Risk-Based Maintenance: Defining Road and Infrastructure Priorities

Ensuring the safety and efficiency of roads, bridges, viaducts, and tunnels is one of the main challenges for managing bodies, operators, and public administrations. The aging of infrastructure and the increase in traffic volumes make it increasingly important to plan interventions effectively and optimize the use of available resources.

In this context, updated data and advanced digital tools are essential to support maintenance decisions. Solutions dedicated to Infrastructure Asset Management allow you to monitor the state of the works, centralize information, and schedule interventions based on objective and measurable criteria.

Among the most effective approaches for managing infrastructure is Risk-Based Maintenance (RBM), or risk-based maintenance. Risk-Based Maintenance is a methodology that prioritizes maintenance based on the level of risk of the assets, calculated by combining the probability of failure with the potential consequences on the infrastructure, users, service continuity, and the territory. In this way, interventions are planned where the risk is higher, optimizing resources, safety, and the reliability of the works.

In this article, we will see what Risk-Based Maintenance is, what factors to consider in the analysis of road risk, how to develop an RBM process, what digital technologies can support its application, and how a case study based on BIM, GIS, digital surveys, and Business Intelligence can help identify maintenance priorities along a road section.

What is Risk-Based Maintenance (RBM)?

Risk-Based Maintenance (RBM) is a maintenance management methodology that defines intervention priorities based on the risk associated with different components of an infrastructure. Unlike traditional approaches that plan activities based on elapsed time or the occurrence of a failure, RBM focuses on elements that have the greatest potential impact on safety, service continuity, and management costs.

The concept of risk generally derives from the combination of two main factors:

• probability of a failure or significant degradation occurring;
• consequences that such an event could generate on users, infrastructure, and the territory.

The goal is not to completely eliminate risk but to manage it consciously, directing economic and operational resources toward interventions that yield the greatest benefit in terms of reducing critical issues.

Risk-Based Maintenance integrates with modern infrastructure asset management strategies because it allows the transformation of technical data, inspections, performance indicators, and territorial information into operational maintenance priorities.

In the case of road infrastructures, RBM allows for the simultaneous evaluation of numerous aspects, such as pavement conditions, the state of artworks, traffic volumes, exposure to natural phenomena, and the strategic importance of connections. The result is more effective maintenance planning, capable of supporting decisions based on objective data and measurable criteria.

Difference between reactive, preventive, predictive, and risk-based maintenance

In maintenance management, there are various approaches that differ in terms of when interventions are made and the type of information used:

• Reactive maintenance represents the simplest and most traditional approach. Interventions are performed only after the occurrence of a failure or malfunction. Although this strategy requires minimal planning, it can incur high costs, service interruptions, and increased safety risks, especially in the case of critical infrastructures;
• Preventive maintenance, on the other hand, involves scheduled activities at predetermined intervals or upon reaching certain usage thresholds. This approach helps to reduce the likelihood of sudden failures but does not always guarantee optimal use of resources, as some interventions may be carried out when not strictly necessary;
• Predictive maintenance, based on the analysis of the actual conditions of the work and the use of sensors, inspections, and data collection systems. In this case, interventions are scheduled when indicators show signs of degradation or highlight an increasing likelihood of failure;
• Risk-Based Maintenance introduces an additional level of analysis. In addition to assessing the state of conservation of the infrastructure and the probability of a problem occurring, it also considers the consequences that such an event could generate. Two elements with similar conditions can therefore receive different priorities if their potential failure has different impacts on safety, mobility, local economy, or service continuity.

For example, a secondary road segment with limited traffic and a viaduct located along an important connecting artery might have the same level of degradation. However, the consequences of a failure on the viaduct would potentially be much more severe, necessitating a higher intervention priority.

For this reason, RBM is now considered one of the most effective strategies for managing complex infrastructures, as it allows available resources to be allocated more rationally and consistently with the actual level of risk present.

Why Apply Risk-Based Maintenance to Road Infrastructures?

Road infrastructures are essential for the mobility of people and goods, for connections between urban and productive areas, and for the continuity of services in the territory. Roads, highways, bridges, viaducts, and tunnels represent a strategic asset to be managed with increasingly effective criteria.

Many infrastructure networks, however, were built several decades ago and today must deal with aging phenomena, increased traffic loads, and increasingly severe environmental conditions. In addition to this, there are often limited financial resources, making it difficult to intervene simultaneously on all structures requiring maintenance.

In this context, planning maintenance solely based on the age of the structure or on received reports is not sufficient. A method is needed that can identify the most critical elements, define objective priorities, and support transparent decisions.

