Risk-Based Multi-Objective Optimization for Iowa DOT Long-Range Bridge Improvement Programming

Challenge: Optimize 20-Year Bridge Improvement Programs

With increasing challenges of ageing and deterioration of bridges, climbing maintenance backlogs, and insufficient investments, optimization of long-range bridge improvement programs has been a high priority for Iowa DOT. Bridge programming decisions need to focus on optimizing budget allocation and investment strategies to meet organizational objectives and performance metrics. Programs need to be designed to ensure the safety and adequacy of managed bridge inventories, reduce risks and lifecycle costs, and maximize return on investment. Iowa DOT required a unique solution that can support detailed analysis of the relationship between funding levels and performance and risk measures of the bridge inventory, and the ability to optimize project selections to ensure that scarce funding resources are invested on the right project at the right time.

“IDS software helped show us the impact of differing funding levels. This helped us understand the daunting task ahead of us. The system’s ability to show how funding levels change the overall condition of the bridge population is very beneficial. This system provided information that quantified how much spending was needed to better preserve our bridge population. The system verified spending levels that we knew were needed, but were only able to use broad assumptions to determine ourselves. Being able to see how the health of the bridge population changes with different spending levels is something that has always been difficult to quantify.”

Scott Neubauer, Iowa DOT

Solution: Asset Optimizer

Asset OptimizerTM GIS-centered cloud-based software was used to perform in-depth data analysis and develop optimal 20-year risk-based plans for Iowa state-owned bridges. The software implemented a unique risk model, developed by Iowa DOT engineers, to assess the priority of bridges based on structural condition and load rating, as well as a range of criticality factors, e.g., vertical and horizontal clearances, traffic levels, type of carried roadway, detour length, and waterway adequacy. Data-driven deterioration and risk models were developed based on historical NBI data and considered a range of variables such as age, traffic volume, design load, and deck type, etc. Cost and benefits models for various bridge improvement actions were defined, along with various constraints governing these actions.

Multiple planning scenarios were defined for interstate and non-interstate bridge groups. Iowa DOT engineers defined scenario parameters including budget profile, target condition or risk metrics, budget splitting among improvement methods, average annual inflation rate, etc.  Asset OptimizerTM generated optimal project lists that satisfied all defined criteria and constraints. Scenario analysis helped to quantify relationships between funding levels and system condition and risk metrics, and to develop optimized and defensible long-range bridge improvement program.

Caltrans – Cross-Asset Optimization of Transportation Infrastructure Assets


Client: California Department of Transportation (Caltrans)

Project: Cross-Asset Optimization Model Development Services

IDS is working with Caltrans to develop and demonstrate an innovative cross-asset multi-objective optimization model to support project selection and prioritization of transportation infrastructure assets for the State Highway Operation and Protection Program (SHOPP). IDS Asset OptimizerTM software is being used to support the development and implementation of this model

Solution: Asset Optimizer

Asset OptimizerTM GIS-centered cloud-based software is being used to implement an innovative 3-step cross-asset risk-based multi-objective optimization approach to optimize budget allocation and generate long-range network-level project portfolio. The optimization model will support the development of 10-year optimal plans under a range of scenarios and investment strategies, with an initial focus on bridge and pavement assets. The optimization model extends Caltrans’ current MODA methodology to enable efficient prioritization and optimal programming of projects across highway system assets. The model will help evaluate the impact of different funding levels on system performance and risk metrics and determine required funding levels to meet performance and risk targets. The cross-asset optimization model will help Caltrans make optimal programming and investment decisions to meet organizational objectives and performance metrics, and deliver optimized long-range programs for its entire transportation asset portfolio.

Enhancing Yukon’s Transportation Asset Management Framework

Enhancing Yukon’s Transportation Asset Management Framework


Client: Government of Yukon, Highways & Public Works

Location: Whitehorse, Yukon, Canada

Project: Transportation Asset Management Framework Initiative

IDS was commissioned by the Transportation Division of Yukon’s Highways & Public Works to provide technical services to support the asset management initiative and to undertake a review of the Division’s asset management processes.


Yukon’s Transportation Division has undertaken a major initiative to implement innovative processes and technology to implement asset management best practices. An important activity within this initiative is the assessment of the current state of asset management practices in the Division to identify areas where improvements can be made to advance these practices. This activity required the development of a gap analysis tool that emphasizes the key areas relevant to the needs and state of practice in the Division, and to establish goals and priorities towards continuous improvement of organizational practices, performance, and maturity.


This project involved customization of the American Association of State Highway and Transportation Officials (AASHTO) Gap Analysis Tool, which provides a structured approach to assess maturity of asset management practices in transportation agencies using a hierarchical organization of asset management areas, categories, elements, and criteria. The project also involved the development of a maturity model and an online survey to collect and analyze information pertinent to asset management processes in the Division. The data collected addressed the six main practice areas identified by the AASHTO model, namely, Legislative Compliance, Policy Guidance, Planning & Programming, Program Delivery, Information & Analysis, and Lifecycle Management & TAM. Subsequently, the data was aggregated, analyzed, and summarized to provide insight into current practices, assess maturity of processes using a quantitative scale, and identify gaps and opportunities to improve asset management processes.