Federal Highway Administration

Unmanned Aerial Vehicles (UAVs) have gained widespread popularity as an efficient means for remote sensing, asset management, and reality modeling. In the realm of geotechnical applications, the accuracy of measurements obtained through UAVs is critical for assessing practical parameters such as soil layer thickness in earthworks and soil compaction projects. Several factors, including drone and camera quality, flight altitude, flight path, georeferencing methodology, number of photographs, and environmental conditions, can impact measurement accuracy. Potential dynamic movements of the drone during photo capture introduce variability (relative to photographs taken from a fixed camera position) at each point along the flight path, leading to potential discrepancies in results, as recorded images are influenced by environmental factors such as lighting, wind, and dust. This study aims to quantify the differences between two general approaches to image acquisition: (1) periodic stop-motion photography taken during a drone flight with single photographs taken at specified intervals and (2) image analysis of photographs extracted from continuous-feed video recorded by the drone during continuous flight. To assess the two image capture techniques, aerial surveys of check points in known configurations were performed, with ground control points also being placed at predetermined locations within the survey area. Traditional survey methods were employed to georeference the coordinates of the ground control points. Approximately 400 photos were captured under “nearly fixed” (relatively stable) positional states, with an equivalent number of images extracted from the continuous video feed at similar corresponding locations. The coordinates of the checkpoints were obtained using reality modeling software. The findings from this study offer valuable insights into the comparative accuracy of these two image acquisition approaches and also shed light on the influence of various video capturing techniques on measurement accuracy.

New luminescent coatings are increasingly being used in highway tunnels to address inadequate internal lighting conditions. However, there is currently a lack of scientifically reliable methods to evaluate the effectiveness of these paints in improving lighting conditions, reducing driver psychological stress, and quantifying these impacts. This study utilized new luminescent coatings to improve tunnel lighting conditions, conducting real-vehicle tests to measure drivers’ physiological parameters including pupil diameter and heart rate. It examined the mechanisms through which variations in lighting conditions within highway tunnels affect the psychological workload of drivers. A hierarchical analysis–fuzzy comprehensive evaluation (AHP-FCE) method was adopted to develop a quantitative evaluation system for highway tunnel driving psychological load. The results indicate that variations in tunnel lighting substantially influence the psychological workload experienced by drivers during operation. The new luminescent coatings effectively enhanced tunnel lighting conditions, increasing average brightness and illuminance by 37.71% and 40.95%, respectively. Following lighting improvements, pupil diameter variation rates during tunnel entry and exit phases decreased by 35.37% and 10.06%, respectively, while heart rate variation rates decreased by 12.50% and 4.36%. Quantitative analysis of driver mental load revealed a comprehensive score of 0.6230 before lighting enhancement, which decreased to 0.2702 after improvements. This research introduces an innovative integrative framework that combines physiological parameter monitoring with the AHP-FCE method to quantitatively assess the psychological workload experienced by drivers in tunnel environments. This approach addresses a significant gap in the literature concerning the quantitative relationship between tunnel lighting optimization and drivers’ psychological workload responses.

Continuous Compaction Control (CCC) enables near-continuous monitoring of the compaction process during earthwork construction by instrumenting a compaction roller with sensors. One of the most widely used CCC measurements is the Compactometer Value (CMV), due to its historical significance and relatively straightforward calculation procedure, which involves performing spectral analysis of the compaction drum’s vibratory response. In practice, CMV measurements from commercial CCC systems are typically black box in nature, as they report only the final values—without disclosing the raw acceleration data or the calculation steps performed by the CCC system. As a result, most CCC technologies remain closed and proprietary. This paper presents a methodology to independently calculate CMV using data collected from a compaction roller equipped with both an aftermarket CCC retrofit kit and a secondary accelerometer. This setup allowed for direct comparisons between independently measured CMV values and those reported by the CCC retrofit kit. Data was collected from the construction of a full-scale earthen embankment compacted in multiple lifts. Results from this study showed that independently measured CMV values were within 1% of CMV values measured from the retrofit kit. These results provide a practical, transparent workflow for researchers and practitioners interested in replicating CMV measurements, ultimately contributing to the development of more open-source CCC solutions. • Compactometer value (CMV) was independently calculated during soil compaction. • Independent CMV values were compared against CMV values measured from a CCC system. • Independent CMV values were consistent with commercially measured CMV values. • Results from study may help increase the adoption rate of CCC technologies.

This study addresses the critical skills gap between Technical and Vocational Education and Training (TVET) systems and industry needs by proposing a conceptual framework for Public-Private Partnerships (PPPs) in TVET. Despite TVET's potential to enhance employability, many systems—particularly in developing economies—struggle with outdated curricula, inadequate infrastructure, and weak industry linkages. Through a mixed-methods approach combining literature review, case studies (e.g., Malaysia’s Penang Skills Development Centre and Bangladesh’s SEIP program), and stakeholder interviews, the study identifies key mechanisms for effective industry–academia collaboration: joint curriculum design, shared infrastructure, faculty exchanges, and standardized apprenticeships. Findings reveal that institutionalized PPPs significantly improve graduate employability (e.g., SEIP’s 70% placement rate) but face challenges like cultural mismatches and funding asymmetries. The proposed framework integrates governance, resource-sharing, and monitoring components to align TVET with labor market demands. Recommendations include policy incentives for private-sector engagement, phased pilot testing, and robust M&E systems. This research contributes a scalable model for bridging the skills gap while emphasizing contextual adaptation for low- and middle-income settings.