Risk-Based Maintenance meets this need because it combines technical data, operational information, and risk analysis. The goal is to identify situations where the relationship between the probability of failure and potential consequences requires a priority intervention, optimizing the use of available resources.

Applying RBM to road infrastructures allows for improving user safety, reducing the risk of sudden failures and service interruptions, better planning of maintenance investments, managing extensive and complex networks, increasing the transparency of decision-making processes, and promoting a more sustainable management of the life cycle of structures.

An additional particularly relevant aspect concerns the increasing availability of data from monitoring systems, digital inspections, geomatic surveys, and information models. Thanks to this information, it is now possible to build ever more accurate and up-to-date risk assessments, surpassing maintenance logics based solely on experience or non-integrated periodic checks.

Which factors to consider in road risk analysis?

Risk-Based Maintenance requires a broader analysis that takes into account both the probability of failure and the consequences that such an event could generate.

For this reason, it is necessary to integrate information from various sources, such as inspections, monitoring, traffic data, accident statistics, and environmental information. Among the most relevant factors are the state of conservation of the infrastructure, the intensity of traffic flows, the characteristics of the route, the presence of strategic works, and the territorial context in which the work is situated.

Factors influencing infrastructural risk

Condition of the roadway

The condition represents one of the first parameters to consider in the context of a risk assessment. Deteriorated pavements, surface irregularities, deformations, cracks or structural degradation phenomena can progressively compromise the performance of the infrastructure and increase the likelihood of failures or hazardous situations.

The analysis must take into account not only the visible conditions of the roadway but also the state of the deeper layers of the road superstructure, drainage systems, safety barriers, and other elements that contribute to the functionality of the work.

Information can be collected through visual inspections, instrumental surveys, continuous monitoring systems, and specific diagnostic campaigns. The more accurate the knowledge of the condition, the more reliable the risk assessment associated with the roadway will be.

Traffic, exposure, and level of service

The intensity and type of traffic directly influence the risk level of an infrastructure. A section characterized by high traffic volumes or significant presence of heavy vehicles is generally subject to greater stresses and presents more serious consequences in the event of failure or service limitation.

The role that the infrastructure plays within the transport network is also particularly important. A road that connects productive areas, hospitals, ports, or strategic logistics centers may have a higher level of criticality compared to alternative routes with lesser functional relevance.

For this reason, the risk assessment must consider both traffic data and the level of service guaranteed by the infrastructure and the effects that any interruptions could generate on the mobility of the area.

Presence of bridges, viaducts, tunnels, and artworks

The presence of bridges, viaducts, tunnels, retaining walls, and other artworks increases the complexity of infrastructure management. These elements are often subject to specific degradation mechanisms and require dedicated monitoring and maintenance activities.

Moreover, any failure or limitation of use can have significant repercussions on safety and traffic continuity. For this reason, such works are generally considered high-criticality elements and carry significant weight in risk assessments.

Geometry of the route and plano-altimetric conditions

The geometric characteristics of the road can also influence the risk level. Curves with small radii, steep slopes, narrow roadways, complex intersections, or limited visibility conditions can increase the likelihood of accidents and make emergency management more difficult.

Plano-altimetric conditions also affect the stresses to which the infrastructure is subjected and the speed at which certain degradation phenomena may occur. For this reason, the geometry of the route must be evaluated alongside other technical and functional parameters.

Incidence and history of critical events

Data related to incidents constitutes a valuable source for identifying risk situations that are not always evident during technical inspections alone.

The frequency of incidents, their severity, the presence of black spots, and the recurrence of similar events over time can in fact highlight critical issues related to road configuration, pavement conditions, or the interaction between infrastructure and users.

The history of events such as collapses, flooding, landslides, traffic interruptions, or extraordinary maintenance interventions also provides useful information to understand the behavior of the structure over time and estimate the probability that certain issues may recur.

Hydrogeological vulnerability and environmental conditions

Road infrastructure operates within territorial contexts often characterized by different levels of exposure to natural risks. Areas prone to landslides, erosion, flooding, subsidence, or slope instability require particular attention in risk analysis.

Weather conditions can also significantly affect the durability of structures. Thermal excursions, freeze-thaw cycles, high salinity, intense rainfall, or extreme weather events can accelerate material degradation and increase the probability of damage.

The increasing frequency of exceptional climatic phenomena makes it increasingly important to integrate environmental data into decision-making processes related to infrastructure maintenance.

