Bridges are vital components of transportation infrastructure, connecting communities, supporting the movement of vehicles, and facilitating the flow of goods and services. However, like all structures, bridges age over time, enduring wear and tear from natural forces, heavy traffic, and environmental conditions. Managing bridge health is a complex responsibility. Inadequate preservation efforts, including maintenance, rehabilitation, repair, or replacement, can compromise safety and lead to significant costs. This is where bridge deterioration modeling becomes indispensable. These models predict how bridge components and elements deteriorate, enabling timely maintenance and effective resource allocation. By providing insights into future trends, they help bridge owners avoid reactive decisions, optimize budgets, and maintain structural integrity. With predictive capabilities, infrastructure managers can schedule proactive maintenance, prioritize repairs, and make data-driven choices to extend the lifespan of critical infrastructure.
In this article, we’ll explore the various bridge deterioration modeling methods essential for effective bridge management, shedding light on their applications, benefits, and the advanced technologies used to forecast bridge conditions with greater precision.
Bridge deterioration is the process through which bridge components degrade over time due to a combination of environmental, operational, and material factors. It refers to the loss of structural integrity, which can manifest in cracks, corrosion, fatigue, and other forms of damage that compromise the safety and performance of the infrastructure.
Several forms of deterioration impact bridges, including:
These types of deterioration not only affect the safety of the infrastructure but also its operational life and the cost of maintaining or replacing bridges.
Understanding the primary factors that contribute to the deterioration of bridges is critical for effective management and maintenance. Numerous studies, including those by Moomen et al. (2016), Caltrans (2015), Agrawal et al. (2008), Al-Rashed et al. (2023), Althaqafi and Eddie (2022), Abed-Al-Rahim (1995), Thorkildsen (2020), and Miller and Jahren (2014), have shed light on various aspects of bridge deterioration. Here are some of the most significant factors:
These factors emphasize the importance of proactive maintenance and informed material selection.
Bridge deterioration models predict the condition of bridge members (components or elements) over time, assuming no condition-improving action is performed on them. These models are a vital part of Bridge Management Systems (BMS), as they forecast the future condition of structures, enabling BMS to identify optimal treatments and conduct lifecycle planning.
Various modeling techniques exist to simulate bridge deterioration, each offering unique advantages. These models can generally be categorized into three primary types:
Given the complexities associated with mechanistic models, this article focuses on statistical bridge deterioration models, which are widely used in the bridge management field.
Bridge deterioration models can be categorized based on the level of detail they provide in component-level and element-level models. Each level serves different purposes and offers distinct advantages depending on the needs of the bridge management system.
Component-level models focus on the condition of bridge components, where each component comprises multiple elements. Component-level models provide insights into how these major components perform over time in general. These models are essential for overall lifecycle planning and budget estimation, especially for large bridge inventories.
Element-level models offer a more granular prediction of deterioration, enabling bridge managers to understand how specific elements may deteriorate over time and when individual elements may need attention or replacement.
Through the use of accurate deterioration models, bridge owners can better estimate future budget needs. This is crucial for long-term planning and budgeting within Bridge Management Systems. The models also play a key role in identifying optimal treatment strategies for bridge and culvert inventories, ensuring that preservation, maintenance, and rehabilitation efforts are made at the right time. The development of reliable and accurate bridge deterioration models is essential to maintaining the safety, longevity, and financial sustainability of infrastructure, emphasizing the importance of data accuracy and the need for reliable modeling techniques.
Deterioration models can take various forms, from simple age-based methods to more sophisticated machine-learning techniques. These models generally fall into two categories: deterministic and stochastic.
Deterministic models predict the future condition of a bridge based on a set of predefined rules or known parameters. These models are useful for estimating the lifespan of bridges in predictable environments where deterioration is influenced by a limited number of factors.
Stochastic models take into account the randomness and uncertainty inherent in deterioration processes. These models use probability to predict future conditions, offering more nuanced and realistic predictions than deterministic models.
A reliable bridge deterioration model depends heavily on the accuracy and quality of the inspection data used. To enhance the reliability of this data, bridge owners can implement data exploration and visualization techniques to detect errors and clean the dataset accordingly. Leveraging advanced AI/ML-based platforms, such as anomalyX™ developed by AssetIntel™ [Fereshtehnejad et al., 2022], can assist in automatically identifying anomalous data and streamlining the data cleaning process.
The process of developing deterioration models requires thorough data preparation. The level of effort varies depending on the modeling method. Below are considerations for different modeling methods:
The TICR method requires more detailed data processing to calculate the Time-In-Condition Rating (TICR) for each sample data directly. Below are key considerations for realistic TICR calculations:
The data processing required for the MCTP method involves forming a matrix that shows the likelihood of condition states or ratings remaining in their original condition and transitioning into one lesser condition.
