Minnesota Department of Transportation

Low-temperature cracking is a significant distress for asphalt pavements in cold climates. To evaluate the low-temperature cracking resistance of asphalt mixtures, the Semi-Circular Bending (SCB) and the Disk-shaped Compact Tension (DCT) tests are commonly used. However, both tests have their limitations. These include the relatively short ligament length and long testing duration in the SCB, and the complexity of specimen preparation associated with DCT. Moreover, both tests require low-temperature conditioning, which further increases testing time and costs. This study investigates the Simplified Wedge Splitting Test (SWST), designed to evaluate the low-temperature cracking potential of asphalt mixtures. This test aims to use gyratory compacted specimens prepared with minimal cutting effort, while ensuring a sufficient fracture ligament. It also aims to replace the low-temperature conditioning with a faster loading rate at room temperature through Time-Temperature Superposition Principle (TTSP). To this end, SWST experiments were conducted using different ligament lengths and loading rates, and a Finite Element (FE) model was developed with a cohesive zone fracture model to numerically evaluate the SWST test parameters. Experimentally determined SWST fracture properties show sensitivity to the fracture ligament length, with modeling results identifying 75 mm ligament length as optimal. FE modeling at different loading rates shows that linear viscoelasticity cannot fully capture the effect of loading rate on asphalt fracture, limiting the ability of TTSP to provide the required temperature-loading rate correlations. Thereafter, a framework with initial TTSP correction factors is proposed based on the modeling results.

A landslide is a geo-hazard that occurs due to the dislocation of soil or rock mass from the parent mass in the direction of gravitational force. More than 6.7 million is spent annually in the Midwest region on infrastructural repairs due to slope failures and landslides. Identifying landslide-susceptible zones can improve urban management and help prioritize areas requiring immediate remediation. However, no full regional-scale landslide susceptibility (LS) map is available for Minnesota (MN), which is a formerly glaciated region. Hence, this study addresses this gap by leveraging machine learning (ML) and deep learning (DL), combined with explainable artificial intelligence (xAI), to develop a high-resolution LS map. Additionally, the study demonstrates how counterfactuals can be used as a preliminary tool for identifying mitigation measures. Five quantitative methods, namely, Logistic regression, Random Forest (RF), Multi-layer perceptron, TabNet, and TabKANet, were trained on a balanced dataset of landslide and non-landslide points. The total dataset was divided into training (70 The graphical abstract illustrates a workflow in which explainable artificial intelligence (xAI) serves as the central component of a clear, trustworthy landslide susceptibility (LS) map for Minnesota, a state that faces recurring slope failures. However, there is no full-scale LS available for the state. To address this gap, data on landslide causative factors were collected, along with a comprehensive landslide inventory compiled from various sources, and these were used to build a high-resolution LS map. Five machine learning (ML) and deep learning (DL) methods, namely, logistic regression (LR), random forest (RF), multi-layer perceptron (MLP), TabNet, and TabKANet, were trained for LS modeling. TabKANet, a modified version of TabNet, was used for the first time in the literature to develop an LS map. The obtained results highlight the excellent performance of RF and TabKANet during validation and field verification. To enhance the interpretability of the LS map produced using black-box ML and DL methods, xAI, specifically SHapley Additive exPlanations (SHAP), was used. The SHAP analysis shows that slope angle and elevation of the area were the top influencing factors. Furthermore, for the first time, the present analysis uses counterfactuals in landslide analysis to explore mitigation measures that can be adopted in the field to stabilize a landslide-prone area. Overall, the study not only delivers Minnesota’s first detailed LS map but also introduces an xAI-centered framework that can help state and federal agencies make informed decisions when dealing with potentially catastrophic landslide hazards. Developed the first statewide landslide susceptibility (LS) map for Minnesota. Utilized Explainable Artificial Intelligence (xAI) to understand the LS results. SHAP analysis shows slope angle and elevation as key LS contributors in Minnesota. Validated LS maps with field data and generated local SHAP-based site insights. Counterfactuals guided informed decisions for stabilizing unstable slopes.

Aggregate base course is the primary load-bearing layer beneath paved surfaces, while the subbase improves load distribution and provides separation above the subgrade. Because aggregate quality varies widely in type, gradation, shape, texture, angularity, and durability, understanding how these properties influence pavement performance is essential. The resilient modulus (MR) is a key mechanistic parameter describing the recoverable response of unbound aggregates under repeated loading, yet laboratory MR testing is costly and time-consuming. This study developed multilinear regression (MLR) models to relate aggregate properties to a consistent set of MR test results using Minnesota Department of Transportation’s Aggregate Index Property database, compiled from the Aggregate Source Information System and Laboratory Information Management System. Recognizing the significant influence of particle shape characteristics beyond gradation alone, the study also incorporated imaging-based indices of shape, texture, and angularity into an artificial neural network (ANN) model. The ANN was used to evaluate how a comprehensive set of aggregate properties affects the accuracy of predicted MR model parameters. The ANN outperformed the enhanced MLR model in predicting the k1, k2, and k3 parameters, capturing complex nonlinear relationships. These findings highlight the potential of ANN-based surrogate models to reduce reliance on extensive laboratory testing and improve pavement material characterization.

Engineered water repellency (EWR) using organosilanes provides a promising approach for mitigating frost heave by preventing ice lens formation. This study evaluates the environmental and economic impacts of typical flexible pavement structures used in Minnesota, as well as three EWR-treated variants. Primary data for the life cycle assessment (LCA) were collected from the Minnesota Department of Transportation (MnDOT) and Minnesota Road Research Facility and analyzed with the Federal Highway Administration LCA PAVE tool. The life cycle cost analysis (LCCA) was performed with the MnDOT tool to calculate the net present value per the ISO 15686-5 standard. The MnDOT soil replacement method (SRM) with EWR emerged as the most sustainable and cost-effective, showing a 23% reduction in global warming potential compared with the traditional SRM. The primary sources of emissions and expenses were hot mix asphalt activities, accounting for 58%–71% of the total. The LCA contributed less than 8% to the overall integrated cost, underscoring the predominance of LCCA in decision-making. Further field evaluations are recommended to confirm the long-term performance of EWR technologies and to optimize their integration into pavement design.