Review and Evaluation of the Separation Factor Approach for Structural Reliability

Studies have indicated that resistance factors calculated using a first-order second moment reliability method that uncouples the load effect from the resistance, termed the “separation factor approach” (SFA), differ considerably from those calculated using more accurate methods that also consider statistical variations in both the load effects and resistance. This can be attributed, in part, to the SFA implementing a separation factor, α, equal to 0.55, which was determined from tentative loading criteria and statistics in the 1970s. This paper amalgamates the disparate literature/background on the SFA and investigates its sources of error to illustrate its inherent assumptions and limitations. Three studies are conducted whose results are used to recommend appropriate separation factors (with associated bounds) for use in the SFA when determining resistance factors for steel components.It is found that α = 0.55 was calibrated to an atypical range of live-to-dead load ratios and small values of VR, which undermines its applicability when used in conjunction with modern-day statistics. Despite this, α = 0.55 is found to perform well when the reliability index, β, is equal to 3.0. For β = 3.5 and β = 4.0, α = 0.70 and α = 0.80, respectively, give results that agree with more accurate reliability methods at a live-to-dead load ratio of 3.0.

Authors

Institutions

Publication Details

Journal
Engineering Journal
Published
2025-07-01
DOI
https://doi.org/10.62913/engj.v62i3.1346
Primary Topic
Concrete Corrosion and Durability
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Review and Evaluation of the Separation Factor Approach for Structural Reliability

Kyle Tousignant, Jeffrey A. Packer, Isabelle Jollymore
1 links
Engineering Journal
Concrete Corrosion and Durability
article

Review and Evaluation of the Separation Factor Approach for Structural Reliability

Kyle Tousignant, Jeffrey A. Packer, Isabelle Jollymore
article en

Abstract

Studies have indicated that resistance factors calculated using a first-order second moment reliability method that uncouples the load effect from the resistance, termed the “separation factor approach” (SFA), differ considerably from those calculated using more accurate methods that also consider statistical variations in both the load effects and resistance. This can be attributed, in part, to the SFA implementing a separation factor, α, equal to 0.55, which was determined from tentative loading criteria and statistics in the 1970s. This paper amalgamates the disparate literature/background on the SFA and investigates its sources of error to illustrate its inherent assumptions and limitations. Three studies are conducted whose results are used to recommend appropriate separation factors (with associated bounds) for use in the SFA when determining resistance factors for steel components.It is found that α = 0.55 was calibrated to an atypical range of live-to-dead load ratios and small values of VR, which undermines its applicability when used in conjunction with modern-day statistics. Despite this, α = 0.55 is found to perform well when the reliability index, β, is equal to 3.0. For β = 3.5 and β = 4.0, α = 0.70 and α = 0.80, respectively, give results that agree with more accurate reliability methods at a live-to-dead load ratio of 3.0.

Engineering JournalVol. 62(3)
Dalhousie University (CA), University of Toronto (CA)
Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 83%
Concrete Corrosion and Durability
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.