physical attack at the sensing layer. Monte Carlo simulations and sensitivity analysis are performed as methods to evaluate the results. The results indicate that the vulnerability of the transportation network depends to a large extent on the successful exploitation of vulnerabilities, both in the cyber and physical space. Additionally, we demonstrate the usefulness of the proposed approach by comparing the results with other currently available methods. The approach is of interest to stakeholders who are attempting to incorporate the cyber domain into the vulnerability assessment procedures of their system.