PITA Newsletter: Spring 2023

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NEWSLETTER www.pitapa.org | Spring 2023

A Commonwealth-University-Industry Partnership for Economic Development through Research, Technology, and Education


Directors’ Letter PITA Newsletter 2023

The Pennsylvania Infrastructure Technology Alliance (PITA) has connected Pennsylvania’s companies with the Commonwealth’s world-class university researchers and their students for the past 25 years, promoting economic development in Pennsylvania. Funded by the Pennsylvania Department of Community and Economic Development (DCED), PITA helps Pennsylvania increase the state’s market competitiveness through the development of new technologies and process improvements. As we recognize PITA’s 25th anniversary this year, we are proud of the program’s strong history of working with Pennsylvania companies and students to foster economic growth in the state. The program has supported over 1,350 technology and process improvement projects in partnership with more than 550 Pennsylvania companies, obtaining more than two dollars of funding from industry and federal sources for every dollar of state funding. PITA has also mobilized more than 500 faculty members and over 2,250 students to work on Pennsylvania-specific technology, process improvement, and educational outreach projects, and has also enabled 15 startup companies to be created from PITA-sponsored technologies. In this edition of the PITA Newsletter, we highlight recent partnerships with: • Wabtec Corporation • Pittsburgh Regional Transit • HexSpline3D LLC, a Pittsburgh-based startup • The Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS), a U.S. DOT Region 3 University Transportation Center (UTC) We also profile Lehigh University’s Advanced Technology for Large Structural Systems (ATLSS) large-scale structural testing laboratory. As always, we welcome partnerships with new companies. Those interested in working with faculty and graduate students on short-term technology development or process improvement projects should contact the PITA associate directors: Chad Kusko, Lehigh University, chk205@lehigh.edu or Colleen Mantini, Carnegie Mellon University, cmantini@cmu.edu.



PITA Co-Director

PITA Co-Director

Carnegie Mellon University

Lehigh University



PITA Newsletter | 3

Improving the Technology That Monitors Railroad Infrastructure By Jacob Williamson-Rea | Mario Bergés, marioberges@cmu.edu | Katherine Flanigan, kflaniga@andrew.cmu.edu

Pennsylvania’s railways play a crucial role in freight distribution across the United States. According to the American Association of Railroads, Pennsylvania ranks first in the country in the number of operating freight railroads and is the fourth largest rail system in total miles. Despite recent efforts to improve the infrastructure of freight railroads and equipment nationwide, there are opportunities to make these systems safer, as evidenced by the recent catastrophe in East Palestine, Ohio. On February 3, 2023, a train carrying hazardous substances was derailed, likely due to mechanical failure. A research group led by Mario Bergés and Katherine Flanigan, University’s College of Engineering, is researching and developing advanced, cost-effective monitoring technologies to better assess the structural health of rail tracks and vehicles. They have teamed up with Wabtec Corporation, a rail technology company headquartered in Pittsburgh, PA, that manufactures products for locomotives, freight cars, and passenger transit vehicles. “As is the case with other infrastructure areas undergoing modernization, the rail transportation industry has seen a proliferation

Research associate Guillermo Montero pictured next to the lab-scale testbed used by the research team.

Carnegie Mellon University

professors of civil and environmental engineering at Carnegie Mellon

of sensing infrastructure,” says Bergés. “There is a significant opportunity to conduct more accurate, fine-grained, and useful structural health monitoring of railroad infrastructure using the sensing

“We’ve narrowed in on broken rail because broken rail is the

technology that is already onboard rail vehicles today.”

primary reason we see accidents and derailments in the rail industry,” Bergés says. “To curb those issues would have a huge

Through the partnership with Wabtec, Bergés says that the team’s


main goal is to use accelerometers that are already onboard the train as part of a cyber physical system (CPS)—a physical system with a

Flanigan says that the ability to detect potential cracks or

cyber component—to detect signs of cracked or damaged rail and

structural issues in real time will significantly improve rail safety,

automatically issue management actions.

especially when compared to how railroad systems currently handle monitoring tracks.

