An Interpretive Structure Modeling Approach to Assess the Value of Underground Utility As-Builts

Inaccurate, incomplete, or outdated utility as-built data significantly hinder underground infrastructure projects, resulting in delays, increased costs, and safety risks. The reliance on paper-based processes for utility documentation further exacerbates these challenges, often resulting in poor-quality as-built data. Recognizing the substantial value of collecting better quality as-built information to enhance planning, improve design, expedite construction, and reduce costs, this study explores the impact of various utility as-built data formats on transportation projects. The objective of this research was to establish the hierarchical relationship among the identified factors (related to utility as-builts) to determine their level of influence/criticality through interpretive structural modeling (ISM) in construction projects. Furthermore, using cross-impact matrix multiplication applied to classification (MICMAC) analysis, these factors were classified into four categories based on their driving and dependence power. At the end, a survey of state DOTs was conducted to identify the impact of different types of utility as-built data formats on construction projects. The findings illustrate that technical factors, such as utility investigation, positional accuracy/data quality, and the type of utility as-built data format, have a greater influence on construction projects, further affecting operational and decision-related factors like utility coordination, utility risk assessment, and conflict resolution, which ultimately influence overall project cost. The research contributes to developing a framework that models the interdependencies among these factors, providing a decision-making tool for assessing the value of the utility-as-built data format and further guiding the selection of an appropriate data collection format to make an informed choice based on its recognized impact on construction projects.