Modeling the Propagating Effects of Errors and Omissions in Highway Transportation Projects: Methodology and Case Study

Abstract

Errors and omissions (E&O) are a leading cause of change orders (COs) in transportation infrastructure projects, often resulting in cost overruns, schedule delays, and diminished project outcomes. Although numerous studies have examined the incidence and causes of E&O-related COs, few have systematically modeled their sequential downstream propagation across the construction process. There is a need to investigate how E&Os cascade, leading to additional COs in a project. The objectives of this paper are to (1) identify the patterns of E&O co-occurrence with other CO causes, and (2) propose a methodology to probabilistically model E&O propagation and identify high-risk cost and schedule escalation pathways. This study uses two complementary techniques: association rule mining (ARM) and first-order Markov modeling applied to a case study involving 2,149 highway projects in 2010–2023 at a midwestern Department of Transportation (DOT). Key findings show that projects with early-stage design E&O experience 4.32% higher cost growth and 17.98% longer delays. ARM results reveal strong, nonrandom associations between design-related E&O and downstream COs, such as constructability, utilities, and soils (lift>6.0). Markov modeling identifies design E&O as a high-probability origin state, propagating with typical cost increases of 3%–6% and delays of 12%–24%. Overall, this paper contributes to the integration of CO root-cause analysis with predictive modeling to benefit public infrastructure agencies that seek to enhance their project control policies and practices, especially at the design and constructability stages.