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.

Ground surface changes detection using interferometric synthetic aperture radar

Loke Kok Foong,Ali Jamali,Zongjie Lyu 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.26 No.3

Disasters, including earthquakes and landslides, have enormous economic and social losses besides their impact on environmental disruption. Iran, and particularly its Western part, is known as an earthquake susceptible area due to numerous strong ground motions. Studying ecological changes due to climate change can improve the public and expert sector's awareness and response to future disastrous events. Synthetic Aperture Radar (SAR) data and Interferometric Synthetic Aperture Radar (InSAR) technologies are appropriate tools for modeling and surface deformation modeling. This paper proposes an efficient approach to detect ground deformation changes using Sentinel-1A. The focal point of this research is to map the ground surface deformation modeling is presented using InSAR technology over Sarpol-e Zahab on 25th November 2018 as a study case. For surface deformation modeling and detection of the ground movement due to earthquake SARPROZ in MATLAB programming language is used and discussed. Results show that there is a general ground movement due to the Sarpol-e Zahab earthquake between -7 millimeter to +18 millimeter in the study area. This research verified previous researches on the advanced image analysis techniques employed for mapping ground movement, where InSAR provides a reliable tool for assisting engineers and the decision-maker in choosing proper policies in a time of disasters. Based on the result, 574 out of 682 damaged buildings and infrastructures due to the 2017 Sarpol-e Zahab earthquake have moved from -2 to +17 mm due to the 2018 earthquake with a magnitude of 6.3 Richter. Results show that mountainous areas have suffered land subsidence, where urban areas had land uplift.

Swarm-based hybridizations of neural network for predicting the concrete strength

Xinyan Ma,Loke Kok Foong,Armin Morasaei,Aria Ghabussi,Zongjie Lyu 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.26 No.2

Due to the undeniable importance of approximating the concrete compressive strength (CSC) in civil engineering, this paper focuses on presenting four novel optimizations of multi-layer perceptron (MLP) neural network, namely artificial bee colony (ABC-MLP), grasshopper optimization algorithm (GOA-MLP), shuffled frog leaping algorithm (SFLA-MLP), and salp swarm algorithm (SSA-MLP) for predicting this crucial parameter. The used dataset consists of 103 rows of information concerning seven influential parameters (cement, slag, water, fly ash, superplasticizer, fine aggregate, and coarse aggregate). In this work, the bestfitted complexity of each ensemble is determined by a population-based sensitivity analysis. The GOA distinguished its self by the least complexity (population size = 50) and emerged as the second time-effective optimizer. Referring to the prediction results, all tested algorithms are able to construct reliable networks. However, the SSA (Correlation = 0.9652 and Error = 1.3939) and GOA (Correlation = 0.9629 and Error = 1.3922) performed more accurately than ABC (Correlation = 0.7060 and Error = 4.0161) and SFLA (Correlation = 0.8890 and Error = 2.5480). Therefore, the SSA-MLP and GOA-MLP can be promising alternatives to laboratorial and traditional CSC evaluative methods.

Predicting the splitting tensile strength of concrete using an equilibrium optimization model

Yinghao Zhao,Xiaolin Zhong,Loke Kok Foong 국제구조공학회 2021 Steel and Composite Structures, An International J Vol.39 No.1

Splitting tensile strength (STS) is an important mechanical parameter of concrete. This study offers novel methodologies for the early prediction of this parameter. Artificial neural network (ANN), which is a leading predictive method, is synthesized with two metaheuristic algorithms, namely atom search optimization (ASO) and equilibrium optimizer (EO) to achieve an optimal tuning of the weights and biases. The models are applied to data collected from the published literature. The sensitivity of the ASO and EO to the population size is first investigated, and then, proper configurations of the ASO-NN and EO-NN are compared to the conventional ANN. Evaluating the prediction results revealed the excellent efficiency of EO in optimizing the ANN. Accuracy improvements attained by this algorithm were 13.26 and 11.41% in terms of root mean square error and mean absolute error, respectively. Moreover, it raised the correlation from 0.89958 to 0.92722. This is while the results of the conventional ANN were slightly better than ASO-NN. The EO was also a faster optimizer than ASO. Based on these findings, the combination of the ANN and EO can be an efficient non-destructive tool for predicting the STS.

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.

Hybridized dragonfly, whale and ant lion algorithms in enlarged pile’s behavior

Xinyu Ye,Zongjie Lyu,Loke Kok Foong 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.25 No.6

The present study intends to find a proper solution for the estimation of the physical behaviors of enlarged piles through a combination of small-scale laboratory tests and a hybrid computational predictive intelligence process. In the first step, experimental program is completed considering various critical influential factors. The results of the best multilayer perceptron (MLP)-based predictive network was implemented through three mathematical-based solutions of dragonfly algorithm (DA), whale optimization algorithm (WOA), and ant lion optimization (ALO). Three proposed models, after convergence analysis, suggested excellent performance. These analyses varied based on neurons number (e.g., in the basis MLP hidden layer) and of course, the level of its complexity. The training R² results of the best hybrid structure of DA-MLP, WOA-MLP, and ALO-MLP were 0.996, 0.996, and 0.998 where the testing R² was 0.995, 0.985, and 0.998, respectively. Similarly, the training RMSE of 0.046, 0.051, and 0.034 were obtained for the training and testing datasets of DA-MLP, WOA-MLP, and ALO-MLP techniques, while the testing RMSE of 0.088, 0.053, and 0.053, respectively. This obtained result demonstrates the excellent prediction from the optimized structure of the proposed models if only population sensitivity analysis performs. Indeed, the ALO-MLP was slightly better than WOA-MLP and DA-MLP methods.

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.

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.

Efficient metaheuristic-retrofitted techniques for concrete slump simulation

Yinghao Zhao,Chengzong Bai,Chengyong Xu,Loke Kok Foong 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.27 No.5

Due to the benefits of the early prediction of concrete slump, introducing an efficient model for this purpose is of great importance. Considering this motivation, four strong metaheuristic algorithms, namely electromagnetic field optimization (EFO), water cycle algorithm (WCA), teaching-learning-based optimization (TLBO), and multi-tracker optimization algorithm (MTOA) are used to supervise a neural predictive system in analyzing the slump pattern. This supervision protects the network against computational issues like pre-mature convergence. The overall results (e.g., Pearson correlation indicator larger than 0.839 and 0.807 for the training and testing data, respectively) revealed the competency of the proposed models. However, investigating the rankings of the models pointed out the superiority of the WCA (MAE_train = 3.3080 vs. 3.7821, 3.5782, and 3.6851; and MAE_test = 3.8443 vs. 4.0326, 4.1417, and 4.0871 obtained for the EFO, TLBO, and MTOA, respectively). Moreover, the high efficiency of the EFO in terms of model complexity and convergence rate, as well as the adequate accuracy of prediction, demonstrated the suitability of the corresponding ensemble. Therefore, the neural systems trained by these two algorithms (i.e., the WCA and EFO) are efficient slump evaluative models and can give an optimal design of the concrete mixture for any desirable slump.