Optimized ANNs for predicting compressive strength of high-performance concrete

Hossein Moayedi,Amirali Eghtesad,Mohammad Khajehzadeh,Suraparb Keawsawasvong,Mohammed M. Al-Amidi,Bao Le Van 국제구조공학회 2022 Steel and Composite Structures, An International J Vol.44 No.6

Predicting the compressive strength of concrete (CSoC) is of high significance in civil engineering. The CSoC is a highly dependent and non-linear parameter that requires powerful models for its simulation. In this work, two novel optimization techniques, namely evaporation rate-based water cycle algorithm (ER-WCA) and equilibrium optimizer (EO) are employed for optimally finding the parameters of a multi-layer perceptron (MLP) neural processor. The efficiency of these techniques is examined by comparing the results of the ensembles to a conventionally trained MLP. It was observed that the ER-WCA and EO optimizers can enhance the training accuracy of the MLP by 11.18 and 3.12% (in terms of reducing the root mean square error), respectively. Also, the correlation of the testing results climbed from 78.80% to 82.59 and 80.71%. From there, it can be deduced that both ER-WCA-MLP and EO-MLP can be promising alternatives to the traditional approaches. Moreover, although the ER-WCA enjoys a larger accuracy, the EO was more efficient in terms of complexity, and consequently, time-effectiveness.

Slime mold and four other nature-inspired optimization algorithms in analyzing the concrete compressive strength

Hossein Moayedi,Yinghao Zhao,Loke Kok Foong,Quynh T. Thi 국제구조공학회 2024 Smart Structures and Systems, An International Jou Vol.33 No.1

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMAMLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

Metaheuristic-hybridized multilayer perceptron in slope stability analysis

Xinyu Ye,Hossein Moayedi,Mahdy Khari,Loke Kok Foong 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.26 No.3

This research is dedicated to slope stability analysis using novel intelligent models. By coupling a neural network with spotted hyena optimizer (SHO), salp swarm algorithm (SSA), shuffled frog leaping algorithm (SFLA), and league champion optimization algorithm (LCA) metaheuristic algorithms, four predictive ensembles are built for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The data used to develop the ensembles are provided from a vast finite element analysis. After creating the proposed models, it was observed that the best population size for the SHO, SSA, SFLA, and LCA is 300, 400, 400, and 200, respectively. Evaluation of the results showed that the combination of metaheuristic and neural approaches offers capable tools for estimating the FOS. However, the SSA (error = 0.3532 and correlation = 0.9937), emerged as the most reliable optimizer, followed by LCA (error = 0.5430 and correlation = 0.9843), SFLA (error = 0.8176 and correlation = 0.9645), and SHO (error = 2.0887 and correlation = 0.8614). Due to the high accuracy of the SSA in properly adjusting the computational parameters of the neural network, the corresponding FOS predictive formula is presented to be used as a fast yet accurate substitution for traditional methods.

Teaching-learning-based strategy to retrofit neural computing toward pan evaporation analysis

Imran Khan,Hossein Moayedi,Rana Muhammad Adnan Ikram,Loke Kok Foong,Binh Nguyen Le 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.32 No.1

Indirect determination of pan evaporation (PE) has been highly regarded, due to the advantages of intelligent models employed for this objective. This work pursues improving the reliability of a popular intelligent model, namely multi-layer perceptron (MLP) through surmounting its computational knots. Available climatic data of Fresno weather station (California, USA) is used for this study. In the first step, testing several most common trainers of the MLP revealed the superiority of the Levenberg-Marquardt (LM) algorithm. It, therefore, is considered as the classical training approach. Next, the optimum configurations of two metaheuristic algorithms, namely cuttlefish optimization algorithm (CFOA) and teaching-learning-based optimization (TLBO) are incorporated to optimally train the MLP. In these two models, the LM is replaced with metaheuristic strategies. Overall, the results demonstrated the high competency of the MLP (correlations above 0.997) in the presence of all three strategies. It was also observed that the TLBO enhances the learning and prediction accuracy of the classical MLP (by nearly 7.7% and 9.2%, respectively), while the CFOA performed weaker than LM. Moreover, a comparison between the efficiency of the used metaheuristic optimizers showed that the TLBO is a more time-effective technique for predicting the PE. Hence, it can serve as a promising approach for indirect PE analysis.