Consequences of failure on users, network, and territory

In Risk-Based Maintenance, it is not only the probability of failure that matters, but also the impact that such an event could generate.

The temporary closure of a local road and the interruption of a strategic connection can indeed produce very different consequences. In some cases, the inconvenience for users may be limited, while in others, serious repercussions may occur on mobility, economic activities, travel times, and accessibility to essential services.

For this reason, the potential consequences of failure represent one of the most important parameters in defining intervention priorities. Correctly assessing such impacts allows focusing investments on the infrastructures that play a more strategic role for the territory and the community.

How does a Risk-Based Maintenance process for roads and highways work?

The application of Risk-Based Maintenance to road infrastructure follows a structured process that allows for transforming data and technical information into operational decisions. Although methods may vary depending on the characteristics of the network and the available tools, the basic principle remains the same: identify the most relevant critical issues and assign intervention priorities based on the level of risk.

The first phase consists of the collection of available information about the infrastructure, including demographic data, inspection results, traffic information, maintenance history, and any monitoring data. Subsequently, the conditions of the various components of the network are evaluated to identify anomalies and signs of degradation.

The collected data is then used to estimate the risk associated with each asset, combining probability of failure and expected consequences, in order to assign each infrastructure a level of criticality and define the relative maintenance priorities.

Once the analysis is completed, the elements of the network can be classified into different risk categories. The infrastructures considered most critical are placed among the intervention priorities, while those characterized by lower risk levels may be subjected to periodic monitoring or scheduled interventions in the medium to long term.

The process does not conclude with the planning of maintenance activities. One of the most important aspects of RBM is in fact the continuous updating of available information. New inspections, monitoring data, changes in traffic flows, or changes in environmental conditions can influence the level of risk and require a review of previously defined priorities.

Thanks to this dynamic approach, maintenance is no longer managed as a simple sequence of scheduled interventions but as a continuous decision-making process, capable of adapting to the evolution of the infrastructure conditions and the context in which it operates.

Phases of an RBM process

BIM, GIS, AI, and BI Supporting Risk-Based Maintenance

The effectiveness of Risk-Based Maintenance largely depends on the availability of reliable data and the ability to transform it into useful information for the decision-making process. For this reason, in recent years, the management of road infrastructures has seen an increasing integration of digital technologies capable of supporting risk analysis and intervention planning.

Among the most relevant tools is BIM (Building Information Modeling), which allows the creation of digital models containing geometric, technical, and managerial information about the works. In the infrastructure sector, the use of BIM fosters a deeper understanding of the assets and a more efficient management of maintenance activities. To delve deeper into the topic, you can refer to this article dedicated to BIM for Infrastructure.

Alongside BIM, a fundamental role is played by GIS (Geographic Information Systems), which allow the analysis of infrastructures in their territorial context. Through the geo-referencing of data, it is possible to correlate the conditions of the works with external factors such as traffic, environmental characteristics, natural hazards, and the distribution of services.

Artificial Intelligence (AI) is also opening new perspectives in maintenance management. Advanced algorithms can process large amounts of data from sensors, inspections, and monitoring systems to identify anomalies, recognize recurring patterns, and support failure prediction.

Completing this digital ecosystem are Business Intelligence (BI) platforms, which allow the aggregation and visualization of information from different sources through dashboards and composite indicators. In this way, managers can have an updated view of the state of the network and quickly identify situations that require greater attention.

The integration of BIM, GIS, AI, and BI thus allows for overcoming a fragmented management of information, creating a digital environment where technical, territorial, and operational data can be used in a coordinated manner to support risk-based maintenance strategies.

This integration becomes particularly effective when applied to real cases of risk analysis on road sections, where geometric data, digital surveys, territorial information, accident data, and decision-making dashboards can be read in a single information system.

BIM, GIS, AI, and BI supporting risk-based maintenance

Case Study: Rapid Risk Assessment on a Road Segment with BIM, GIS, and Business Intelligence

A concrete example of the application of Risk-Based Maintenance pertains to the rapid risk assessment on road and motorway segments through the integration of BIM data, GIS, digital surveys, Business Intelligence tools, and analysis support technologies.

The objective of the case study is to support managing authorities and concessionaires in evaluating intervention priorities, providing an information system capable of collecting, organizing, and correlating technical, territorial, and inspection data related to the infrastructure. In this way, the decision-making process is no longer based solely on isolated assessments or periodic checks, but on an integrated reading of the risk associated with the roadway segment and the works present along the route.

