Integrating three smart predictive models using a power-law committee machine for the prediction of compressive strength in masonry made of clay bricks and cement mortar

Abstract

Due to the importance of the compressive strength of masonry (CSM) prisms made of clay bricks and cement mortar in the design of masonry structures, it is crucial to develop a reliable method for accurate prediction. This paper presents an innovative approach for estimating CSM by integrating an optimized convolutional neural network (OCNN), extreme learning machine (ELM), and decision tree (DT) using a power-law committee machine (PLCM). The strategy consists of two steps: firstly, three regression approaches (OCNN, ELM, and DT) are developed to model CSM, and secondly, the results of these methods are combined through a PLCM. The convolutional neural network is enhanced using the cuckoo search (CS) algorithm to determine its weights and bias, thereby improving its ability to model CSM accurately. The CS algorithm is also employed in the PLCM structure to extract the optimal contribution of each individual model in the overall prediction. To statistically evaluate the effectiveness and efficiency of the developed models and compare them with previously established empirical correlations for estimating CSM values, various visualization methods and statistical parameters are utilized. The evaluation demonstrates that OCNN, ELM, and DT can estimate CSM with remarkable accuracy and further confirms that PLCM outperforms its individual elements as well as empirical methods. This study provides evidence of the significant improvement in the performance of PLCM in modeling CSM.