Transportation Infrastructure Assets Research Articles

Application of Satellite-Based Remote Sensing for the Management of Pavement Infrastructure Assets

The long-term monitoring of transportation infrastructure assets at a lower cost and with short mobilization time is of significant interest to both state and federal transportation agencies in the U.S. Because of the significant improvement in spatial and temporal resolution of synthetic aperture radar (SAR) remote sensing systems and a notable reduction in the cost of data acquisition, SAR has now become a viable method to provide economic and rapid condition assessment of transportation assets. A research study was developed and performed to comprehensively perform the inspection and characterization of a pavement surface based on the amplitude of backscattering of an X-band radar. In situ characterization of the test site was first performed using traditional inertial profilers and aerial photogrammetry with unmanned aerial vehicle (UAV) surveys. The results from these in situ methods were compared with the corrected amplitude of the SAR data, which indicated that the distribution of surface roughness values computed from the inertial profiler, UAV, and SAR exhibited similar probability densities at various segmental lengths considered in this study. This suggested that the problematic areas that are evident during in situ characterization can be delineated and quantified based on the normalized radar cross section of the pavement surface. Overall, the outcome of this research exhibits the potential of SAR for future transportation asset management undertakings, and the systematic framework developed as a part of this research could be of significant interest to engineers and transportation practitioners.

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.

Impact of drying-wetting cycles on the small strain behaviour of compacted clay

Small strain shear modulus (Gmax) is an important parameter for assessing the performance of compacted soils that underlie typical transport infrastructure assets such as railway tracks. This is particularly important when considering changes in climate patterns, which are expected to yield larger seasonal soil-atmosphere moisture fluctuations. This in turn results in the progressive variation of the small strain properties of compacted soils during their service life (i.e. drying and wetting). In this study, the small strain shear behaviour was evaluated for an intermediate plasticity clay (i.e. kaolin) in a series of drying and wetting cycles. Four different dying and wetting boundaries were considered to explore a wide range of moisture amplitudes during 10 drying-wetting cycles. Drying and wetting was controlled using gravimetric water content in order to mimic realistic field conditions typically observed at substructure level. An ultrasonic pulse transmission method was used to capture the change in small strain stiffness and volume at discrete points during the drying-wetting cycles. The results reveal clear distinctions in behaviour for all four boundaries considered, with wetting boundaries having the greatest influence on behaviour. In this instance, specimens brought to full saturation during wetting exhibited an increase in the small strain shear modulus during progressive drying-wetting cycles. However, a reduction was observed when the wetting boundary was restricted to the compacted state. Measured volume changes were also in agreement with these findings, however there was some evidence of volume increase when drying to residual conditions. The results suggest that this is associated with the formation of the partial pendular state where a loss of capillary contacts between particles occurs. Furthermore, when all data for 10 drying-wetting cycles is plotted in the e-Gmax space, a linear relationship is observed for different constant water content levels. Remarkably, this trend is shown to be independent of the boundary conditions considered in this study or number of drying-wetting cycles.

Improving the life cycle greenhouse gas emissions of transport infrastructure assets – a case study in Brussels, Belgium

Infrastructure transportation assets represent a significant share of environmental effects associated with built stocks. Although there is a growing body of knowledge on the life cycle assessment of buildings and material flow analysis of building stocks, infrastructure assets have received less attention. Existing life cycle assessment studies tend to rely on process analysis which is known to underestimate embodied environmental flows. The aim of this paper is to introduce a bottom-up parametric model for infrastructure transport assets, which can support a detailed characterization of the stock in a consistent and transparent manner. It relies on comprehensive hybrid exchange data for the quantification. This paper establishes the approach and equations used in the proposed model. The model consists of four main steps, namely: developing parametric representations of transport infrastructure assets, quantifying their material stock and replacement-related flows, quantifying life cycle embodied environmental flows and visualizing the results. One pilot case study of a road (including pedestrian sidewalks and bike lanes) and light railway, is used to demonstrate the model. Results assessing this pilot case study over 60 years are presented, including the material stock and replacement flows, as well as life cycle embodied greenhouse gas emissions (LCEGHG). Together these results provide important insights into material/infrastructure asset hotspots assets (e.g. steel lighting posts). Emissions-intensive material products and assets have a notable impact on results of LCEGHG, and they dominate both Initial greenhouse gas emissions (IEGHG) and recurrent greenhouse gas emissions (REGHG). However, they contribute marginally to the total mass. Another important observation is the significance of REGHG representing ~30% of LCEGHG. Interestingly, steel, asphalt and concrete are the main contributors to both REGHG and IEGHG. Future research consists of automating the proposed model and packaging it as a python-based Rhinoceros Grasshopper plugin before conducting testing and verification.