From Data Collection to the Digital Knowledge Framework

The first phase involved constructing the knowledge framework of the infrastructure. Geometric data, information on artworks, three-dimensional surveys, point clouds, 360° images, environmental data, and cartographic layers related to phenomena such as landslides, hydraulic hazards, and land conditions were collected.

The acquisitions can be carried out using various technologies, including mobile laser scanners, drone surveys, long-range laser scanners, and 360° photographic acquisition systems. These tools allow for a detailed digital representation of the roadway axis, the existing works, and the territorial context, often without having to interrupt traffic during the survey operations.

The result is a true “zero-time digital” snapshot of the infrastructure, useful for comparing the current state with subsequent surveys, identifying degradations, anomalies, or variations over time, and supporting monitoring and maintenance activities.

BIM-GIS Integration to Understand the Infrastructure in Its Context

The collected data are integrated into a BIM-GIS environment, where informational models, point clouds, cartographies, and territorial layers can be consulted in a single shared view. The georeferencing of information allows for linking each element of the infrastructure to its spatial context, facilitating a joint reading between the state of the works and environmental conditions.

In this type of system, it is possible to visualize, for example, the roadway layout together with the existing artworks, BIM models, point clouds, data related to hydraulic hazards, landslide-prone areas, and other territorial themes. This integration allows overcoming a separate data management approach and building a more comprehensive information base for risk analysis.

Accident Analysis and Identification of Critical Points

A particularly relevant aspect of the case study concerns accident analysis. Data on recorded accidents along the route can be represented geographically and distinguished based on the severity of the event, the stretch involved, and the moment it occurred, for example, during daytime or nighttime.

This reading allows for identifying critical points in the network and understanding whether the concentration of accidents is related to local factors, such as the geometry of the route, visibility, presence of obstacles, pavement conditions, safety barriers, or planimetric-altimetric characteristics of the road.

To make the analysis more objective, the accident rate can also be used, calculated by relating the number of accidents to the vehicles that passed and the length of the considered stretch. This indicator allows for comparing the behavior of a roadway segment with average reference values and identifying any conditions of risk above the average.

BI Dashboard and Maintenance Priorities

The integration with Business Intelligence tools allows for transforming the collected data into dashboards, graphs, and synthetic indicators. Through visual representations, such as thematic maps, scatter plots, or criticality indicators, the manager can quickly identify the kilometer sections with the highest level of risk and assign priorities to maintenance interventions.

In this way, raw data from diverse sources are transformed into valuable information for the decision-making process. Dashboards allow for comparing roadway segments, visualizing the distribution of critical events, highlighting points with a higher accident rate, and supporting the planning of interventions based on the degree of priority.

The value of the case study lies precisely in the ability to transition from fragmented information management to a digital integrated system, where BIM, GIS, surveys, inspections, and risk indicators contribute to defining priorities. In this perspective, Risk-Based Maintenance becomes a continuous operational process, capable of updating over time with new surveys, new inspections, and new events recorded throughout the lifecycle of the infrastructure.

From RBM to the Digital Twin of Infrastructures

The case study demonstrates how the integration of BIM data, GIS, digital surveys, inspections, and Business Intelligence dashboards can represent a concrete foundation for the evolution towards the digital twin of infrastructures.

The evolution of digital technologies is progressively transforming the way infrastructures are managed throughout their entire lifecycle. In this context, Risk-Based Maintenance represents one of the fundamental pieces for building increasingly advanced management models oriented towards the continuous update of information and decision support.

One of the concepts gaining increasing relevance is the digital twin, that is, a digital replica of the infrastructure that integrates geometric data, technical information, operational data, and monitoring results in a single informational environment.

Unlike a simple three-dimensional model, the digital twin is constantly updated through data from the field and allows for representing the state of the work in almost real-time. This enables managers to monitor the infrastructure conditions, simulate future scenarios, and evaluate the effects of different maintenance strategies before implementing them.

At the base of this approach is often the availability of models developed according to the principles of Infrastructure Information Modeling (iBIM), which allow for organizing and managing information related to infrastructure assets in a structured manner.

The integration between the digital twin and Risk-Based Maintenance significantly improves the quality of risk analyses. Information from sensors, inspections, and monitoring systems can continuously feed the digital model, updating criticality indicators, and supporting the definition of intervention priorities.

In perspective, the convergence of RBM, digital twin, artificial intelligence, and advanced monitoring systems will allow for increasingly predictive and proactive infrastructure management. The goal will no longer just be to intervene when a criticality emerges, but to anticipate problems and optimize the entire lifecycle of assets, improving safety, reliability, and economic sustainability.

