Articles
DOI DOI: 10.62441/actainnovations.v63i.1164

RELIABILITY ANALYSIS OF REINFORCED CONCRETE MEMBERS UNDER HIGH EPISTEMIC UNCERTAINTY USING MONTE CARLO SIMULATION AND ROSENBLUTH (2N+1)

Alaa A. Abd Ali1
  1. 1 Department of Civil Engineering, Faculty of Engineering, University of Kufa

Abstract

This study investigates the effect of high epistemic uncertainty on the flexural reliability of a singly reinforced concrete beam. The reliability index β was evaluated as a function of the Coefficient of Variation (COV) of four basic random variables: concrete compressive strength (), steel yield strength (fy), service dead load (D) and service live load (L). Two reliability methods were employed: Monte-Carlo Simulation (MCS), and the Rosenblueth (2n+1) point estimate method. Results were checked against ACI 318 target reliability index β=3.5 and Eurocode 2 / EN 1990 RC2 target reliability index β=3.8. Results demonstrate excellent agreement between MCS and Rosenblueth (2n+1) with an error less than 4%, confirming its validity as a low-cost alternative. Compressive strength of concrete (), exhibited high sensitivity, where poor quality control with no testing can cause failure of both ACI and Eurocode, with Eurocode failing earlier. Steel reinforcement was found to be the most reliable parameter and never violates code limits within realistic ranges. Service dead load showed negligible influence and alone cannot cause code violation. The key finding is that service live load is the governing parameter for reliability and the only load parameter that directly compromises ACI318 within realistic uncertainty levels. At high uncertainty representing change of use or overcrowding, β falls below ACI 3.5 and Eurocode 3.8. It is recommended to limit COV(L) ≤ 0.33 to maintain target reliability, and that controlling live load assumptions is more effective than improving material quality.

How to Cite

Ali, A. A. A. (2026). RELIABILITY ANALYSIS OF REINFORCED CONCRETE MEMBERS UNDER HIGH EPISTEMIC UNCERTAINTY USING MONTE CARLO SIMULATION AND ROSENBLUTH (2N+1). ACTA INNOVATIONS, 63, 944–953. https://doi.org/10.62441/actainnovations.v63i.1164
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