Concrete compressive strength prediction modelling utilising ensemble-deep-learning framework

Mu'tasime Abdel-Jaber, Ma'en Abdel-Jaber, Rob Beale and Nisrine Makhoul

International Journal of Mathematics in Operational Research, 2026, vol. 34, issue 1, 73-99

Abstract: The unique ensembled-deep-learning model used in this work is used to estimate the compressive strength of concrete automatically. These raw data will be pre-processed using a data-cleaning technique. Then, characteristics based on Yule's coefficient, Pearson's coefficient, and Percentile coefficient will be retrieved from the pre-processed data, along with statistical features. A new ensembled-deep-learning model will be developed using the extracted features to predict the concrete strength. A new hybrid optimisation approach called the hybrid poor rich owl algorithm (HPROA) is applied to adjust CNN's weight to increase the projected model's capacity for accurate prediction. A conceptual HPROA is the fusion of the common poor and rich optimisation (PRO) and owl optimisation will result in the hybrid optimisation model that is provided. The implementation is performed using the MATLAB software. Compared to already used methods, the suggested model's performance is evaluated and the obtained mean square error (MSE) is zero.

Keywords: compressive strength; concrete; ensembled-deep-learning model; data cleaning technique; multilayer perceptron; MLP; convolutional neural network; CNN; gated recurrent units; GRUs. (search for similar items in EconPapers)
Date: 2026
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