Guest Editors
Dr. Danial Jahed Armaghani, University of Technology Sydney, Australia; South Ural State University, Russia
Dr. Ahmed Salih Mohammed, University of Sulaimani, Iraq
Prof. Ramesh Murlidhar Bhatawdekar, Indian Institute of Technology, India; Universiti Teknologi Malaysia, Malaysia
Mr. Pouyan Fakharian, Semnan University, Iran
Dr. Ashutosh Kainthola, Banaras Hindu University, India
Mr. Wael Imad Mahmood, Komar University of Science and Technology, Iraq
Summary
In the last two decades, the topic of computational intelligence (CI) has undergone several definitions. Adaptation and self-organization algorithms and implementations that permit or facilitate appropriate behaviours (intelligent behaviour) in complex and dynamic settings are included in the notion of CI. One or more properties of reason, such as generalisation, discovery, association, and abstraction, are said to be present in this computer paradigm, which demonstrates a capacity to adapt to new conditions and learn from them. Many of the issues we face today in the area of engineering are becoming more complicated because of the prevalence of amorphous structures and behaviours, as well as large-scale, low dependability, and a scarcity of shared or comprehensive information. This intricacy necessitated that the scope of CI is widened to highlight adaptability.
In order to operate a system similar to human thinking, CI relies on three primary components: artificial neural networks, fuzzy logic, and evolutionary computation, both of which employ machine learning theories to cope with uncertainty. Hybrid CI models have shown a greater performance and application level in numerous fields of engineering than conventional CI models, which had serious limitations such time-consuming human participation and a lack of resilience. Metaheuristic algorithms may be utilised to improve base model hyper-parameters (CI models), adding extra value to frequently used base intelligence approaches.
This Special Issue focuses on the creation of unique hybrid intelligence strategies for handling regression, classification, and time series challenges. We invite scholars to submit original research papers that will help to promote ongoing research on the use of emerging CI and hybrid CI systems to assess and solve complex engineering challenges. In addition, state-of-the-art research reports, reviews, and critical evaluations of CI and hybrid CI systems are most welcome.
Keywords
Fuzzy and neuro-fuzzy Systems
Support vector machines-based systems
Genetic algorithm and genetic programming
Deep learning-based techniques
Time series systems
Hybrid artificial neural network systems
Evolutionary algorithms
Theory-guided CI systems
Metaheuristic and optimization algorithms
Published Papers
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Open Access
ARTICLE
Improved Prediction of Slope Stability under Static and Dynamic Conditions Using Tree-Based Models
Feezan Ahmad, Xiaowei Tang, Jilei Hu, Mahmood Ahmad, Behrouz Gordan
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.025993
（This article belongs to this Special Issue:
Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)
Abstract Slope stability prediction plays a significant role in landslide disaster prevention and mitigation. This paper’s
reduced error pruning (REP) tree and random tree (RT) models are developed for slope stability evaluation and
meeting the high precision and rapidity requirements in slope engineering. The data set of this study includes
five parameters, namely slope height, slope angle, cohesion, internal friction angle, and peak ground acceleration.
The available data is split into two categories: training (75%) and test (25%) sets. The output of the RT and REP
tree models is evaluated using performance measures including accuracy (
Acc), Matthews correlation coefficient
(
Mcc), precision…
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Open Access
ARTICLE
Novel Hybrid XGBoost Model to Forecast Soil Shear Strength Based on Some Soil Index Tests
Ehsan Momeni, Biao He, Yasin Abdi, Danial Jahed Armaghani
CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2527-2550, 2023, DOI:10.32604/cmes.2023.026531
（This article belongs to this Special Issue:
Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)
Abstract When building geotechnical constructions like retaining walls and dams is of interest, one of the most important
factors to consider is the soil’s shear strength parameters. This study makes an effort to propose a novel predictive
model of shear strength. The study implements an extreme gradient boosting (XGBoost) technique coupled with
a powerful optimization algorithm, the salp swarm algorithm (SSA), to predict the shear strength of various soils.
To do this, a database consisting of 152 sets of data is prepared where the shear strength (τ) of the soil is considered
as the model output and some soil index tests…
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Open Access
ARTICLE
Predicting the Thickness of an Excavation Damaged Zone around the Roadway Using the DA-RF Hybrid Model
Yuxin Chen, Weixun Yong, Chuanqi Li, Jian Zhou
CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2507-2526, 2023, DOI:10.32604/cmes.2023.025714
（This article belongs to this Special Issue:
Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)
Abstract After the excavation of the roadway, the original stress balance is destroyed, resulting in the redistribution of stress
and the formation of an excavation damaged zone (EDZ) around the roadway. The thickness of EDZ is the key
basis for roadway stability discrimination and support structure design, and it is of great engineering significance
to accurately predict the thickness of EDZ. Considering the advantages of machine learning (ML) in dealing with
high-dimensional, nonlinear problems, a hybrid prediction model based on the random forest (RF) algorithm is
developed in this paper. The model used the dragonfly algorithm (DA) to optimize two hyperparameters…
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Open Access
ARTICLE
An Improved Bald Eagle Search Algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization
Wenchuan Wang, Weican Tian, Kwok-wing Chau, Yiming Xue, Lei Xu, Hongfei Zang
CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1603-1642, 2023, DOI:10.32604/cmes.2023.026231
（This article belongs to this Special Issue:
Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)
Abstract The Bald Eagle Search algorithm (BES) is an emerging meta-heuristic algorithm. The algorithm simulates the
hunting behavior of eagles, and obtains an optimal solution through three stages, namely selection stage, search
stage and swooping stage. However, BES tends to drop-in local optimization and the maximum value of search
space needs to be improved. To fill this research gap, we propose an improved bald eagle algorithm (CABES) that
integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.
