Blowup of a Concrete Pavement Adjoining a Rigid Structure

DTFH61-88-P-00820The main cause of concrete pavement blowups are axial compression forces induced into the pavement by a rise in temperature and moisture. Recent analyses by this writer and his students were based on the notion that blowups are caused by lift-off buckling of the pavement. The cases analyzed were: (1) continuously reinforced concrete pavement and (2) concrete pavement weakened by a transverse joint or crack. The present paper contains an analysis of another case, when a long continuously reinforced concrete pavement adjoins a rigid structure, like a bridge abutment. The analysis is similar to the ones described above. The resulting formulation is non-linear and is solved exactly, in closed form. The obtained results are evaluated numerically and are compared with those of a long continuously reinforced pavement, in order to show the effect of the rigid structure on the pavement response

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

Development of a Practical Risk Framework for Railway Bridge Stiffness Transitions

69A3551847103The objective of this research was to take advantage of historic measurement cycles to develop a risk index for bridge transitions that takes into account the stiffness differential (mean stiffness between zones), stiffness variation (variation around the mean), train axle load, train operating speed, rate of degradation of the transition zone, length of the bridge, and other factors. The data utilized to achieve this objective were vertical track deflection data and railway operating data for approximately 500 miles of railway with nearly 100 bridges. This research activity resulted in a framework for practically implementing a risk index for bridge transitions that allows the railway to prioritize bridges for maintenance and/or remedial action as well as monitor the health of bridge transitions. In addition, the resulting framework has the ability to identify the most cost-effective approach to managing the bridge transitions, status quo maintenance, implementation of a transition zone (and best approach), or matching stiffness through the bridge. Since the data utilized were very well known to the railway, and the resulting outcome is an easy-to-use and practical framework, it is expected that acceptance by industry partners will be timely and well received