Landscape Vehicular Anti-Ram (LVAR) systems are a group of protective barriers, which are designed using natural materials (e.g., boulders) and have proven to both effectively protect sensitive structures against threats and be aesthetically pleasing. This paper presents two consecutive vehicular crash tests hitting the same single boulder embedded in AASHTO coarse aggregate fill. A LS-DYNA model was developed to simulate the field-scale tests, which were instrumented with high-speed cameras and pressure cells. A readily available truck model from the National Crash Analysis Center was modified and implemented in the LS-DYNA model. The boulder and surrounding soil were modeled using the Mohr-Coulomb failure criteria. The model parameters were calibrated using results from the first field-scale test with a truck traveling at 48.3 km/hr (30 mph) impacting the LVAR system. The calibrated model was then used to simulate the second field-scale test, which involved a truck traveling at 80.5 km/hr (50 mph) impacting the same LVAR system without resetting the boulder or soil. The calibrated model was able to provide the global response of the system, including the time-history of the translational displacement and rotation of the boulder, and was in good agreement with field-scale test results. This suggests that the overall global response was dominated by the dynamic behavior of the truck and boulder system upon impact. Hence, a simple material model for soil and boulder is sufficient for simulating the tests conducted.

This study focuses on developing and examining the effectiveness of Transfer Learning (TL) for structural health monitoring (SHM) systems that transfer knowledge about damage states from one structure (i.e., the source domain) to another structure (i.e., the target domain). Transfer Learning (TL) is an efficient method for knowledge transfer and mapping from source to target domains. In addition, Proper Orthogonal Modes (POMs), which help classify behavior and health, provide a promising tool for damage identification in structural systems. Previous investigations show that damage intensity and location are highly correlated with POM variations for structures under unknown loads. To train damage identification algorithms based on POMs and ML, one generally needs to use multiple simulations to generate damage scenarios. The developed process is applied to a simply supported truss span in a multi-span railway bridge. TL is first used to obtain relationships between POMs for two modeled bridges: one being a source model (i.e., labeled) and the other being the target modeled bridge (i.e., unlabeled). This technique is then implemented to develop POMs for a damaged, unknown target using TL that links source and target POMs. It is shown that the trained knowledge from one bridge was effectively generalized to other, somewhat similar, bridges in the population.

Truck platooning—digitally linking two or more trucks to travel in a closely spaced convoy—is an emerging technology with the potential to save fuel and reduce labor. A framework is described to determine how much a platoon permit load might be increased above Federal Bridge Formula B legal limits, given strict control over the load characteristics and operational tactics. Soon, platoons are expected to advance not only with respect to traffic operations but also in their ability to weigh and report axle weight and spacing, functioning as mobile weigh-in-motion vehicles. Consequently, platoon live load statistics (bias and coefficient of variation) can differ from code assumptions, and are perhaps controllable, which poses a significant opportunity with respect to operational strategies. A parametric study is presented that examined safe headways between platooning trucks, considering different girder spacings, span lengths, numbers of spans, types of structure, truck configurations, numbers of trucks, and adjacent lane loading scenarios. The Strength I limit state was evaluated for steel and prestressed concrete I-girder bridges optimally designed using load and resistance factor design. Reliability indices, β , were calculated for each load case based on Monte Carlo simulation. Summary headway guidance was developed and is presented here to illustrate potential safe operational strategies for varying truck weights and platoon live load effect uncertainties.