Data-driven estimation of deterioration curves: a railway supporting structures case study

A significant portion of railway network income is spent on the maintenance and restoration of the railway infrastructure to ensure that the networks continue to provide the expected level of service. The execution of the interventions – that is, when and where to perform maintenance or restoration activities, depends on how the state of the infrastructure assets changes over time. Such information helps ensure that appropriate interventions are selected to reduce the deterioration speed and to maximise the effect of the expenditure on monitoring, maintenance, repair and renewal of the assets. Presently, there is an explosion of effort in the investigation and use of data-driven methods to estimate deterioration curves. However, real-world time history data normally includes measurement of errors and discrepancies that should not be neglected. These errors include missing information, discrepancies in input data and changes in the condition rating scheme. This paper provides solutions for addressing these issues using machine learning algorithms, estimates the deterioration curves for railway supporting structures using Markov models and discusses the results.

FEA on Grey Cast Iron Assets: A Case Study on Penstocks in the Waste Water System

Penstocks have been used in the water industry for flow control since the Victorian expansion and consolidation of clean and waste water networks. However, the Victorians were the first to use grey cast iron (GCI) castings to manufacture large scale penstocks. Most of these ageing assets are still in operation, however engineering assessments are necessary to determine a structure’s fitness-for-service. Even today, penstocks in the sewer system tend to be made from GCI, due to ease of manufacturing, resistance to corrosion and cost. One characteristic property of grey cast iron is the graphite flake structure in the material, contributing to its low toughness, inconsistency in material strength and brittle behaviour, despite exhibiting slight hardening properties. Finite element analysis (FEA), is a numerical method which allows the analysis of complex structures by splitting it into finite parts and solving them with a computer processor. Despite the versatility of FEA, appropriate considerations and assumptions are necessary due to the difficulty to obtain data from inspection and unique material behaviour of GCI. The article shows concerns for an analysis of GCI penstocks using FEA, which extends into the application of fracture mechanics approaches for defect assessments.

Binary Logistic Regression Approach for Decision Making in Bridge Management

Bridge management professionals need effective tools to help guide the decision-making process and maintain quality infrastructure in a region. A new binary response is herein defined by categorizing bridges as at-risk and not at-risk, based on the existing overall bridge condition scores. Fitting binary logistic regression model for the response, the probability of a bridge being at-risk is expressed in terms of the primary bridge factors age, load, types of construction material and structural design, and conditions of the deck, superstructure, and substructure. These estimated probabilities multiplied by specified consequence values are used to introduce the risk classes and their ranks. Employing the method for training and validating sets of sizes 13,540 and 3,385 in 2017, and 13,481 and 3,370 in 2018 data in National Bridge Inventory (NBI) Indiana, a statistically significant model is established containing age, load, conditions of both superstructure and substructure. Moreover, at-risk bridges are identified from Indiana NBI data in both years and for a subset from Connecticut in 2017. The novel bridge-ranking tool prioritizes bridges for maintenance purposes such as replacing or repairing and hence efficiently guides the management in the decision-making process for capital expenditures, and perhaps, for predicting the missing overall bridge condition.