Date of Award

2025

Document Type

Open Access Master's Thesis

Degree Name

Master of Science in Civil Engineering (MS)

Administrative Home Department

Department of Civil, Environmental, and Geospatial Engineering

Advisor 1

Raymond A. Swartz

Committee Member 1

Qingli Dai

Committee Member 2

Daniel M. Dowden

Abstract

In cold regions in the United States, residential timber structures are susceptible to roof collapses due to heavy snow accumulation. The recent updates in ASCE 7-22 have introduced reliability-targeted ground snow loads, leading to significant increases in design snow loads for certain areas. Despite this development, existing buildings in these regions designed under historical code requirements may be under-designed for snow load. This fact provides motivation to owners of these structures to pursue effective monitoring and mitigation strategies to prevent structural failures. This research focuses on the development of an early warning system utilizing cost-effective slope monitoring sensors to estimate snow loads on residential and commercial timber roofs. This study focuses on beam-supported flat roofs. By continuously assessing roof slope changes, the system aims to provide real-time alerts for potential structural overloads due to excessive snow. Alerts will prompt owners to remove snow from the roof. The study also incorporates Monte Carlo sampling methods to calibrate the proposed system, characterizing its sensitivity across various scenarios. Furthermore, the research addresses some implications of climate change, which has led to more frequent and severe snowstorms in mid-latitude regions. The findings aim to inform design considerations and safety protocols, aligning with the enhanced requirements outlined in ASCE 7-22. The proposed system offers a proactive and condition-based approach to structural safety, potentially reducing the risk of roof collapses and enhancing the resilience of residential buildings in snow-prone areas.

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