Abstract Gravel, unsurfaced, or unpaved road networks account for 35% of the total roadways in the US, with some states such as Iowa having about 60% of their roadways unpaved, and regular monitoring is required to maintain and extend their service life. Among the major types of gravel road distress, dust clouds from passing traffic can cause significant environmental problems, impair visibility, and pose health risks to human respiratory systems. Popular technical manual for unsurfaced road maintenance management typically recommend assessment of dust-severity levels based on the severity of a dust cloud produced by a vehicle driving at 40 km / h ( 25 mi / h ). Because this method exposes assessors to unsafe conditions, its use is impractical for long road networks and high-dust severity conditions, so there is a need to explore automated and remote-sensing techniques for rating dust severity. Although previous studies have employed image-processing techniques to assess dust levels from images captured by vehicle-mounted cameras, dust accumulation on windshields and unstable camera attachments have reduced the reliability of such data collection. To address these limitations, this paper investigates the application of small uncrewed aircraft systems (sUAS) in rating dust clouds generated by traffic at various speeds. For this experiment, we collected dust data using sUAS from three gravel roads in Iowa and applied deep-learning models, specifically You Only Look Once version 8 large (YOLOv8l), to detect the truck driven for dust activation and residual network (ResNet) models to classify the level of dust severity in each sUAS video frame. The ResNet-152 classification model achieved the best result on the test data set with a weighted F1-score of 0.9867, highlighting a promising outcome for adoption of sUAS for dust monitoring.
Abstract Bridge maintenance cost is not an isolated variable but a constructed outcome derived from damage size, repair method, and unit cost. However, existing data-driven approaches often treat cost estimation as a black-box prediction problem, providing only aggregated cost values, without transparent and granular information. To address this knowledge gap, this study proposes a multistage cost estimation framework that explicitly structures maintenance cost estimation according to the fundamental construction management logic of quantity–method–price. The proposed framework sequentially estimates repair-required damage size, classifies appropriate repair methods, determines corresponding unit costs, and integrates these components to total maintenance cost. An extreme gradient boosting–based pipeline was developed using 3,716 inspection records from the Korean Bridge Management System and validated with 100 real-world bridge maintenance records. The results show that the proposed framework reduces the mean absolute error by 41.3% and 73.7% compared to conventional direct cost estimation models. By aligning data-driven modeling with the maintenance cost formation logic, this study advances bridge maintenance with more transparent and granular cost estimation.
Abstract Airports face challenges in integrating sustainability and resilience into their long-term operational plans due to climate change, regulatory mandates, financial constraints, and disruptions such as the COVID-19 pandemic. While technology adoption plays a key role in addressing these issues, many airports lack structured strategies for evaluating, selecting, and implementing sustainability and resilience-focused innovations. Technology is often adopted reactively, driven by crises or regulatory compliance, rather than through proactive, strategic planning. This study introduces an empirically supported decision-support framework that combines established technology adoption models, sustainability and resilience assessment logic, and airport-specific implementation needs into a structured process for sustainable and resilient technology adoption. The framework synthesizes engineering-based maturity assessment, strategic planning, cross-disciplinary implementation, and Envision-based sustainability and resilience criteria, providing a tool that merges engineering judgment with organizational, regulatory, and environmental considerations. It equips airport decision-makers with a practical methodology for aligning technological innovation with operational performance, sustainability objectives, and long-term adaptability. The framework was empirically supported through expert input and statistical analyses of survey responses representing 27 commercial airports across the United States. An illustrative case study demonstrates an application of the framework by translating strategic sustainability and resilience goals into actionable implementation steps. This study contributes to the body of knowledge by presenting an evidence-based decision-support methodology that bridges the gap among strategic commitments and implementation, supports decarbonization, operational resilience, and infrastructure sustainability, while providing practical guidance for planners, sustainability leaders, and policymakers.
Abstract Amidst increasing need for efficiency gains and protection against vulnerabilities, the expression of infrastructure as digital twins is increasingly important to assess disruptions and planning. Water distribution networks (WDN) are one such infrastructure facing an increasing need for modernization and resilience. However, the dearth of high-resolution data on WDNs prevents robust simulations. This study explores the challenges and opportunities of synthetic water distribution models as a critical bridge to navigate the complexities of urban water networks. A synthetic infrastructure model is applied to San Juan and Mayagüez, Puerto Rico, to generate synthetic WDNs composed of reservoirs, pumps, links, and nodes, using the road network as a proxy for pipe location. The synthetic networks are then compared with true data from the Puerto Rico Aqueduct and Sewer Authority to assess their ability to reproduce key structural and hydraulic characteristics. Results show that the synthetic networks effectively capture large-scale network connectivity and spatial patterns, with moderate to strong correlations in pipe placement, nodal allocation, and pressure profiles. Challenges are apparent in synthesizing across urban and rural morphologies, with the synthetic models overestimating nodal density in rural areas while underestimating it in urban cores. Pipe diameter distributions also diverge, with synthetic models favoring smaller pipes, impacting redundancy and criticality assessments. Opportunities to improve synthetic models include integrating population-density based allocation strategies to influence looped versus branched structure, improved handling of reservoir connections, and refining hydraulic parameters to enhance realism. These findings demonstrate the potential of synthetic water distribution models as scalable tools for resilience analysis in data-scarce regions while highlighting strategies to improve the representation of sociotechnical complexities and hydraulic dynamics.
Abstract Longitudinal rail movement, or rail creep, creates maintenance and operation challenges in heavy axle load (HAL) freight corridors; it can contribute to track instability, gauge tightening, and changes in rail neutral temperature, for example. Rail creep is often described as directional net migration, but field observations show that longitudinal rail movement can also fluctuate cyclically. For this reason, longitudinal rail movement amplitude can be a useful metric to evaluate how much movement is allowed by the track’s longitudinal restraint system under given operational, geometric, and restraint-related conditions. This study presents a nonintrusive unmanned aerial vehicle (UAV)-based photogrammetric method to quantify relative longitudinal movement between rail and crossties over time. The method uses an optimization-based geometric correction leveraging track elements with dimensions approximately constant over time (i.e., tie plates, crossties, rail marks, and rail spacing), allowing geometric measurements to be extracted without ground control points. UAV surveys were conducted at discrete distances near 20 track structures, including grade crossings, turnouts, and open-deck bridges, across four US HAL corridors. The final data set included 764 crosstie observations collected three times at approximately 3-month intervals. Longitudinal rail movement amplitude was analyzed using a log-linear gamma regression model with cluster-robust inference to evaluate operational, geometric, and restraint-related variables. Results showed that structure type and distance from the structure, up to 152 m, did not have a consistent effect on movement amplitude after controlling for other factors. Directional traffic was associated with approximately 55% lower movement amplitude than bidirectional traffic; steeper grades were also associated with lower amplitudes. Crossties with two aligned, and two embedded anchors were associated with reductions in movement amplitude ( − 57 % , and − 31 % ) compared with nonanchored crossties, and a single loose anchor was associated with an increase in movement amplitude ( + 50 % ). These results can support rail restraint system design, inspection, and maintenance prioritization.
