Updated 2/3/2023: **All the positions in the Head Research Group for Fall 2023 have been filled to date. More positions may become available starting the Spring 2024 semester, pending funding of submitted proposals. Thank you for expressing your interest. –Dr. Head (email: head@udel.edu)
Group photo on 12/8/2022!
Group photo (2020)!
Another successful bridge load testing using digital image measurements with the research team!
2019 – practice run with the Imetrum system!
Madison and Drew conducting preliminary measurements for sensor calibration
Madison and Drew near Slaughter Beach, DE installing a water level pressure sensor for remote monitoring (2021)!
Dr. Head managing the data from a bridge load test!
Shake table testing verification at Lehigh University (January 2021)
Dr. Head setting up for a bridge load test!
Wael Aloqaily
KJ Olsen in the lab conducting a compression test for concrete cylinders with embedded plastics.
Sajjad Safari
from left to right: M. Head, H. Shenton, C. Aloupis, M. Chajes, and M. Santare
Shaymaa Obayes
SEI travel scholarship recipients to attend 2022 Structures Congress in Atlanta, GA
Bridge field testing using digital image measurements compared to mounted sensors
Dr. Head staying busy!
Luke Timber, GRA, preparing iMetrum equipment to capture digital image measurements
Training using Instron 1331 for dynamic testing
Bring on the concrete!
Instron 1331-MTS upgrade and training
The Head Research Group addresses real-world challenges related to structural resilience, especially during earthquakes, using numerical simulations and in some cases scaled structural testing for condition assessment and to develop performance-based design methodologies. Dr. Head has secured competitive state and federal research funding, which enables the group to make novel contributions in three (3) core and cutting-edge areas:
structural monitoring and condition assessment using load testing and vision-based measurements to evaluate structural behavior and create “digital twins”
seismic assessment of buildings to predict peak floor accelerations during earthquakes using a machine learning approach
coastal infrastructure assessment due to extreme weather-related events, saltwater intrusion, and how nature-based solutions may mitigate these impacts