Developing mass timber building inspection methodology using resistance drilling and artificial intelligence
Graduate Student Name
Opeyemi Odule
Email Address
Faculty mentor/Supervisor
Gerald Presley
Email Address (Faculty mentor/Supervisor)
Department Affiliation
Wood Science & Engineering
Job Location
Richardson hall, College of Forestry, Oregon state university, Corvallis
Description of project or research opportunity
My research focuses on finding a better way to detect early-stage wood decay in mass timber building materials, specifically Cross-Laminated Timber (CLT) and Mass Plywood Panels (MPP). These materials are increasingly being used in modern construction, but there's currently no reliable method to check for deterioration while they're already in use in a building.
To address this, I'm using a tool called a resistograph, which drills into the wood and measures resistance, to see if it can pick up signs of decay early on. I'm comparing those readings with how much mass the wood has lost and changes in its density to see how well the tool works.
The goal is to develop a simple, practical method that engineers and building managers can use to inspect mass timber structures before any serious damage occurs.
To address this, I'm using a tool called a resistograph, which drills into the wood and measures resistance, to see if it can pick up signs of decay early on. I'm comparing those readings with how much mass the wood has lost and changes in its density to see how well the tool works.
The goal is to develop a simple, practical method that engineers and building managers can use to inspect mass timber structures before any serious damage occurs.
Tasks student will perform
The student will assist with data collection by operating resistance drilling devices to test wood specimens. Prior to beginning, they will receive hands-on training on how the equipment works and safe operating procedures. Additional tasks may include sample preparation, recording measurements, and organizing data. The student will work 8–10 hours per week over the course of 8–10 weeks, providing consistent and dedicated support throughout the data collection phase.
Special skills required
Time management, team player and excellent communication skills
Proposed dates of employment
-
Anticipated hours worked per week
8 - 10