Machine learning and statistical approaches have transformed the management of infrastructure systems such as water, energy and modern transport networks. Artificial Intelligence-based solutions allow asset owners to predict future performance and optimize maintenance routines through the use of historic performance and real-time sensor data. The industrial adoption of such methods has been limited in the management of bridges within aging transport networks. Predictive maintenance at bridge network level is particularly complex due to the considerable level of heterogeneity encompassed across various bridge types and functions. This paper reviews some of the main approaches in bridge predictive maintenance modeling and outlines the challenges in their adaptation to the future network-wide management of bridges. Survival analysis techniques have been successfully applied to predict outcomes from a homogenous data set, such as bridge deck condition. This paper considers the complexities of European road networks in terms of bridge type, function and age to present a novel application of survival analysis based on sparse data obtained from visual inspections. This research is focused on analyzing existing inspection information to establish data foundations, which will pave the way for big data utilization, and inform on key performance indicators for future network-wide structural health monitoring.

Introduction: Climate-related disasters have cost the world over £450 billion over the last 3 years. In the race to mitigate these effects, the UK government has committed to net-zero emissions by 2050. Transport provides the largest single sector contribution to CO₂ emissions, the road network accounts for up to 91%. As the only UK country without a formal climate change bill Northern Ireland could compromise the overall effort. Case description: In this research a survey of road asset owners, managers, academics, consultants, public transport providers was undertaken to seek to understand the current barriers to adapting a dispersed rural road network in Northern Ireland for net-zero transport. The survey data was collected though an online form with a combination of multiple choice and open ended questions. Thematic analysis was used to code and analyse the data collected which enabled a discussion around the key expert opinions gathered. Discussion and evaluation: The paper presents details of the current road network in Northern Ireland and highlights some of the issues faced by asset owners. The survey questions were developed though engagement with transport professionals in Northern Ireland and focus predominantly on road use rather than the impact of current land management practices or environmental conditions such as flood risk. The response highlights a clear enthusiasm for change in the operation of the public road network which is hindered by a lack of government strategy and limited public consultation. Conclusions: The high response rate (41%) for the survey highlights the interest of those in the transport sector to engage in activities which can support a better understanding of how road networks contribute to CO₂ emissions. Within the survey data a requirement for behavioural change was highlighted as a key step to reduce transport related emissions, the enthusiasm for change demonstrates this is the optimum time to engage with the public and develop clear transport strategies. More accurate findings and empirical evidence could have been established had the study considered specific, transport planning, environmental and land use conditions for Northern Ireland. This will be the focus of further research in this area to enable clear translation of the research to other countries.

This paper outlines an initial analysis of 20 years of data held on an electronic bridge management database for approximately 3500 arch bridges across Northern Ireland (NI) by the Department for Infrastructure. Arch bridges represent the largest group of bridge types, making up nearly 56% of the total bridge stock in NI. This initial analysis aims to identify trends that might help inform maintenance decisions in the future. Consideration of the Bridge Condition Indicator (BCI) average value for the overall arch bridge stock indicates the potential for regional variations in the overall condition and the potential for human bias in inspections. The paper presents the most prevalent structural elements and associated defects recorded in the inspections of arch bridges. This indicated a link to scour and undermining for the worst-conditioned arch bridges. An Analysis of Variance (ANOVA) analysis identified function, number of spans, and deck width as significant factors during the various deterioration stages in a bridge’s lifecycle.