Application of Unmanned Aerial Technologies for Inspecting Pavement and Bridge Infrastructure Assets Conditions

Infrastructure in the U.S.A. was graded with a “C–” in the 2021 ASCE report card. Frequent monitoring of infrastructure assets is key to ensuring the serviceability of the assets and safety of public users. Advancements in aerial technologies, compact sensors, computer processing, and image analysis software have given rise to various engineering applications of sensor-mounted unmanned aerial vehicle (UAV) platforms. Several contactless sensors are being mounted on UAVs to conduct a comprehensive assessment of infrastructure conditions compared to a qualitative visual examination by an experienced inspector. Many transportation agencies in the U.S.A. have been using UAVs for applications ranging from the research stage to regular inspection activities. These applications are mainly focused on inspecting infrastructure assets such as pavements, bridges, substructures, railways, and other assets. The current research discussed the approaches followed to conduct aerial condition monitoring of transportation infrastructure assets using optical sensors mounted on UAVs. Qualitative and quantitative inspections of pavement, bridge, and substructure infrastructure case studies were performed. Pavement distress extents were measured in three dimensions and a 360° inspection of bridges was conducted by accessing hard-to-reach areas. Scaled views of the four sides of a bridge were developed and the challenges in data collection and processing were outlined. Further, a localized inspection was also demonstrated to show the feasibility of using photogrammetry for remote condition assessments post-disaster/emergency. Overall, the rich visualization, safety, flexibility, and ease of handling offered by these technologies are expected to transform the method of conducting infrastructure performance monitoring inspections in the future.

Road Transportation and Energy Integration Strategy in China

Transportation and energy are crucial for social development and civilization evolution. The energization of transportation infrastructure assets and clean transformation of transportation energies are effective decarbonization strategies for solving severe challenges such as resource shortage, climate change, and environmental pollution. This study first reviews the integration history of road transportation and energy, and investigates the demand for and trends of the integration from the perspectives of national energy security, clean transformation of energy supply, energization potential of transportation infrastructure assets, and intelligentization of the transportation system. The pathway for promoting the integrated development of road transportation and the energy sector is proposed based on assessing the wind and solar resources and the self-consistent supply capacity of energies within the road transportation system. Moreover, a roadmap for integrating road transportation and renewable energies is proposed considering the carbon peak and carbon neutralization targets. Finally, suggestions are proposed from two aspects. In terms of technology development, we suggest that China should enhance technology research and development, improve the industry chain, and promote technology innovation and integration. Additionally, it should provide policies for improving top-level design and strategic planning, exploiting new market modes, and strengthening international cooperation.

Procuring urban rail transit infrastructure by integrating land value capture and public-private partnerships: Learning from the cities of Delhi and Hong Kong

Worldwide, the private sector has actively participated in delivering transport infrastructure assets in cities. Typically, Private Participation in Infrastructure (PPI) approaches have been used to deliver urban rail transit (URT) systems. Public-Private Partnership (PPP) agreements or variants thereof have been the procurement method of choice by governments. However, PPPs have been unable to provide governments with value for money and concessionaires with sufficient operating revenues. The corollary, in this case, has meant that governments have been drawn to consider alternative forms of financing to procure and support the economic viability of a city's URTs. Land value capture (LVC) has been identified as a potential finance mechanism that can be integrated with a PPP to ensure its economic success. There are, however, only a limited number of cities that have integrated LVC with a PPP to successfully their URT projects. In this paper, an exploratory case study approach is used to learn from the experiences of Delhi Airport Metro Express and Hong Kong's Mass Transit Rail. Consequently, we develop a conceptual model that integrates land value capture and PPPs that can assist policy-makers with the procurement of their rail infrastructure. The implications of our developed model for procurement policy are presented.