From data to infrastructure digital twin

Benefits and limitations of risk based maintenance

The adoption of Risk-Based Maintenance offers numerous benefits in the management of road infrastructures, especially when it is necessary to optimize the use of available resources and define intervention priorities based on objective criteria.
Among the main advantages of RBM are:

• a greater capacity to identify the most critical infrastructures and focus maintenance interventions on them;
• the improvement of safety through the proactive identification of high-risk situations;
• a more efficient planning of investments and maintenance activities;
• the reduction of unexpected service interruptions and emergency interventions;
• a greater transparency in decision-making processes, supported by measurable data and indicators;
• a more sustainable management of the entire life cycle of infrastructures.

Despite these benefits, the application of the methodology also presents some critical issues that must be carefully considered:

• the need for reliable, up-to-date, and easily accessible data;
• the initial investment required for the collection, organization, and digitization of information;
• the complexity of integration among data sources, monitoring systems, and different management platforms;
• the involvement of multidisciplinary skills for risk analysis and interpretation of results;
• the necessity to periodically update assessments to maintain the system’s effectiveness over time.

How to set up a RBM strategy for road infrastructures?

The implementation of a Risk-Based Maintenance strategy requires a structured process that allows the transformation of data and technical information into operational decisions. Generally, the path can be divided into the following steps:

1. inventory the infrastructure assets: the first step consists of identifying and cataloging the infrastructures to be managed, collecting information on roads, bridges, viaducts, tunnels, and other works present in the network;
2. collect and organize available data: it is necessary to centralize data relating to the state of conservation, inspections, monitoring, traffic, executed interventions, and critical events recorded over time;
3. define risk assessment criteria: it is essential to establish which parameters to use to estimate the probability of failure and the possible consequences associated, considering the characteristics of the network and management objectives;
4. classify infrastructures based on risk level: the analysis allows for the identification of the most critical assets and assigns objective priorities to maintenance interventions;
5. plan interventions and allocate resources: based on identified priorities, it is possible to schedule maintenance activities and allocate financial resources to the works that present the highest level of criticality;
6. monitor and periodically update assessments: Risk-Based Maintenance is a dynamic process. New data from inspections, sensors, and monitoring must be used to continuously update analyses and adapt maintenance strategies to the evolving conditions of the infrastructure.

Risk-Based Maintenance represents today one of the most effective approaches to improving the management of road infrastructures, optimizing maintenance investments, and increasing safety and reliability levels of the network. The integration with digital technologies, monitoring systems, and asset management platforms also enables more informed and data-driven decision-making.
To efficiently manage the entire life cycle of roads, bridges, and other infrastructural works, discover the Infrastructure Asset Management software, designed to centralize data, monitoring, and maintenance processes in a single digital environment.

FAQ on Risk-Based Maintenance

What is Risk-Based Maintenance?

Risk-Based Maintenance is a maintenance strategy that defines intervention priorities based on the risk level of assets. Risk is assessed by considering the probability of failure or degradation and the potential consequences such events may generate on safety, service, users, and the territory.

How is risk calculated in the maintenance of infrastructure?

Risk can be estimated by combining various factors, including probability of failure, consequences, vulnerability of the work, exposure, and danger of the context. In the infrastructure sector, these elements can be summarized through risk matrices, attention classes, criticality indices, and maintenance KPIs.

What data is needed to apply Risk-Based Maintenance?

To apply Risk-Based Maintenance, demographic data of assets, inspections, geometric surveys, conservation status, traffic information, historical intervention data, accident data, structural monitoring, and environmental or territorial information are needed.

What role do BIM and GIS play in Risk-Based Maintenance?

BIM allows for the organization of geometric, technical, and managerial information of infrastructure assets, while GIS enables reading such information in their territorial context. The BIM-GIS integration helps connect the state of the work with external factors such as traffic, landslides, hydraulic risk, accidents, and accessibility.

Why is Business Intelligence useful in risk-based maintenance?

Business Intelligence allows the aggregation of data from different sources and transforms them into dashboards, charts, and synthetic indicators. This way, managers can quickly identify the most critical assets, compare different road segments, and schedule interventions based on priority levels.

What is the link between Risk-Based Maintenance and digital twin?

The digital twin can feed and update the Risk-Based Maintenance process over time, integrating data from surveys, sensors, inspections, BIM models, GIS, and monitoring systems. This allows for the evaluation of risk evolution and improves intervention planning throughout the entire life cycle of the infrastructure.