ways to improve rail safety with new technologies. It's great to work with CMU to take emerging data analytics techniques in infrastructure monitoring and applying them to a new context. The team has been able to quickly adapt an existing testbed and show some promising results." Dr. James D. Brooks, principal scientist at Wabtec Corporation, Advanced Technologies

“The ability to detect cracked rails ultimately minimizes catastrophic outcomes,” says Flanigan. “Many of today’s operations involve employees walking along tracks and visually inspecting rails for cracks or relying on sparse wayside detection technologies.” By implementing sensing computation technology, the research team anticipates an automated and scalable way to monitor tracks more frequently and alert rail managers to potential concerns sooner. Flanigan says that the collaboration with Wabtec has been incredibly insightful thanks to the company’s industry knowledge. Continued on page 7


"The industry is always looking for

PITA Newsletter | 4

Fatigue Life Estimation of Bridges with Smart Mobile Sensing By Chad Kusko | Shamim Pakzad, snp208@lehigh.edu Bridges undergo significant vibrations and repeated stress variations during their operational life. Fatigue cracking and eventual fatigue failure of bridges are possible outcomes, highlighting the need for scalable life cycle analysis to estimate the remaining useful life of a structure accurately. Fatigue life is typically estimated by the number of stress cycles a structure can withstand before failure. Engineers traditionally use strain gauges—sensors that measure changes in force or stress applied to an object—to collect strain measurements and assess the fatigue life of bridges. However, large-scale deployment of wired strain gauges

Lehigh University

poses several limiting factors: it can be expensive; it requires significant labor and material resources; and it often requires additional spatial data. To overcome

drivers) and uploads the data to a cloud server. The acquired data

these constraints, engineers would benefit from a sensing

is aligned, categorized, and processed to train the deep learning

strategy that supports information extraction from

framework. The deep learning framework is then used to estimate

inexpensive data sources with higher spatial precision.

dynamic strain for complex bridge structures.

Shamim Pakzad, a professor in Lehigh University’s

“This project utilizes acceleration data collected by mobile device

Department of Civil and Environmental Engineering, and

users traveling across a bridge, which helps to provide up-to-date

his research team are using PITA funding to develop a

information on the structure’s condition in an efficient manner,”

novel approach to solve this challenge. In collaboration

says Pakzad. “This data is then fed to a deep learning-based

with the Center for Integrated Asset Management for

framework to estimate dynamic properties of the structure and,

Multimodal Transportation Infrastructure Systems

ultimately, the strain levels that heavy traffic can cause.”

(CIAMTIS), a U.S. DOT Region 3 University Transportation Center, Pakzad’s project focuses on employing deep

Smart mobile sensing presents an intriguing opportunity to

learning (or deep neural networks) with physics-based

help engineers assess the fatigue life of bridge structures in a

foundations to exploit sensed data from structural health

manner that is more efficient and less expensive than traditional

monitoring applications.

approaches. Pakzad indicates that future work is required to generalize the deep learning framework and validate the data

“Deep learning algorithms are designed to learn from

collection, including enhancing the existing networks that will

data,” explains Pakzad. “As such, their performance

enable the estimate of strains in other directions.

improves with more data, which is ideal for the proposed crowdsensing approach.” Unlike strain, acceleration can be measured relatively inexpensively through mobile sensing, an area of significant interest across many engineering fields. Mobile


sensing eliminates the spatially restrictive feature of a fixed sensing approach, such as the use of strain gauges. This project uses a mobile phone application to collect acceleration and GPS data from drivers crossing a bridge. The research team captures acceleration information from a series of mobile sensors (mobile phones of multiple

PITA Newsletter | 5

Modeling Material Transport in Neurons for Biological Neural Circuits Design

Neurons in the brain grow by moving essential materials

the microtubules, particles amass in the axon and starve the

around the cell structure through a process known as

connected dendrites, causing neural degeneration.

intracellular material transport. Material transport is especially crucial to ensure vital elements, such as proteins

Traffic jams occur in two common ways. Just as automotive traffic

and mitochondria, reach their proper destinations at

lanes converging at a tunnel entrance can cause stoppages, a lack

appropriate times for the development, function, and

of sufficient microtubules in the axon limits the volume of neural

survival of neural circuits.

material that flows to the dendrites. Traffic jams also develop when the streamlined tube shape of microtubules becomes

Disrupting this process can lead to extreme swelling of

tangled, known as “swirl” transport patterns, causing buildup.

