Presenter: Lawrencia Akuffo
Organization: Rowan University
SAFETY
Abstract:
This study examines the structural health and socioeconomic variables influencing the condition of all bridges in New Jersey. Starting by examining the resilience of bridges under various load scenarios (Average Daily Traffic (ADT)/Live Loads, Environmental Loads/Conditions) categorized by bridge materials.
First, a Kaplan Meier model was used to determine the time to failure for the various load scenarios categorized by the bridge materials; giving the percentage of failed bridges at year 0,50,100,150 and 200 indicating the proportion of bridges that remain functional without failure after the marked years; additionally, a Cox PH model was used to determine the relationship between the covariates and the bridge failure risks.
Second, we investigate the impacts of local socioeconomic factors on bridge conditions. Since tolls, gas taxes, user fees and taxes, and federal road funds, provide most of the funding for bridge reconstruction, this analysis sought to identify any relationships between (a) bridge condition history (the time it takes to reach failure criteria), (b) the percentage of good and fair bridges in each county, and (c) the socioeconomic factors that are present. Using K-means clustering we first conducted an exploratory analysis to evaluate the data behavior, then using Multiple Linear Regression we found how much the various factors combination impacted the bridge health. The importance of each affecting factor was ranked through Random Forest.
The results showed that AADT contributed 31.4% to the bridge deterioration, followed by Population contributing 22.73%, and Business 16.38%, with Median Income contributing to the 1.08%. This reinforces the need for targeted funding relying on each locality’s specific needs to support equitable bridge maintenance and improve overall infrastructure quality. This insight, combined with time-to-failure analysis, suggests prioritizing specific bridge types in low-income areas to ensure longevity despite limited funds.
Lawrencia Akuffo is a civil engineer and researcher whose work bridges the fields of structural engineering, data science, and infrastructure resilience. As a Graduate Fellow at Rowan University’s Department of Civil and Environmental Engineering, her research focuses on structural health monitoring and remaining capacity estimation of bridges and prestressed concrete girders using advanced modeling tools like Abaqus and machine learning. Lawrencia’s innovative studies integrate LiDAR data, point cloud analysis, and socioeconomic modeling to understand infrastructure performance and deterioration across New Jersey’s bridges. Her multidisciplinary approach, which combines statistical modeling, structural simulation, and data-driven insights, positions her at the forefront of next-generation transportation infrastructure research.
Presentation Slides:
To be added.