Despite major repairs, rehabilitations, and replacement projects, preservation actions usually do not directly increase the condition rating of components or the condition state of elements. Consequently, their effect is not distinguishable in the data preparation process. However, these actions are usually indirectly incorporated into deterioration models, primarily through longer time spent in conditions.
Bridge Management Systems (BMS) must have mechanisms to account for the benefits of preservation actions, ensuring these effects are realistically included for optimal long-term planning. AssetIntel™’s manageX™ software offers this functionality by allowing users to define the decimal condition rating that components improve to after undergoing preservation actions. This feature enables more accurate and realistic modeling of long-term bridge condition performance.
Age-based models are relatively simple to develop, requiring only a year’s worth of data at a minimum. These models predict the distribution and expected age of bridges at each condition, followed by a regression model. While straightforward, age-based models are limited by their sole focus on age in predicting conditions, which can lead to inconsistencies for bridges whose actual condition deviates from the model’s predictions for their age [FHWA, BMS Workshop].
The TICR method demands more historical data and a higher level of inspection data processing than either the Age-based or MCTP methods for component-level deterioration modeling. This approach involves collecting reliable data to directly measure the number of years a component has remained in a given condition rating, enhancing the accuracy and reliability of bridge deterioration models. The TICR method is favored by bridge managers because it allows them to compare and validate the model results with their own assessments.
One advantage of the MCTP method is that it offers probabilistic predictions that account for the inherent randomness in the deterioration process. These probabilities can be converted into TICR for both elements and components (See [FHWA, BMS Workshop] for further details). To reduce the randomness in deterioration models, MCTP models are refined by explicitly considering the age and effectiveness of protective members. More details can be found in [FHWA, BMS Workshop]. Like the age-based method, the MCTP method can be developed with just one year of data, although using multiple years of data is recommended for greater accuracy.
The methods discussed above primarily focus on the general randomness within the deterioration process, which can be significant and may reduce the accuracy of predictions. Although age-based and MCTP methods mitigate some uncertainty by factoring in age and protective members, as discussed in the Primary Factors Contributing to the Deterioration of Bridges section, numerous additional factors influence the deterioration of bridge components. These factors go beyond just age and protective members. Machine learning methods can incorporate these additional factors as inputs, providing more accurate predictions for the condition of bridge members over time.
Due to the large benefits of the ML method and the practicality of the predictions provided through the TICR method, AssetIntel™ has developed an automatic Machine-Learning program that generates deterioration models for bridge components, in terms of Time-In-Condition-Rating.
This program combines the robust benefits of Machine Learning with the practicality of TICR-based predictions. By default, it analyzes the entire history of federal tape data for each U.S. state. The process includes:
These deterioration models can be integrated or modified for use in an Action Trigger model within a Bridge Management System, such as manageX™.
For bridge elements, manageX™ incorporates deterioration predictions based on Markov transition probabilities. This enables the system to estimate the quantities of bridge elements in different condition states over time, providing a detailed view of infrastructure health.
A sample preliminary deterioration model tree, developed for a U.S. state, demonstrates the system's ability to group components and predict their long-term condition trends effectively. By leveraging these models, manageX™ empowers bridge managers to make data-driven decisions, prioritize interventions, and maintain structural integrity.
Bridge deterioration occurs over time due to environmental conditions, material aging, and continuous use, leading to issues such as corrosion, cracking, fatigue, and scour. Predicting these changes is essential for ensuring safety, optimizing maintenance, and reducing long-term costs.
Bridge deterioration models play a crucial role in **Bridge Management Systems (BMS)** by forecasting future conditions and guiding proactive decision-making. These models range from simple Age-Based and Time in Condition Rating (TICR) methods to more advanced Markov Chain Transition Probability (MCTP) and Machine Learning (ML) approaches. Deterministic models follow fixed deterioration patterns, while stochastic models account for variability, providing a more dynamic understanding of bridge aging. Regardless of the method, model accuracy depends on high-quality data, continuous updates, and consideration of influencing factors such as traffic loads, materials, and environmental conditions.
AssetIntel™’s manageX™ takes bridge deterioration modeling to the next level, integrating machine learning, real-time data syncing to refine predictions and improve resource allocation. By leveraging manageX™, infrastructure managers can anticipate maintenance needs, extend bridge lifespan, and optimize budgets, ensuring their bridges and culverts remain safe, cost-effective, and reliable for years to come.