Carnegie Mellon University

By Nathan Snizaski | Jessica Zhang, jessicaz@andrew.cmu.edu | Vickie Webster-Wood, vwebster@andrew.cmu.edu

the axon, the elongated cable that connects the cell body with the branched dendrites at the ends of the nerve cell. The buildup of unreleased chemicals, known as “traffic jams,” limits the flow of critical substances across the nerve cell and impairs communication with other neurons. Traffic jams have been observed in neurological and neurodegenerative diseases, such as Alzheimer’s, Huntington’s, and Parkinson’s disease. A team of researchers at Carnegie Mellon University has developed an innovative, 3D-modeling technology to understand material transport regulation and its application in biological neural circuits design. “To understand different transport phenomena occurring in growing neurons, we study the particles that can attach to microtubules along the axon,” says Jessica Zhang, a professor of mechanical engineering at Carnegie Mellon University’s College of Engineering. Microtubules are thin, cylindrical polymers in the axon that quickly move particles the neuron. “When a cell’s microtubules align with the axon, this can

Healthy material transport (a) vs unhealthy material transport (b) in neurons. Created with BioRender.com.

provide an ‘escalator pathway’ effect for particles to move very quickly and efficiently along that microtubule,” says Zhang. However, when transport problems occur along

Continued on page 7


across the axon to the branch-like dendrites at the ends of

PITA Newsletter | 6

NHERI Lehigh Experimental Facility expands lab space for cyber-physical testing laboratory By Chad Kusko | James Ricles, jmr5@lehigh.edu

Lehigh University’s Advanced Technology for Large Structural Systems (ATLSS) Engineering Research Center is a national center for research and education on structures and infrastructure materials. As a large-scale structural testing laboratory, ATLSS provides a unique set of testing capabilities for Pennsylvania’s technology companies and has aided researchers with structural testing for several PITA projects. ATLSS is also the home to Lehigh’s Natural Hazards Engineering Research Infrastructure (NHERI) Real-Time Multi-Directional (RTMD) Experimental Facility, which provides the Commonwealth with a unique facility to Lehigh University

investigate community resilience to natural hazards. Designed to be a world-class, open-access facility, researchers can leverage specialized equipment to conduct accurate, large-scale, multi-directional simulations of

Cyber-physical testbed for characterization testing of a nextgeneration Banded Rotary Friction Damper for mitigating dynamic vibrations in structural systems.

the effects of natural hazard events. Investigators can simulate the impact of earthquakes and wind events on

“The PITA funds have leveraged federal funds from the National

civil infrastructure systems—such as buildings, bridges, and

Science Foundation to help to expand the features of NHERI

industrial facilities—with potential soil-foundation effects.

Lehigh through RCPSS,” says Ricles. “NHERI Lehigh can now offer Pennsylvania companies state-of-the-art research resources

Through funding from the National Science Foundation and

that can be utilized to derive solutions to challenging problems,

partial support from PITA, the NHERI Lehigh Experimental

including those associated with multi-hazard resiliency of our

Facility has expanded its lab space for cyber-physical

community and the civil infrastructure. NHERI Lehigh remains

testing. With an additional 4,000 square feet of lab space,

at the forefront of large-scale structural testing with focuses on

the NHERI Lehigh Real-time Cyber-Physical Structural

natural hazards simulations, including earthquakes and wind

Systems (RCPSS) Testing Laboratory can accommodate a


broader range of research applications while supporting education and community outreach activities, such as training in cyber-physical systems. The RCPSS Testing Laboratory features five testbeds that have dedicated dynamic actuators along with a multidirectional shake table. A real-time integrated control system connects the testbeds and shake table, enabling users of the RCPSS to conduct concurrent testing that is synchronized by simultaneously engaging the various testbeds and the shake table.


James Ricles, professor of structural engineering and director of the NHERI Lehigh Experimental Facility, believes the additional capabilities at NHERI Lehigh provide significant testing opportunities for Pennsylvania companies.

Cyber-physical multi-directional shake table for performance testing of equipment floor isolation systems in buildings subject to wind and seismic loading.

