MTC research briefs

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June 29, 2015

The Midwest Transportation Center (MTC) sponsors a competitive research program to fund projects focused on State of Good Repair in infrastructure with attention to safety and Data Driven Performance Measures for Enhanced Infrastructure Condition.

The following are some projects led by Iowa State University, as the lead university, and its university partners. Stay up to date on research conducted by the MTC at http://www.intrans.iastate.edu/mtc/index.cfm/research.

Iowa State University

Terrestrial Laser Scanning-Based Bridge Structural Condition Assessment

Project PI: Yelda Turkan

Terrestrial laser scanners (TLS) are promising sensors for automatically identifying structural condition indicators, such as cracks, displacements, and deflected shapes, as they are able to provide high coverage and accuracy at long ranges. This project investigates the feasibility to measure TLS performance for automatic detection of cracks for bridge structural condition assessment. The TLS data is provided as point clouds with color and intensity data associated with each point within the cloud. Point cloud data can be analyzed using computer vision algorithms to detect cracks for condition assessment of reinforced concrete structures.

Wavelet neural network algorithms for detecting cracks from laser scan point clouds are being developed based on the state-of-the-art condition assessment codes and standards. The proposed method for crack detection would enable automatic and remote assessment of bridge condition and help reduce costs associated with infrastructure management and enhance maintenance operations. Several Departments of Transportation (DOTs) have been contacted to learn about their bridge inspection processes and use of advanced technologies such as 3D models or laser scanning. So far, contacts with nine states have been made.

Creighton University

Iowa Road Conditions: Putting Data to Work

Project PIs: Ravi Nath and William Duckworth

The Iowa Department of Transportation (Iowa DOT) monitors Iowa road conditions with an eye towards repairing the roads in a timely fashion. An expansive amount of data is collected each year on the severity of cracks of various types and matched with other bits of data related to traffic patterns and construction materials used in the road as well as data on previous road repair efforts. Using the data from the Iowa DOT for 2013, this project is looking for patterns and trends in these data that will aid in modeling current road conditions as well as predicting future cracking and stress-related conditions. Resource allocation decisions will benefit from a data-driven view of Iowa road conditions.

Harris-Stowe State University

Asset Management Transportation System Model 

Project PI:  Fatemeh Zakery

Student researchers, under faculty advisor supervision, developed and completed a research project to create a sustainable asset management transportation system model. Since Harris-Stowe State University is a historically black university, the team focused on inner city road conditions related to municipal asset allocation within threeSt. Louis City wards. The team collaborated with the Missouri Department of Transportation (Missouri DOT), the St. Louis City Street Department, local government officials, universities, and related stakeholders.

The team identified roadways for the assessment using roads, traffic, location, safety, utility, and demographics in residential and commercial areas. The selected 45 block area was divided into six sections. The designated roads were surveyed, photographed, and evaluated, and the number of necessary street repairs and costs of each repair were forecasted. It is expected that this economic development model may be implemented in the St. Louis Metropolitan Region and replicated by similar municipalities. This project was selected for presentation at the 2015 Annual Washington Research Forum in Washington, D.C., on March 13-14, 2015.

University of Missouri, Columbia

Implementation of Asset Management in Grandview, Missouri

Project PI:  Henry Brown

One challenge in keeping local infrastructure in a state of good repair is the limited availabilityof funding to help preserve and improve existing facilities. The successful implementation of asset management can help local governments to save money in the long run by utilizing a “mix of fixes” approach that emphasizes maintenance over rehabilitation. The goal of this project is to assist the city of Grandview, Missouri, with the implementation of asset management and in the process to develop a system for local governments in Missouri to implement asset management. The project is primarily focused on pavement management and includes initial inventory and data analysis to help the City of Grandview to best allocate its resources to maintain its roads. However, the framework that is developed will be applicable to other assets such as signs and pavement markings.

University of Missouri, St. Louis

Women as Assets in Railroad & Motor Carrier Transportation

Project PI:  Ray Mundy

Currently less than two percent of the U.S. Railroad industry field workforce is comprised of women. Railroad field work has been and still is thoroughly dominated by men. While the U.S. motor carrier industry is somewhat better with closer to five percent women drivers, one can see there are still significant opportunities for women in these workforces. This project will examine the historic context and operational barriers perceived to be in these workforces. The project will also examine what steps and attitudes are necessary to encourage more female participation in these well-paying occupations. 

Wichita State University

Risk and Failure Resilience Quantification of Interdependent Transportation Systems

Project PI: Pingfeng Wang 

Complex interdependencies between critical transportation infrastructure systems exacerbate the consequences of initial failure events through cascading failure effects and damage propagation. To address an increasing demand to develop highly resilient transportation infrastructure systems, this research project will create a Bayesian network (BN) based probabilistic platform for analysis and design that enables not only interdependency between components and subsystems being ultimately considered, but also resilience realization through system design and resilience restoration by optimized failure mitigation/recovery before or after major adverse events.

This research is motivated by the emerging need for developing high-reliability low-cost critical interdependent transportation infrastructure systems, in which reliable functions for each subsystem and reliable dependencies across subsystems are required to maintain desired functionality in the face of system failures due to major natural disasters or gradual aging effects. This research project will explore the gap between quantitative and qualitative assessment of engineering resilience for complex transportation infrastructure systems. A conceptual framework is proposed for modeling engineering resilience, and a Bayesian network is employed as a quantitative tool for assessing and analyzing engineering resilience. A case study of an aircraft manufacturing supply chain demonstrates the developed tools. The developed approach would empower system designers to better grasp the weaknesses and strengths of their own systems against system disruptions induced by adverse failure events.

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