Abstract
This study introduces an improved artificial intelligence (AI) approach called intelligence optimized support vector regression (IO-SVR) for estimating the compressive strength of high-performance concrete (HPC). The nonlinear functional mapping between the HPC materials and compressive strength is conducted using the AI approach. A dataset with 1,030 HPC experimental tests is used to train and validate the prediction model. Depending on the results of the experiments, the forecast outcomes of the IO-SVR model are of a much higher quality compared to the outcomes of other AI approaches. Additionally, because of the high-quality learning capabilities, the IO-SVR is highly recommended for calculating HPC strength.
Highlights
High-performance concrete (HPC) is widely used in the construction sector in a variety of projects because its superior strength, workability and durability surpass those of regular concrete
The efficiency and applicability of the given hybrid model in predicting HPC strength on the basis of laboratory test records were assessed by benchmarking its performance relative to the performance levels of other artificial intelligence (AI) models (i.e., least squares support vector regression (LS-support vector regression (SVR)) and SVR)
This study established an innovative method for predicting HPC concrete strength on the basis of an HPC compressive test
Summary
High-performance concrete (HPC) is widely used in the construction sector in a variety of projects because its superior strength, workability and durability surpass those of regular concrete. The most critical property of HPC is its compressive strength. As HPC is being used more and more in the construction field, improving the forecasting capabilities of the compression strength of this concrete is extremely helpful in choosing the proper concrete mixtures [1,2]. The usual formula-based approaches restrict predictive functionality and have been proven to be unable to deliver acceptable performances, due to a variety of conditions and materials which could affect the compressive strength. Since the correlation between compressive strength and concrete materials are highly nonlinear, mathematical modeling of HPC is rather difficult, and in many cases inaccurate [3]
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