Predicting the concrete compressive strength through MLP network hybridized with three evolutionary algorithms

Xin Geng,Hossein Moayedi,Feifei Pan,Loke Kok Foong 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.28 No.5

In this research, we synthesized an artificial neural network (ANN) with three metaheuristic algorithms, namely particle swarm optimization (PSO) algorithm, imperialist competition algorithm (ICA), and genetic algorithm (GA) to achieve a more accurate prediction of 28-day compressive strength of concrete. Seven input parameters (including cement, water, slag, fly ash, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA)) were considered for this work. 80% of data (82 samples) were used to feed ANN, PSO-ANN, ICA-ANN, and GA-ANN models, and their performance was evaluated using the remaining 20% (21 samples). Referring to the executed sensitivity analysis, the best complexities for the PSO and GA were indicated by the population size = 450 and for the ICA by the population size = 400. Also, to assess the accuracy of the used predictors, the accuracy criteria of root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE) were defined. Based on the results, applying the PSO, ICA, and GA algorithms led to increasing R,² in the training and testing phase. Also, the MAE and RMSE of the conventional MLP experienced significant decrease after the hybridization process. Overall, the efficiency of metaheuristic science for the mentioned objective was deduced in this research. However, the combination of ANN and ICA enjoys the highest accuracy and could be a robust alternative to destructive and time-consuming tests.

Employing TLBO and SCE for optimal prediction of the compressive strength of concrete

Yinghao Zhao,Hossein Moayedi,Mehdi Bahiraei,Loke Kok Foong 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.26 No.6

The early prediction of Compressive Strength of Concrete (CSC) is a significant task in the civil engineering construction projects. This study, therefore, is dedicated to introducing two novel hybrids of neural computing, namely Shuffled Complex Evolution (SCE) and Teaching-Learning-Based Optimization (TLBO) for predicting the CSC. The algorithms are applied to a Multi-Layer Perceptron (MLP) network to create the SCE-MLP and TLBO-MLP ensembles. The results revealed that, first, intelligent models can properly handle analyzing and generalizing the non-linear relationship between the CSC and its influential parameters. For example, the smallest and largest values of the CSC were 17.19 and 58.53 MPa, and the outputs of the MLP, SCE-MLP, and TLBO-MLP range in [17.61, 54.36], [17.69, 55.55] and [18.07, 53.83], respectively. Second, applying the SCE and TLBO optimizers resulted in increasing the correlation of the MLP products from 93.58 to 97.32 and 97.22%, respectively. The prediction error was also reduced by around 34 and 31% which indicates the high efficiency of these algorithms. Moreover, regarding the computation time needed to implement the SCE-MLP and TLBO-MLP models, the SCE is a considerably more time-efficient optimizer. Nevertheless, both suggested models can be promising substitutes for laboratory and destructive CSC evaluative models.

ANN-Incorporated satin bowerbird optimizer for predicting uniaxial compressive strength of concret

Dizi Wu,Shuhua Liu,Hossein Moayedi,Mehmet Akif CIFCI,Binh Nguyen Le 국제구조공학회 2022 Steel and Composite Structures, An International J Vol.45 No.2

Surmounting complexities in analyzing the mechanical parameters of concrete entails selecting an appropriate methodology. This study integrates a novel metaheuristic technique, namely satin bowerbird optimizer (SBO) with artificial neural network (ANN) for predicting uniaxial compressive strength (UCS) of concrete. For this purpose, the created hybrid is trained and tested using a relatively large dataset collected from the published literature. Three other new algorithms, namely Henry gas solubility optimization (HGSO), sunflower optimization (SFO), and vortex search algorithm (VSA) are also used as benchmarks. After attaining a proper population size for all algorithms, the Utilizing various accuracy indicators, it was shown that the proposed ANN-SBO not only can excellently analyze the UCS behavior, but also outperforms all three benchmark hybrids (i.e., ANN-HGSO, ANN-SFO, and ANN-VSA). In the prediction phase, the correlation indices of 0.87394, 0.87936, 0.95329, and 0.95663, as well as mean absolute percentage errors of 15.9719, 15.3845, 9.4970, and 8.0629%, calculated for the ANN-HGSO, ANN-SFO, ANN-VSA, and ANN-SBO, respectively, manifested the best prediction performance for the proposed model. Also, the ANN-VSA achieved reliable results as well. In short, the ANN-SBO can be used by engineers as an efficient non-destructive method for predicting the UCS of concrete.