Transport infrastructure asset resilience: Managing government capabilities

The management of the organisational capabilities needed to ensure the resilience of transport infrastructure assets is a challenge for governments worldwide. However, an absence of critical research in this area has exacerbated this challenge. The upshot, in this instance, has been the inability of governments to engender and enact an effective transport resilience strategy. This paper aims to fill this void and address the following research questions: (1) How do government organisational capabilities interact with one another to determine the resilience of transport projects? and (2) What is the best way to manage these organisational capabilities to aid a resilience strategy? Using Social Network Analysis, seven transport infrastructure case studies that were significantly impacted by natural hazards in the United Kingdom were used to answer the proposed research questions. The analysis revealed five inter-related factors are required to ensure the resilience of transport assets: (1) leadership; (2) reflexive (organisational) learning; (3) support from senior management; (4) a culture adept to resilience; and (5) continuous improvement (also investment) in asset absorbability, adaptability and vulnerability. The original contribution of this research is threefold: (1) a network providing a systematic visualisation of the interactions between organisational capabilities influencing asset resilience; (2) the prioritisation of governmental capabilities; and (3) the development of management framework providing a pathway that can accommodate environmental changes and asset resilience.

Transportation infrastructure asset management modeling using Markov decision process under epistemic uncertainties

Purpose Transportation infrastructure asset management has long been an active but challenging problem for agencies, which urges to maintain a good state of their assets but faces budgetary limitations. Managing a network of transportation infrastructure assets, especially when the number is large, is a multifaceted challenge. This paper aims to develop a life-cycle cost analysis (LCCA) based transportation infrastructure asset management analytical framework to study the impacts of a few key parameters/factors on deterioration and life-cycle cost. Using the bridge as an example infrastructure type, the framework incorporates an optimization model for optimizing maintenance, repair, rehabilitation (MR&R) and replacement decisions in a finite planning horizon. Design/methodology/approach The analytical framework is further developed through a series of model variations, scenario and sensitivity analysis, simulation processes and numerical experiments to show the impacts of various parameters/factors and draw managerial insights. One notable analysis is to explicitly model the epistemic uncertainties of infrastructure deterioration models, which have been overlooked in previous research. The proposed methodology can be adapted to different types of assets for solving general asset management and capital planning problems. Findings The experiments and case studies revealed several findings. First, the authors showed the importance of the deterioration model parameter (i.e. Markov transition probability). Inaccurate information of p will lead to suboptimal solutions and results in excessive total cost. Second, both agency cost and user cost of a single facility will have significant impacts on the system cost and correlation between them also influences the system cost. Third, the optimal budget can be found and the system cost is tolerant to budge variations within a certain range. Four, the model minimizes the total cost by optimizing the allocation of funds to bridges weighing the trade-off between user and agency costs. Originality/value On the path forward to develop the next generation of bridge management systems methodologies, the authors make an exploration of incorporating the epistemic uncertainties of the stochastic deterioration models into bridge MR&R capital planning and decision-making. The authors propose an optimization approach that does not only incorporate the inherent stochasticity of bridge deterioration but also considers the epistemic uncertainties and variances of the model parameters of Markovian transition probabilities due to data errors or modeling processes.

A multi-step assessment framework for optimization of flood mitigation strategies in transportation networks

Floods can lead to substantial direct damages to transportation infrastructure assets and cause significant indirect losses to communities that rely on the transportation network. This makes it crucial to understand the network's functional and operational deterioration caused by floods to help transportation agencies to either mitigate or plan for consequences of failures. To achieve this goal, this study uses an actual segment of a transportation network as a testbed to develop a network-level multi-step assessment framework to evaluate the flood-related risks and implement specific flood mitigation measures to the testbed. The multi-step framework first accomplishes assessments of the investment of pre-flood mitigation strategies, post-flood asset recovery cost, and indirect post-flood traffic delay and opportunity losses of the network. Then, a life-cycle benefit to cost analysis is applied to estimate the performance of the various flood mitigation strategies. This systematic effort is expected to provide a holistic overview of the flood risk to the network and a conservation-oriented means for optimizing and prioritizing mitigation investments while ensuring that the system resilience is sufficiently improved with respect to capability for absorbing flood hazards and adapting to partially-dysfunctional network operations during floods.

