Landslide Risk Assessment in Cut Locations Using Artificial Intelligence Based on Right-of-Way Videos and Geophysical Data

69A3551847103Sidehill and through cuts are often used in the construction of new railroad rights-of-way to limit the length, curvature, and grade of the route. However, rights-of-way that utilize cuts are susceptible to damage from falling debris driven by slope failure events such as shallow landslides and rockfalls. At-risk slopes, or geohazards, are traditionally analyzed using intensive field investigations and historical failure events to determine their likelihood of failure and the potential consequences of failure. Anticipating slope failures that may occur due to everyday weather events and other catalysts in the region helps protect railroad assets and employees, ensuring safe operations. Many rights-of-way have a large density of geohazards; thus, performing in-situ measurements to determine their failure likelihood requires extensive resources. In addition, installing infrastructure to detect or inhibit debris flow is expensive and often unrealistic for all geohazards. This study aimed to create a new slope stability risk framework for railroad cut sections by processing digital images of railroad rights-of-way recorded by inspection vehicles and related geophysical data. A geohazard-affected track section along the Harrisburg Line was used as the study area. Computer vision techniques were used to identify and quantify geohazard features that indicated slope instability. An object detection model based on deep learning (DL) was trained to detect these slope instability indicators and generate risk scores from rights-of-way inspection videos. Moreover, a landslide inventory was compiled, and a landslide susceptibility model was developed for the study area based on available geophysical data. The object detection model and the landslide susceptibility model were combined using a relative risk assessment framework to determine which sections were most at-risk of landslide, and results were compared with the railroad identified geohazard sections across the study area

Enhanced Moisture Sensitivity Study

0092-18-06The moisture damage susceptibility of four different asphalt concrete mixes from Wisconsin, two with dolomite aggregates and two with siliceous aggregate, was evaluated through laboratory and conditioning testing. One mix was considered a good performing mix and the other three with poor or marginal performance with respect to moisture damage resistance. Moisture conditioning followed existing standards and included freeze/thaw, suction/pressure through Moisture-induced Stress Tester (MiST), and simultaneous effect of water and loading using Hamburg Wheel Tracking Device. Wheel tracking was also conducted on specimens after conditioning with MiST as well as specimens maintained dry without water conditioning. The tests to measure mix properties followed existing standards or provisional standard protocols and included indirect tensile strength test, indirect tensile modulus test, Hamburg wheel tracking test, and ultrasonic pulse velocity test. Based on the results, the laboratory performance of the mixes was ranked with respect to moisture damage susceptibility, and a set of recommendations for establishing pass/fail criteria was developed and presented. MiST conditioning is recommended in case the intention is to evaluate the effect of moisture conditioning on engineering properties of the asphalt mix and when the changes in dynamic modulus of the asphalt mix is intended. Investigating the change in asphalt concrete modulus as a result of moisture damage is of interest as it is one of the major input parameters to the most recently developed performance prediction models. Recommendation is made to use Hamburg wheel tracking for at least medium to high traffic volume roads and continue using AASHOT T 283 for lower volume roads. Recommendation is also made to include criterion on minimum required wet strength in cases where AASHOT T 283 is utilized. This criterion is in addition to the existing criterion on minimum indirect tensile strength ratio (TSR) of the mix