Slope stability analysis using black widow optimization hybridized with artificial neural network

Loke Kok Foong,Huanlong Hu,Mesut Gör,Hossein Moayedi,Abdolreza Osouli 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.4

A novel metaheuristic search method, namely black widow optimization (BWO) is employed to increase the accuracy of slope stability analysis. The BWO is a recently-developed optimizer that supervises the training of an artificial neural network (ANN) for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The designed slope bears a loaded foundation in different distances from the crest. A sensitivity analysis is conducted based on the number of active individuals in the BWO algorithm, and it was shown that the best performance is acquired for the population size of 40. Evaluation of the results revealed that the capability of the ANN was significantly enhanced by applying the BWO. In this sense, the learning root mean square error fell down by 23.34%. Also, the correlation between the testing data rose from 0.9573 to 0.9737. Therefore, the postposed BWO-ANN can be promisingly used for the early prediction of FOS in real-world projects.

Preparation of Immunotoxin Herceptin-Botulinum and Killing Effects on Two Breast Cancer Cell Lines

Hajighasemlou, Saieh,Alebouyeh, Mahmoud,Rastegar, Hossein,Manzari, Mojgan Taghizadeh,Mirmoghtadaei, Milad,Moayedi, Behjat,Ahmadzadeh, Maryam,Parvizpour, Farzad,Johari, Behrooz,Naeini, Maria Moslemi,Fa Asian Pacific Journal of Cancer Prevention 2015 Asian Pacific journal of cancer prevention Vol.16 No.14

Background: Worldwide, breast cancer is the most common cancer diagnosed among women and a leading cause of cancer deaths. The age of onset in Iran has become reduced by a decade for unknown reasons. Herceptin, a humanized monoclonal antibody, is a target therapy for breast cancer cells with over expression of HER2-neu receptors, but it is an expensive drug with only 20% beneficial rate of survival. This study introduces a novel approach to enhance the efficacy of this drug through immunoconjugation of the antibody to botulinum toxin. Decreasing the cost and adverse effects of the antibody were secondary goals of this study. Materials and Methods: Botulinum toxin was conjugated with Herceptin using heterobifunctional cross linkers, succinimidyl acetylthiopropionate (SATP) and sulfo-succinimidyl-4-(N-maleimidomethyl) cyclohexane-1-carboxylate (SMCC) according to the supplier's guidelines and tested on two breast cancer cell lines: SK-BR-3 and BT-474. Toxin and Herceptin were also used separately as controls. The cytotoxicity assay was also performed using the new bioconjugate on cultured cells with Alamar blue and a fluorescence plate reader. Results: Herceptin-Toxin bioconjugation significantly improved Herceptin efficacy on both breast cancer cell lines when compared to the control group. Conclusions: Toxin-Herceptin bioconjugation can be a potential candidate with increased efficiency for treating breast cancer patients with over expression of the HER2 receptor.

Analyzing behavior of circular concrete-filled steel tube column using improved fuzzy models

Yuxin Zheng,Hongwei Jin,Congying Jian,Zohre Moradi,Mohamed Amine Khadimallah,Hossein Moayedi 국제구조공학회 2022 Steel and Composite Structures, An International J Vol.43 No.5

Axial compression capacity (Pu) is a significant yet complex parameter of concrete-filled steel tube (CFST) columns. This study offers a novel ensemble tool, adaptive neuro-fuzzy inference system (ANFIS) supervised by equilibrium optimization (EO), for accurately predicting this parameter. Moreover, grey wolf optimization (GWO) and Harris hawk optimizer (HHO) are considered as comparative supervisors. The used data is taken from earlier literature provided by finite element analysis. ANFIS is trained by several population sizes of the EO, GWO, and HHO to detect the best configurations. At a glance, the results showed the competency of such ensembles for learning and reproducing the Pu behavior. In details, respective mean absolute errors along with correlation values of 4.1809% and 0.99564, 10.5947% and 0.98006, and 4.8947% and 0.99462 obtained for the EO-ANFIS, GWO-ANFIS, and HHO-ANFIS, respectively, indicated that the proposed EO-ANFIS can analyze and predict the behavior of CFST columns with the highest accuracy. Considering both time and accuracy, the EO provides the most efficient optimization of ANFIS and can be a nice substitute for experimental approaches.