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Comparative Evaluation of Ordinary Least Square Regression and Principal Component Regression Models for Reliability-Based Optimization of Concrete Mix Proportions
Rachna Aggarwal
Pages - 1 - 18 | Revised - 01-10-2025 | Published - 31-10-2025
Published in International Journal of Engineering (IJE)
MORE INFORMATION
KEYWORDS
Reliability, Concrete, Optimization, Principal Component Regression.
ABSTRACT
This paper explores Reliability Based Design Optimization (RBDO) technique for finding optimal
concrete mixture compositions that are less sensitive to uncertainties involved in concrete mix
design process. The optimization problem is formulated to determine optimal concrete mix
parameters, namely, water content (w), fine aggregate content (fa), coarse aggregate content
(ca) and cement content (c). This is achieved by minimizing the cost of concrete for a given
compressive strength and target reliability. The compressive strength is considered for 28 days
and 56 days curing periods. Compressive strength models are developed using Ordinary Least
Square Regression (OLSR) and Principal Component Regression (PCR) techniques. SPSS 12.0
and MATLAB 5.3 are used to develop these models. An attempt has also been made to
demonstrate the effect of prediction models on optimal concrete mix parameters. The RBDO
problems are solved using Sequential Optimization and Reliability Assessment (SORA) method
which is implemented using Altair Hyperstudy 10.0. Optimal mixes for a wide range of target
compressive strengths and different reliability levels are reported. It is seen that optimization
results based on PCR models are more reliable than the results obtained using OLSR models.
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Dr. Rachna Aggarwal
Department of Mathematics
Mukand Lal National College
Yamuna Nagar, Haryana, 135001 - India
raggarwal.math@mlncollegeynr.ac.in
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