Firstly, CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage, to select…
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Open Access
ARTICLE
Language Education Optimization: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems
Pavel Trojovský, Mohammad Dehghani, Eva Trojovská, Eva Milkova
CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1527-1573, 2023, DOI:10.32604/cmes.2023.025908
（This article belongs to this Special Issue:
Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)
Abstract In this paper, based on the concept of the NFL theorem, that there is no unique algorithm that has the best
performance for all optimization problems, a new human-based metaheuristic algorithm called Language Education Optimization (LEO) is introduced, which is used to solve optimization problems. LEO is inspired by the
foreign language education process in which a language teacher trains the students of language schools in the
desired language skills and rules. LEO is mathematically modeled in three phases: (i) students selecting their
teacher, (ii) students learning from each other, and (iii) individual practice, considering exploration in local search
and…
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Graphic Abstract
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Open Access
ARTICLE
Technique for Multi-Pass Turning Optimization Based on Gaussian Quantum-Behaved Bat Algorithm
Shutong Xie, Zongbao He, Xingwang Huang
CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1575-1602, 2023, DOI:10.32604/cmes.2023.025812
（This article belongs to this Special Issue:
Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)
Abstract The multi-pass turning operation is one of the most commonly used machining methods in manufacturing field. The main objective of this operation is to minimize the unit production cost. This paper proposes a Gaussian quantum-behaved bat algorithm (GQBA) to solve the problem of multi-pass turning operation. The proposed algorithm mainly includes the following two improvements. The first improvement is to incorporate the current optimal positions of quantum bats and the global best position into the stochastic attractor to facilitate population diversification. The second improvement is to use a Gaussian distribution instead of the uniform distribution to update the positions of…
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Open Access
ARTICLE
Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network: A Case Study of Vietnam
Huong Thi Thanh Ngo, Nguyen Duc Dam, Quynh-Anh Thi Bui, Nadhir Al-Ansari, Romulus Costache, Hang Ha, Quynh Duy Bui, Sy Hung Mai, Indra Prakash, Binh Thai Pham
CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2219-2241, 2023, DOI:10.32604/cmes.2023.022566
（This article belongs to this Special Issue:
Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)
Abstract Flash floods are one of the most dangerous natural disasters, especially in hilly terrain, causing loss of life, property, and infrastructures and sudden disruption of traffic. These types of floods are mostly associated with landslides and erosion of roads within a short time. Most of Vietnam is hilly and mountainous; thus, the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management. In this study, three Machine Learning (ML) methods namely Deep Learning Neural Network (DL), Correlation-based Feature Weighted Naive Bayes (CFWNB), and Adaboost (AB-CFWNB) were…
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Open Access
ARTICLE
Seismic Liquefaction Resistance Based on Strain Energy Concept Considering Fine Content Value Effect and Performance Parametric Sensitivity Analysis
Nima Pirhadi, Xusheng Wan, Jianguo Lu, Jilei Hu, Mahmood Ahmad, Farzaneh Tahmoorian
CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 733-754, 2023, DOI:10.32604/cmes.2022.022207
（This article belongs to this Special Issue:
Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)
Abstract Liquefaction is one of the most destructive phenomena caused by earthquakes, which has been studied in the issues of potential, triggering and hazard analysis. The strain energy approach is a common method to investigate liquefaction potential. In this study, two Artificial Neural Network (ANN) models were developed to estimate the liquefaction resistance of sandy soil based on the capacity strain energy concept (
W) by using laboratory test data. A large database was collected from the literature. One group of the dataset was utilized for validating the process in order to prevent overtraining the presented model. To investigate the complex influence…
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Open Access
ARTICLE
Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models
Quang-Hieu Tran, Hoang Nguyen, Xuan-Nam Bui
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 2227-2246, 2023, DOI:10.32604/cmes.2022.021893
（This article belongs to this Special Issue:
Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications)
Abstract This study considered and predicted blast-induced ground vibration (PPV) in open-pit mines using bagging and
sibling techniques under the rigorous combination of machine learning algorithms. Accordingly, four machine
learning algorithms, including support vector regression (SVR), extra trees (ExTree), K-nearest neighbors (KNN),
and decision tree regression (DTR), were used as the base models for the purposes of combination and PPV initial
prediction. The bagging regressor (BA) was then applied to combine these base models with the efforts of variance
reduction, overfitting elimination, and generating more robust predictive models, abbreviated as BA-ExTree, BAKNN, BA-SVR, and BA-DTR. It is emphasized that the ExTree…
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