A procurement policy-making pathway to future-proof large-scale transport infrastructure assets

Governments worldwide have made a significant financial commitment to combat increasing traffic congestion and ageing transport networks over the next decade. However, large-scale transport projects are often late, over-budget, and below quality, making it difficult to future-proof assets and accommodate unanticipated changes. Evidence indicates that the traditional procurement model for large-scale projects used by Australian State Governments, for example, fails to deliver expected benefits. Markedly, a focused policy-making pathway is absent, especially for future-proofing these complex projects. Hence, the need to move away from a prevailing ‘understand, reduce, respond’ to a more adequate ‘understand, embrace, adapt’ attitude towards complexity and uncertainty in project procurement. The enabling functions of asset management, digitization, delivery, and finance might help. However, little is known about how they can coalesce to form a policy-making pathway to provide governments value for money outcomes and ensure assets are future-proofed. In this paper, we fill this void by reviewing the normative literature and proposing a conceptual approach. The issues we examine are of the utmost interest to governments worldwide as they grapple with designing, constructing, operating and maintaining transport assets that are both resilient to unexpected events and adaptable to changing needs, uses or capacities including climate change.

Life-cycle modelling of concrete cracking and reinforcement corrosion in concrete bridges: A case study

The development of effective life cycle management strategies for transport infrastructure assets is of importance for meeting the defined public policies and levels of service. In the last decades, much progress has been made in assessing the life-cycle performance of bridges using reliability-based approaches. However, the goal of developing a comprehensive life-cycle performance assessment framework for bridges has not been fully achieved. This is due to the uncertainties surrounding model parameters as well as the correlation between these parameters (e.g. the complex correlation between the reinforcement corrosion and the concrete cracking). It becomes more challenging due to the limited access to bridge inspection data by bridge research communities resulting from confidentiality issues. Using a typical highway concrete bridge as a case study, the present study systematically investigated the impact of concrete crack induced reinforcement corrosion on the serviceability of concrete bridges by developing an engineering reliability-based approach involving an auto-regressive crack propagation model and a steel corrosion prediction model. The model parameters were calibrated using the eight-year inspection data of an operating bridge. The influence of different external environments in the reinforcement corrosion, ultimately the residual life of the bridges, was also investigated through conducting a series of parametric studies. Based on the collected bridge inspection data, the model results predict that, although the surface crack of a RC bridge is repairable through periodic maintenance, the corrosion of the steel bars in the bridge still continues over time with a corrosion rate which depends on different maintenance intervention cycle periods (Tcycle). For example, reducing Tcycle from 12 years to 4 years could potentially prolong the service life of the bridge by around 15 years. The developed model could assist bridge managers to estimate the optimal Tcycle to prolong the service life of bridges.

A Road Map for Geotechnical Monitoring of Transportation Infrastructure Assets using Three-Dimensional Models Developed from Unmanned Aerial Data

Infrastructure forms the backbone of a nation’s growth and economy. The smooth operation of those infrastructure assets depends on many factors including proper use of construction materials under different loading, environmental and durability assessments, and the frequency of monitoring after construction. The performance and maintenance of an infrastructure asset thus depend on the behavior of the system in a built geological environment. Proactive monitoring of infrastructure often leads to preventive maintenance. However, it is not economically feasible to use the current traditional monitoring techniques, especially considering the vastness of the infrastructure networks. In this study, unmanned aerial vehicle–close-range photogrammetry (UAV–CRP) technology is being proposed as a supplemental data collection tool to complement existing traditional monitoring techniques for geotechnical infrastructure. Two case studies covering a pavement structure built over rehabilitated subgrade rich with sulfates and stability of a rock slope adjacent to an old rail line were monitored aerially to understand their state of health conditions. The pavement site had a history of experiencing sulfate-induced heaving and was rehabilitated using extended mellowing after lime stabilization. The rail line under inspection was constructed more than a century ago, and the stability of the weathered rock cut holds the key for safe operations on the rail line. The rock was highly weathered and considered to undergo circular failure. Aerial images were collected and processed to build three-dimensional models to evaluate and assess the condition of these geotechnical assets. This approach not only provides a comprehensive idea through dense point cloud models offering real-field-like view of the asset conditions but also is expected to result in significant savings in data collection time and costs.

