Estimating Compressive Strength of Concrete Using Neural Electromagnetic Field Optimization

Abstract

Concrete compressive strength (CCS) is among the most important mechanical characteristics of this widely used material. This study develops a novel integrative method for efficient prediction of CCS. The suggested method is an artificial neural network (ANN) favorably tuned by electromagnetic field optimization (EFO). The EFO simulates a physics-based strategy, which in this work is employed to find the best contribution of the concrete parameters (i.e., cement (C), blast furnace slag (SBF), fly ash (FA1), water (W), superplasticizer (SP), coarse aggregate (AC), fine aggregate (FA2), and the age of testing (AT)) to the CCS. The same effort is carried out by three benchmark optimizers, namely the water cycle algorithm (WCA), sine cosine algorithm (SCA), and cuttlefish optimization algorithm (CFOA) to be compared with the EFO. The results show that hybridizing the ANN using the mentioned algorithms led to reliable approaches for predicting the CCS. However, comparative analysis indicates that there are appreciable distinctions between the prediction capacity of the ANNs created by the EFO and WCA vs. the SCA and CFOA. For example, the mean absolute error calculated for the testing phase of the ANN-WCA, ANN-SCA, ANN-CFOA, and ANN-EFO was 5.8363, 7.8248, 7.6538, and 5.6236, respectively. Moreover, the EFO was considerably faster than the other strategies. In short, the ANN-EFO is a highly efficient hybrid model, and can be recommended for the early prediction of the CCS. A user-friendly explainable and explicit predictive formula is also derived for the convenient estimation of the CCS.

Estimating the Concrete Ultimate Strength Using a Hybridized Neural Machine Learning

“…The models that were suggested in this work could achieve a reliable early analysis of the CSC based on the effects of mixture characteristics including cement, BFS, FA1, water, SP, CA, FA2, and age. However, it is worth discussing that the suggested model, i.e., the SMA-ANN, has achieved significant improvements with respect to some of the previous studies [43,[52][53][54]. Table 5 compares the accuracy indices of this study to those that were commonly used in the cited studies.…”

“…Regarding the 96% correlation observed for the prediction phase, the developed BAS-based ensemble was introduced as an efficient approximator. Likewise, Akbarzadeh et al [43] professed the outstanding accuracy of ANN tuned by electromagnetic field optimization (EFO) for predicting the CSC. The EFO algorithm outperformed several compatible techniques including sine cosine algorithm (SCA) and cuttlefish optimization algorithm (CFOA).…”

Enhancing Ultimate Bearing Capacity Assessment of Rock Foundations using a Hybrid Decision Tree Approach

“…They streamline labor-intensive engineering procedures and substantially contribute to these projects' cost efficiency. Additionally, numerous research studies utilize ML techniques to estimate the 𝑄 𝑢 (ultimate bearing capacity) of rocks [7,8]. Yagiz et al [9] presented ANN models, whereas Jahed Armaghani et al [10] introduced adaptive neuro-fuzzy inference system (𝐴𝑁𝐹𝐼𝑆) models to forecast rock strength.…”

Concrete Surface Crack Detection with Convolutional-based Deep Learning Models

“…In recent years, an increasingly prominent pattern has emerged where machine learning and deep learning models are being utilized for image analysis across a range of applications, encompassing image recognition, classification, and segmentation in the realms of engineering and medicine [2][3]. However, in contrast to conventional and traditional methods employed in diverse civil engineering applications [4][5][6][7][8][9], deep neural networks, functioning as end-to-end learning models, exhibit the capability to automatically extract features from images. The fusion of deep learning and image analysis has ushered in a transformative era in the domain of structural assessment.…”