Continued from page 3

“Eventually, our research could lead to a system in which cracks

Bergés and Flanigan have been refining their team’s

are identified almost immediately,” Flanigan says. “For this to be

technology in their lab, where in collaboration with Wabtec,

successful, the train that causes the crack must be the train that

they have built a testbed complete with a scaled rail model:

identifies the crack. This way, the detection is made well before

rails, wheels, suspension systems, dynamics tuned to an

any other rail cars travel over the cracked rail, preventing any

actual freight vehicle, and more.

PITA Newsletter | 7

Monitoring Railroad Infrastructure

chance of derailment.” Soon, they will test their technology at Wabtec’s testbed As an added bonus, this technology will not only improve overall

in Erie, PA, to further refine it and take the next step to

rail safety but will cut maintenance costs as well. On-board, real-

implement it in the real world.

time detection means that employees will know about cracks in rails almost immediately, which will allow them to take preventive measures sooner than relying on employees’ visual inspections of the rails.

Modeling Material Transport in Neurons Continued from page 5

“With the isogeometric analysis solver, we can use quadratic and cubic elements to represent cylinders, circles, and other nonlinear structures,” says Zhang. “Because we’re using higher-order elements, we can easily reduce the degree of freedom and still achieve accurate representations of complex neural circuits to solve the problem very efficiently.” Vickie Webster-Wood, a professor of mechanical engineering at Carnegie Mellon University’s College of Engineering, uses experimental approaches to validate Zhang’s computational models by assessing the growth of neural circuits on micropatterned culture substrates. The research team uses soft lithography techniques to constrain how neurons can grow in different environments and monitors neural activity over time. “We can stain specific components within the cells and watch how they move as these neurons are growing,” says Webster-Wood. “The data collected of how neurons actually grow under these constraints can then be used to validate the models, which then helps us iteratively improve our modeling tools.”

“From the experimental side, we are fascinated by how molecules and compounds move around inside neurons, which is a very confined space,” says Webster-Wood. By understanding and predicting how the neurons will grow in different conditions, Webster-Wood believes that researchers can use the information to design networks of neurons either to study certain disease phenomena or to build controllers for biohybrid robots. Webster-Wood is excited by the potential application of the team's research focus in biohybrid robotics and hopes that Pennsylvania can become a leading hub for this emerging technology area. “One of the things I’m interested in is how to build these neural circuits and how to get them to provide the behaviors we want as roboticists,” says Webster-Wood. “The only way we can really do that is to make sure we understand and can predict how they grow and how they transmit information, both electrically and through chemical and material transport.” Currently, the team is growing and monitoring healthy neurons. As they gather more experimental data, the team hopes to contribute important insights into the physiology and disease of neurons, as well as biohybrid robotics applications.


In partnership with Pittsburgh-based startup HexSpline3D LLC, the team developed a novel isogeometric analysis (IGA) solver to model the complicated structure of neurons. IGA is a higher-order finite element technology that enables accurate 3D modeling with less computational burden than traditional finite element analysis (FEA).



PITA is an industry-led program that enables companies to identify opportunities for Lehigh University and Carnegie Mellon University, and for the universities to provide expertise and capabilities, through faculty and students, that the companies may not otherwise be able to access. Pennsylvania companies gain access to faculty expertise, university equipment, and students. University faculty and students are afforded the opportunity to work on real-world, market-driven challenges confronting Pennsylvania companies. Faculty and students assist companies in creating technology of the future and enhancing the competitiveness of Pennsylvania companies with the goal of the creation of jobs in Pennsylvania and the retention of highly trained/educated students in Pennsylvania. PITA Technology focus areas include: • Transportation • Telecommunications and information technology • Facilities

Contacts Nathan Snizaski Chief Editor Carnegie Mellon University nsnizask@andrew.cmu.edu 412-268-9157 Chad Kusko PITA Co-Associate Director Lehigh University chk205@lehigh.edu 610-758-5299 Colleen McCabe Mantini PITA Co-Associate Director Carnegie Mellon University cmantini@cmu.edu 412-268-5314

• Water systems • Energy & environment • Public health & medicine • Hazard mitigation & disaster recovery


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