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Optimizing Sample Size for Concrete Strength, Uncertainty and Cost Reduction

Palma, Vittorio LU ; Celati, Simone LU orcid ; Natali, Agnese ; Salvatore, Walter and Thöns, Sebastian LU (2026) IABSE Symposium Copenhagen 2026: Bridging Advanced Technologies - Structural Innovation In IABSE Symposium Copenhagen 2026: Bridging Advanced Technologies - Structural Innovation 2. p.876-884
Abstract

For the experimental assessment of concrete material properties, estimating compressive strength is crucial for the calculation of structural safety and optimising testing campaigns. However, the optimal number of tests required to comply with a target reliability while balancing effort has rarely been analysed in the past. This work introduces an approach combining statistical modelling and uncertainty analysis to evaluate how sample size influences prediction accuracy and structural reliability, risks and testing costs. Based on core samples of concrete from an existing structure, prediction intervals are propagated into reliability calculations against a target level. A parametric study shows uncertainty decreases rapidly with an... (More)

For the experimental assessment of concrete material properties, estimating compressive strength is crucial for the calculation of structural safety and optimising testing campaigns. However, the optimal number of tests required to comply with a target reliability while balancing effort has rarely been analysed in the past. This work introduces an approach combining statistical modelling and uncertainty analysis to evaluate how sample size influences prediction accuracy and structural reliability, risks and testing costs. Based on core samples of concrete from an existing structure, prediction intervals are propagated into reliability calculations against a target level. A parametric study shows uncertainty decreases rapidly with an increasing number of tests, up to a threshold beyond which further gains are not cost-efficient. The procedure identifies the sample size that balances effort and uncertainty reduction, providing a first step towards a tool for planning and optimising testing campaigns.

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Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Compressive strength of concrete, Diagnostic testing of existing structures, Reinforced concrete structures, Reliability analysis, system modelling
host publication
IABSE Symposium Copenhagen 2026 : Bridging Advanced Technologies - Structural Innovation - Bridging Advanced Technologies - Structural Innovation
series title
IABSE Symposium Copenhagen 2026: Bridging Advanced Technologies - Structural Innovation
volume
2
pages
9 pages
conference name
IABSE Symposium Copenhagen 2026: Bridging Advanced Technologies - Structural Innovation
conference location
Copenhagen, Denmark
conference dates
2026-04-21 - 2026-04-24
external identifiers
  • scopus:105040815745
ISBN
9798331335489
DOI
10.2749/copenhagen.2026.0884
language
English
LU publication?
yes
additional info
Publisher Copyright: © (2026) by International Association for Bridge and Structural Engineering (IABSE) All rights reserved.
id
2c57e8bd-e618-49f8-8f31-2d89f4f25a5b
date added to LUP
2026-07-27 10:45:55
date last changed
2026-08-11 13:12:17
@inproceedings{2c57e8bd-e618-49f8-8f31-2d89f4f25a5b,
  abstract     = {{<p>For the experimental assessment of concrete material properties, estimating compressive strength is crucial for the calculation of structural safety and optimising testing campaigns. However, the optimal number of tests required to comply with a target reliability while balancing effort has rarely been analysed in the past. This work introduces an approach combining statistical modelling and uncertainty analysis to evaluate how sample size influences prediction accuracy and structural reliability, risks and testing costs. Based on core samples of concrete from an existing structure, prediction intervals are propagated into reliability calculations against a target level. A parametric study shows uncertainty decreases rapidly with an increasing number of tests, up to a threshold beyond which further gains are not cost-efficient. The procedure identifies the sample size that balances effort and uncertainty reduction, providing a first step towards a tool for planning and optimising testing campaigns.</p>}},
  author       = {{Palma, Vittorio and Celati, Simone and Natali, Agnese and Salvatore, Walter and Thöns, Sebastian}},
  booktitle    = {{IABSE Symposium Copenhagen 2026 : Bridging Advanced Technologies - Structural Innovation}},
  isbn         = {{9798331335489}},
  keywords     = {{Compressive strength of concrete; Diagnostic testing of existing structures; Reinforced concrete structures; Reliability analysis; system modelling}},
  language     = {{eng}},
  pages        = {{876--884}},
  series       = {{IABSE Symposium Copenhagen 2026: Bridging Advanced Technologies - Structural Innovation}},
  title        = {{Optimizing Sample Size for Concrete Strength, Uncertainty and Cost Reduction}},
  url          = {{http://dx.doi.org/10.2749/copenhagen.2026.0884}},
  doi          = {{10.2749/copenhagen.2026.0884}},
  volume       = {{2}},
  year         = {{2026}},
}