• Weigh-in-motion data provides large quantity of traffic information for pavement design. • Reliability of pavement design highly depends on the quality of truck traffic data. • The weigh-in-motion recorded traffic data may not be sufficiently accurate for satisfactory pavement design. • The accuracy of weigh-in-motion traffic data can be improved through machine learning algorithms. The success of the mechanistic-empirical pavement design guide implementation depends largely on a high level of accuracy associated with the information supplied as design inputs. Truck axle load spectra play a critical role in all aspects of the pavement structure design. Inaccurate traffic information will yield an incorrect estimate of pavement thickness, which can either make the pavement fail prematurely in the case of under-designed thickness or increase construction cost in the case of over-designed thickness. The primary objective of this study was to create an accurate traffic design input module, and thus to improve the quality of pavement designs. The traffic input module was created with the most recent data to better reflect the axle load spectra for pavement design. The unclassified vehicles by weigh-in-motion devices were analyzed and a neural-network-model-based classification method was utilized to determine the appropriate allocations of unclassified vehicles to truck classes. The updated truck traffic information includes average annual daily truck traffic, truck volume monthly adjustment factors, truck volume lane distribution factors, truck volume directional distribution factors, truck volume class distributions, traffic volume hourly distribution factors, distributions of for single-axle, tandem-axle, tridem-axle, and quad-axle loads, average axle weight, average axle spacing, and average number of axle types.

This paper presents an effort to address urgent issues associated with semitruck platooning. The weigh-in-motion data were first examined to determine the characteristics of naturally formed semitruck platoons. A safety-based two-vehicle platoon model was then utilized to determine the critical headways for assessing the safety of semitruck platooning in various driving scenarios. The possibilities for passenger vehicles to cut into semitruck platoons were assessed using actual vehicle headway data. Finally, the potential impacts of semitruck platooning on typical interstate highway pavements were assessed using finite element analysis. Most semitruck platoons consist of two to three vehicles. An optimal platoon gap of 0.94 s was identified for connected and automated semitruck platooning. The potential impacts of truck platooning on concrete pavement were negligible for a platoon gap greater than 0.2 s. The maximum possible impact was less than 4% in terms of the service life reduction for interstate asphalt pavement.

Effective speed management in transition areas is crucial. Although numerous studies have proposed countermeasures to ensure driving safety, little research has been conducted on identifying effective and low-cost countermeasures for speed management when transitioning from rural roads to small towns. This study proposes two countermeasures: roadside vegetation and change in lane width and investigates the impact of these countermeasures on speed management performance in this context using a driving simulator experiment. Thirty participants completed eight scenarios, and countermeasures were evaluated based on stabilized speed, minimum speed, and in-town average speed. Results showed that stabilized speed and minimum speed decreased significantly in the combination of narrow lane and different vegetation designs compared to the baseline. Post-countermeasure in-town average speed didn’t decrease significantly in all scenarios. These findings suggest that roadside vegetations and narrow lane width can be effective for speed management in the transition from rural roads to small towns.

In this work, a field study was conducted to evaluate the curing effectiveness of concrete slabs cured by air and compound. Two non-destructive testing methods including ground penetrating radar (GPR) and electrical resistivity measurement were used. The dielectric constant value developments of air and compound cured concrete slabs were determined by GPR measurement. The compressive strength development was tested by concrete cylinders. The hydration degrees of air and compound cured slabs were determined by Ca(OH)2 (CH) content through thermogravimetric analysis (TGA) by a modified calculation method. The microstructure development of air and compound cured concrete slabs were characterized by mercury intrusion porosimetry measurement (MIP). It has found that the dielectric constant value of compound cured concrete slab was always higher than air cured concrete slab. The second reflected pulse peak amplitude of GPR waveform was less influenced by weather conditions, making it a reliable indicator of the dielectric constant value. On the other hand, the dielectric constant value determined by the first reflected pulse peak amplitude was greatly influenced by the weather conditions. The dielectric constant value determined by the two-way travel time method exhibited strong correlations with compressive strength, CH content and porosity. However, the electrical resistivity development of air and compound cured concrete slabs were not consistent with compressive strength, CH content and microstructure development. Our findings suggested that the GPR measurement can be practically used to evaluate the curing effectiveness of concrete and can be a good indicator of “hydration potential” from a development perspective.

Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and for the development of management strategies employed by state highway agencies. Traditionally, friction evaluations have been conducted on a case-by-case basis, focusing on specific road sections. However, this approach fails to provide a comprehensive assessment of friction conditions across the entire road network. This paper introduces a hybrid clustering algorithm, namely the combination of density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM), to perform pavement-friction performance ratings across a statewide road network. A large, safety-oriented dataset is first generated based on the attributes possibly contributing to friction-related crashes. One-, two-, and multi-dimensional clustering analyses are performed to rate pavement friction. After using the Chi-square test, six ratings were identified and validated. These ratings are categorized as (0, 20], (20, 25], (25, 35], (35, 50], (50, 70], and (70, ∞). By effectively capturing the hidden, intricate patterns within the integrated, complex dataset and prioritizing friction-related safety attributes, the hybrid clustering algorithm can produce pavement-friction ratings that align effectively with the current practices of the Indiana Department of Transportation (INDOT) in friction management.

Mixing cement with water in mass concrete production is associated with hydration heat generation, which may cause concrete mixes to dry out and crack. The experimental work aims to evaluate the effects of using liquid nitrogen (LN) in cooling mortar mixes and concrete. LN was used in doses of 0, 25, 50, 75 and 100% by water volume to either cool the mixing water at T = 4°C or to inject it into concrete mixtures directly. The concrete mix proportions were based on ACI PRC-207.1-21 for mass concrete. The mortars' initial and final setting times and temperatures indicated that a greater percentage of LN delayed the hardening process and the peak hardening rates, exhibiting slower cement hydration. LN cooling enhances the compressive strength of mortar and concrete cubes when VLN/Vwater≤1. However, concrete compressive strength decreased due to ice formation that turned into voids when VLN/Vwater>1. Cooling concrete with chilled water (T = 4°C) showed a 30% and 34.51% increase in mortar and concrete cubes' compressive strength, respectively, and a maximum of 16.45% and 12.97% when LN was injected into mortars and concrete mixes. Cooling the concrete with chilled water (T = 4°C) showed a slower cooling process, which improved cement hydration results.

Many agencies use Automated Traffic Signal Performance Measures (ATSPMs), which require vehicle detection and communication equipment, to evaluate traffic signal efficiency. ATSPMs usually rely on projections based on spatially limited vehicle trajectory samples to estimate performance. Recently, connected vehicle (CV) data has become available that provides entire vehicle trajectories that can be used to generate accurate signal performance measures without the need for projections or infrastructure upgrades. This paper analyzes over 50 intersections in Utah to evaluate how traditional detector-based arrivals on green (AOG) calculations compare with their CV-based calculations. The effects that saturation and queue- lengths have on estimations are analyzed. In general, there is close correlation between computations when queues are short and undersaturated conditions exist. However, if queues extend past the advance detector, detector-based calculations tend to overestimate AOG since vehicles are detected during green but may stop before. This impact is particularly large when the approach is oversaturated, which led to overestimations of around 40% in this study. In addition, detector-based estimations can underestimate AOG in undersaturated scenarios with short queues if vehicles reach the advance detector on red but reduce their speed afterwards, allowing them to not stop. In some cases, the detector-based technique underestimated AOG by over 50%. The findings can help practitioners understand how detector-based estimations vary by traffic conditions. This is particularly important as the industry moves toward a hybrid blend of detector- and CV-based signal performance measures. It is recommended that CV trajectories be used to measure AOG during periods with long queues, oversaturated conditions, or both.