Efficient culvert load rating through data-driven solutions

Abstract

Culverts play a critical role in managing water flow beneath roads and railways, necessitating accurate load rating to ensure structural integrity and safety. Evaluating the structural capacity of culverts to support anticipated traffic load and other loads is imperative for maintaining the efficiency and safety of transportation operations. Traditionally, the load rating process has been laborious and time-consuming, primarily due to the extensive data extracted from structural analysis programs such as Midas or Space Gass. In this paper, a case study involving 5 culverts is presented, highlighting the importance of automation in load rating assessments. The study proposes data-driven solutions that harness the power of programming languages such as Microsoft Visual Basic Application (VBA) and Python, coupled with specialized modules such as Pandas DataFrame. These tools facilitate efficient processing and in-depth analysis of culvert data, optimizing the load rating process. The automation of load rating calculations through programming streamlines the assessment process, significantly reducing the time and effort required for accurate results. By embracing automation and leveraging advanced software, engineers can enhance their ability to swiftly and accurately evaluate culvert load ratings, ultimately enhancing infrastructure safety and operational efficiency in the transportation sector.