Developing a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength

Concrete compressive strength is a critical parameter in construction and structural engineering. Destructive experimental methods that offer a reliable approach to obtaining this property involve time-consuming procedures. Recent advancements in artificial neural networks (ANNs) have shown promise in simplifying this task by estimating it with high accuracy. Nevertheless, conventional ANNs often require deep networks to achieve acceptable results in cases with large datasets and where generalization is required for a variety of mixtures. This leads to increased training durations and susceptibility to noise, causing reduced accuracy and potential information loss in deep networks. In order to address these limitations, this study introduces a novel multi-lobar artificial neural network (MLANN) architecture inspired by the brain's lobar processing of sensory information, aiming to improve the accuracy and efficiency of estimating concrete compressive strength. The MLANN framework employs various architectures of hidden layers, referred to as "lobes," each with a unique arrangement of neurons to optimize data processing, reduce training noise, and expedite training time. Within the study context, an MLANN is developed, and its performance is evaluated to predict the compressive strength of concrete. Moreover, it is compared against two traditional cases, ANN and ensemble learning neural networks (ELNN). The study results indicated that the MLANN architecture significantly improves the estimation performance, reducing the root mean square error by up to 32.9% and mean absolute error by up to 25.9% while also enhancing the A20 index by up to 17.9%, ensuring a more robust and generalizable model. This advancement in model refinement can ultimately enhance the design and analysis processes in civil engineering, leading to more reliable and cost-effective construction practices.