CIAM-COR-R37
Research Team
PI: Tong Qiu, Ph.D., P.E., Penn State
Funding Sources
Penn State Core Funds — $87,336
PennDOT Match — $86,695
Total Project Cost — $174,031
Agency ID or Contract Number
69A3551847103
Start and End Dates
01/03/2022 — 08/03/2023
Project Description
The purpose of this project is to develop artificial intelligence (AI) models for advance warning of rainfall-induced landslides for unstable slopes above or below state maintained roadways in Pennsylvania. Landslides are a significant geologic hazard throughout most of southwestern Pennsylvania and in certain other parts of the state (Delano and Wilshusen 2001). The average annual direct and indirect cost of landslides is in the tens of millions of dollars in the state. Landslides cause damage to utilities, buildings, and transportation routes, which, in turn, creates travel delays and other side effects. For example, during the rainy season of 2019, PennDOT’s Pittsburgh district concurrently dealt with 95 landslides, among which the Reis Run slide resulted in the closure of Reis Run Road on May 31, 2019, causing significant safety risk and inconvenience to the traveling public and local residents. As more land is being developed, with more frequent extreme rainfalls associated with the climate change, an increased frequency of rainfall-induced landslides is likely in the coming decades. Roadway reconstruction costs, travel delays, and other side effects could be significantly reduced if an advanced warning system of rainfall-induced landslides could be provided to, and implemented by, transportation officials to address rainfall-induced landslides before they affect the safety, inconvenience and cost to the public.