Criticality analysis of a country’s transport network via an agent-based supply chain model

Building resilience against shocks has become a pillar of sustainability. By understanding how the different components of an economy interact in times of crisis, we can design resilience strategies that go beyond building walls or dams. We formulate an original agent-based model to explore a crucial pathway through which a disaster affects the economy: the transport–supply chain nexus. The model simulates the behaviour of firms facing transport and supply disruptions and estimates the resulting indirect losses. As an illustration, the model is used to assess the criticality of Tanzanian roads, which are vulnerable to floods. We report three main results. First, the model generates maps that identify the transport infrastructure assets that are most critical for specific supply chains: roads that are most important for food security are different from those supporting international trade, for instance. Second, economic losses from transport disruptions increase non-linearly with the duration of disruptions, highlighting the benefit from fast repairs. Third, by combining economic and transport modelling, we can consider a broader spectrum of interventions. Beyond strengthening the transport system, it is also possible to make supply chains more resilient to disruption with, for instance, sourcing decisions or inventory management. Assessing the performance of an economy in times of crisis is a difficult task. This study presents an agent-based model capturing the behaviour of firms facing transport and supply disruptions due to natural disasters, and shows possible paths to reinforce infrastructure and supply chain resilience.

Delivery of Transport Infrastructure Assets: Decision-Making Model to Ensure Value for Money

AbstractTransport infrastructure is pivotal for economic and social development. Over the past decade, Public-Private Partnerships (PPPs) have been widely adopted for its delivery in developing and...

A framework to guide strategic disinvestment in roadway infrastructure considering social vulnerability

Abstract Strategic disinvestment involves a conscious reallocation of funds to prioritize transportation infrastructure assets in terms of their importance or criticality, and then deliberately shifting limited resources away from lower priority assets toward higher priority assets. While previous investment paradigms considered the economic implications of expanding or maintaining assets versus the alternative of no expansion or no maintenance, disinvestment considers whether it is realistic to attempt to maintain all assets according to their original standards, or if performance standards should be changed or ownership transferred. While the concept of disinvestment has received attention in recent years, there is no established framework for disinvestment actions, potential travel impacts associated with disinvestment actions have not been widely examined, and it is unclear how disinvestment may affect socially vulnerable populations. In this paper, we present a novel framework to guide disinvestment decisions. The framework identifies different disinvestment actions and illustrates the savings, costs, and control tradeoffs associated with those actions. Each action offers different cost-savings and is associated with varying levels of reduced responsibility or obligation. We next introduce a measure of social vulnerability referred to as the Disinvestment Vulnerability Index (DVI) to help identify socially vulnerable populations impacted by disinvestment. We then propose a methodology for identifying a set of least critical or “non-essential” roadway assets as initial candidates for disinvestment, and introduce an approach for quantifying the potential effect of asset-specific disinvestment actions on vulnerable populations. The suggested approach allows decision-makers to “take the first step” in selecting an initial set of potential disinvestment candidates by identifying assets that have a minimal disruptive effect on network-wide travel, while simultaneously considering accessibility and mobility issues related to socially vulnerable populations. We illustrate the approach in the state of Vermont using the state’s travel demand model and data obtained from the U.S. Census.

Deep learning for bridge load capacity estimation in post-disaster and -conflict zones.

