Algorithm to Estimate the Capacity Reserve of Existing Masonry Arch Railway Bridges

Most railway masonry arch bridges were designed according to codes that predate the 1950s; therefore, assessing their load-carrying capacity to comply with current codes is of the utmost importance. Nonetheless, acquiring the necessary information to conduct in-depth analyses is expensive and time consuming. In this article, we propose an expeditious procedure to conservatively assess the Load Rating Factor of masonry arch railway bridges based on a minimal set of information: the span, rise-to-span ratio, and design code. This method consists in applying the Static Theorem to determine the most conservative arch geometry compatible with the original design code; assuming this conservative geometrical configuration, the load rating factor, with respect to a different design load, is estimated. Using this algorithm, a parametric analysis was carried out to evaluate the Load Rating Factor of old arch bridges in respect of the modern freight load of the Trans-European Conventional Rail System, for different spans, rise-to-span ratios, and original design codes. The results are reported in easy-to-use charts, and summarized in simple, practical rules, which can help railway operators to rank their bridges based on capacity deficit.

Sensor data-based probabilistic monitoring of time-history deflections of railway bridges induced by high-speed trains

Structural condition monitoring of railway bridges has been emphasized for guaranteeing the passenger comfort and safety. Various attempts have been made to monitor structural conditions, but many of them have focused on monitoring dynamic characteristics in frequency domain representation which requires additional data transformation. Occurrence of abnormal structural responses, however, can be intuitively detected by directly monitoring the time-history responses, and it may give information including the time to occur the abnormal responses and the magnitude of the dynamic amplification. Therefore, this study suggests a new Bayesian method for directly monitoring the time-history deflections induced by high-speed trains. To train the monitoring model, the data preprocessing of speed estimation and data synchronization are conducted first for the given training data of the raw time-history deflection; the Bayesian inference is then introduced for the derivation of the probability-based dynamic thresholds for each train type. After constructing the model, the detection of the abnormal deflection data is proceeded. The speed estimation and data synchronization are conducted again for the test data, and the anomaly score and ratio are estimated based on the probabilistic monitoring model. A warning is generated if the anomaly ratio is at an unacceptable level; otherwise, the deflection is considered as a normal condition. A high-speed railway bridge in operation is chosen for the verification of the proposed method, in which a probabilistic monitoring model is constructed from displacement time-histories during train passage. It is shown that the model can specify an anomaly of a train-track-bridge system.

Graph SLAM-Based 2.5D LIDAR Mapping Module for Autonomous Vehicles

This paper proposes a unique Graph SLAM framework to generate precise 2.5D LIDAR maps in an XYZ plane. A node strategy was invented to divide the road into a set of nodes. The LIDAR point clouds are smoothly accumulated in intensity and elevation images in each node. The optimization process is decomposed into applying Graph SLAM on nodes’ intensity images for eliminating the ghosting effects of the road surface in the XY plane. This step ensures true loop-closure events between nodes and precise common area estimations in the real world. Accordingly, another Graph SLAM framework was designed to bring the nodes’ elevation images into the same Z-level by making the altitudinal errors in the common areas as small as possible. A robust cost function is detailed to properly constitute the relationships between nodes and generate the map in the Absolute Coordinate System. The framework is tested against an accurate GNSS/INS-RTK system in a very challenging environment of high buildings, dense trees and longitudinal railway bridges. The experimental results verified the robustness, reliability and efficiency of the proposed framework to generate accurate 2.5D maps with eliminating the relative and global position errors in XY and Z planes. Therefore, the generated maps significantly contribute to increasing the safety of autonomous driving regardless of the road structures and environmental factors.