Highway bridges play a crucial role in transportation infrastructure, but their construction and operation also have significant environmental impacts. To achieve sustainable development, it is essential to account for pollution and carbon emissions and evaluate the effectiveness of emissions reduction measures. This paper presents a comprehensive analysis of the current status and challenges in pollution and carbon emissions accounting, as well as emissions reduction effect evaluation for highway bridges. An assessment framework for pollutants and greenhouse gases is established. Case studies reveal that raw material production contributes significantly to total emissions, accounting for approximately 75.7% (after normalization). Among greenhouse gases, CO2 emissions dominate, accounting for about 45.3% of normalized emissions. Various optimization strategies are proposed and evaluated. The integrated application of these strategies can lead to a pollution and carbon reduction effect of approximately 3% (after normalization) of total emissions across different emissions reduction scenarios.

The chapter discusses the problem of determining liability for harm caused by self-driving cars. The article examines the legal relations that arise in connection with the infliction of harm by autonomous vehicles, as well as possible mechanisms for the distribution of responsibility between the participants in these relations. The relevance of the study is due to the rapid development of autonomous technologies and the simultaneous lack of clear legal regulatory mechanisms. An analysis of current approaches to the allocation of responsibility between manufacturers, owners and users of unmanned systems is proposed, as well as possible ways to address the identified legal gaps.

Abstract Signal noise removal is an indispensable and critical procedure in obtaining clean pavement texture data for reliable pavement evaluation and management. Nevertheless, the presently established denoising approaches to pavement texture data still rely on traditional techniques that have long struggled with removing noise accurately and consistently. This paper innovatively initiates a one‐dimensional (1D) residual denoising network (R1DNet) to achieve the noise removal of 3D pavement texture data. R1DNet is proposed as a 1D architectural encoder–decoder that considers the unique characteristics of 3D texture data from 3D laser imaging technology. The encoder extracts diverse profile features of input noisy texture data through two favorably developed 1D modular structures: a cascade deep convolutional module and a parallel multi‐scale attention module. The decoder gradually parses the extracted profile features and estimates noise, with which the clean texture data are obtained based on a simple subtraction operation. The architecture of R1DNet is determined to be optimal in both accuracy and efficiency, using a customized performance‐balancing evaluation function. For model development in a supervised manner, a systematic labeling method is specifically developed, which can build the baseline clean texture data from real 0.1 mm noisy 3D texture data. The experimental results show that the proposed R1DNet can effectively eliminate noise and produce clean texture data closely matching the baseline, presenting significant improvements in accuracy and consistency, compared to the traditional denoising methods.

The control and monitoring of in situ soil moisture content during the soil compaction process is of critical importance. It has been well established that compacting soils at or near their optimum moisture content enables them to be compacted in an ideal fashion, thus improving their associated engineering behavior (e.g., bearing capacity, shear strength, permeability). Within the United States, the Nuclear Density Gauge (NDG) has become the “gold standard” for monitoring soil in situ unit weight and moisture content during the construction process for earthwork quality assurance/quality control (QA/QC) purposes. However, its radioactive levels necessitate strict protocols for the operation, transport, and storage of the gauge. To address these challenges, the “Egauge,” a hybrid nuclear-electrical density gauge with lower radioactive levels, has been developed. Previous research has shown that the EGauge is able to produce nearly identical wet unit weight measurements relative to the NDG for the same soil type. However, previous research conducted by the authors has shown that the EGauge systematically underestimates soil moisture content relative to the NDG in some cases. Consequently, research is needed to develop soil-specific calibration models for the EGauge to improve its accuracy in estimating soil moisture content in situ, thus ensuring it can serve as a reliable replacement for the NDG. Within this study, soil-specific calibration models for three different soil types were developed using the machine learning algorithm Multi-Gene Genetic Programming (MGGP). The development of these models greatly enhanced the EGauge’s ability to estimate soil moisture content in situ, as each model developed within this study was able to estimate soil moisture content within 1.5% or less relative to representative oven-dried samples.

Sea level rise and seawater intrusion induced by climate change increase soil pore water salinity in coastal regions. This can affect soil compressibility and shear strength, especially for fine-grained soils. One of the main parameters to characterize the shear strength of soils is residual shear strength, which is essential for analyzing the stability of active landslides. In the US, this parameter is typically measured using a Bromhead ring shear device. However, soil extrusion during tests using this device causes wall friction, leading to the overestimation of shear strength. To address this issue, two Bromhead ring shear devices with modified beveled top platens were tested. Results showed that the beveled platen significantly reduced wall friction, providing more accurate residual shear strength measurements. This study also investigated the effect of pore water salinity increase on the kaolinite’s residual strength. The results showed that an increase in salinity from freshwater to seawater levels did not substantially affect the kaolinite’s residual shear strength.