Many post-disaster and post-conflict regions do not have sufficient data on their transportation infrastructure assets, hindering both mobility and reconstruction. In particular, as the number of ageing and deteriorating bridges increases, it is necessary to quantify their load characteristics in order to inform maintenance and asset databases. The load carrying capacity and the design load are considered as the main aspects of any civil structures. Human examination can be costly and slow when expertise is lacking in challenging scenarios. In this paper, we propose to employ deep learning as a method to estimate the load carrying capacity from crowdsourced images. A convolutional neural network architecture is trained on data from over 6000 bridges, which will benefit future research and applications. We observe significant variations in the dataset (e.g. class interval, image completion, image colour) and quantify their impact on the prediction accuracy, precision, recall and F1 score. Finally, practical optimization is performed by converting multiclass classification into binary classification to achieve a promising field use performance.

Conceptual framework of life-cycle performance measurement: Ensuring the resilience of transport infrastructure assets

Having efficient and effective transport infrastructure (e.g. bridges, roads, railways, airways and tunnels) in place is essential for supporting the economic and social well-being of an economy. External disturbances that emerge as a result of climate change, for example, are impacting its performance. Delivering, managing and maintaining transport assets that are resilient and adaptive to changing environmental conditions have become a priority for many governments worldwide. Central to ensuring that transport infrastructure functions at their optimum and is resilient to external changes is performance measurement, as it enables those processes that need to be modified and improved for enhancing the asset’s adaptability throughout their lifecycle to be identified. Despite the importance of performance measurement in ensuring the resilience of transport infrastructure, it has received limited attention by governments in their policy making. Therefore, this paper provides a review of the extant literature and proposes a life-cycle resilient performance measurement framework (PMF) within transport context. The developed PMF is robust in comprehensively capturing the underlying perspectives that are significant for: (1) understanding the current state of the resilient level of transport assets; and (2) enabling a higher ability of the assets to adapt to environment-related changes in the future. The implications of the proposed framework for transport policy development are also discussed in this paper.

Karst Sinkhole Detecting and Mapping Using Airborne LiDAR - A Conceptual Framework

Sinkholes cause subsidence and collapse problems for many transportation infrastructure assets. Subsequently, transportation infrastructure management agencies dedicate a considerable amount of time and money to detect and map sinkholes as part of their asset management programs. Traditionally, sinkholes are detected through area reconnaissance, which includes visual inspection of a site to locate existing sinkholes or device inspection of a site to locate potential sinkholes or previously filled sinkholes. Another method for sinkhole detection is through a review of maps such as geological maps. These methods are expensive, time-consuming, and labor-intensive. Recent advances in remote sensing, especially airborne light detection and ranging (LiDAR), allows for the examination of the change in the Earth's surface elevation accurately and rapidly. The focus of this study is to develop a conceptual framework for sinkhole detection and mapping with airborne LiDAR. This conceptual framework lays the foundation for the future application of airborne LiDAR for sinkhole detection and mapping.

Total system error analysis of UAV-CRP technology for monitoring transportation infrastructure assets

Infrastructure forms the backbone of a nation's economy, and there is a growing demand for a technology that offers quick, safe, and reliable infrastructure performance monitoring data, which would lead to proactive maintenance or repairs of the infrastructure to ensure its quality performance as per the original design. The increase in the applications of unmanned aerial vehicles (UAVs) or drones for such monitoring purposes have been propelled by advancements in aerial vehicle platforms and image-analyses software. Evaluating UAVs for high-quality infrastructure-distress data monitoring has been a major focus for this research. Photogrammetry is the science of measuring distances from two or multiple images of an object and close range photogrammetry (CRP) can be accomplished by using an optical camera mounted on a UAV platform and taking the images of the infrastructure object. This technology, hereafter termed as UAV-CRP technology, utilizes UAVs coupled with high quality camera lens and a global navigation satellite system (GNSS) device to collect high quality data with fewer ground control points (GCPs). The various pieces of equipment need to have their aerial technology compatibility tested in different environmental conditions and evaluate the imagery that is used for image analyses and interpretations. A comprehensive set of calibration checks were executed using the total system comprising of UAV and accessories to evaluate the imagery quality level of transportation and geotechnical infrastructure data. All the checks performed ensured that the data obtained from the UAV-CRP technology meets the quality requirements for infrastructure data. A case study of pavement infrastructure data was provided to validate the high accuracy UAV-CRP data obtained following the guidelines developed in this paper.