A generalized artificial intelligence model for estimating the friction angle of clays in evaluating slope stability using a deep neural network and Harris Hawks optimization algorithm

Hong, Zhang; Nguyen, Hoang; Bui, Xuan‐Nam; Pradhan, Biswajeet; Costache, Romulus; Aryal, Jagannath

doi:10.1007/s00366-020-01272-9

Cited by 35 publications

(12 citation statements)

References 76 publications

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“…It should also be emphasized that the HHO algorithm has already been implemented to optimize ANN and CNN hyperparameters in previous research [ 7 , 47 ]. However, since every problem is specific, and by taking the NFL into account, the HHOs potential to evolve a CNN’s structure to classify MRI brain tumor images has not been established.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

“…The main focus of the research proposed in this manuscript is the development of an enhanced Harris Hawks optimization (HHO) algorithm that addresses the observed flaws of its basic version. The HHO is a recently proposed, yet well-known swarm intelligence metaheuristics [ 6 ] that showed great potential in tackling many real-world challenges [ 7 , 8 ]. In this study, the authors tried to further investigate and expand the HHO’s potential by incorporating a chaotic mechanism and a novel replacement strategy that enhance both the exploitation and exploration of the basic algorithm, with only a small additional overhead in terms of computational complexity and new control parameters.…”

Section: Introductionmentioning

confidence: 99%

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Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

Basha

Bačanin

Vukobrat

et al. 2021

Sensors

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The research presented in this manuscript proposes a novel Harris Hawks optimization algorithm with practical application for evolving convolutional neural network architecture to classify various grades of brain tumor using magnetic resonance imaging. The proposed improved Harris Hawks optimization method, which belongs to the group of swarm intelligence metaheuristics, further improves the exploration and exploitation abilities of the basic algorithm by incorporating a chaotic population initialization and local search, along with a replacement strategy based on the quasi-reflection-based learning procedure. The proposed method was first evaluated on 10 recent CEC2019 benchmarks and the achieved results are compared with the ones generated by the basic algorithm, as well as with results of other state-of-the-art approaches that were tested under the same experimental conditions. In subsequent empirical research, the proposed method was adapted and applied for a practical challenge of convolutional neural network design. The evolved network structures were validated against two datasets that contain images of a healthy brain and brain with tumors. The first dataset comprises well-known IXI and cancer imagining archive images, while the second dataset consists of axial T1-weighted brain tumor images, as proposed in one recently published study in the Q1 journal. After performing data augmentation, the first dataset encompasses 8.000 healthy and 8.000 brain tumor images with grades I, II, III, and IV and the second dataset includes 4.908 images with Glioma, Meningioma, and Pituitary, with 1.636 images belonging to each tumor class. The swarm intelligence-driven convolutional neural network approach was evaluated and compared to other, similar methods and achieved a superior performance. The obtained accuracy was over 95% in all conducted experiments. Based on the established results, it is reasonable to conclude that the proposed approach could be used to develop networks that can assist doctors in diagnostics and help in the early detection of brain tumors.

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Section: Preliminaries and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

Basha

Bačanin

Vukobrat

et al. 2021

Sensors

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“…Artificial intelligence (AI) and machine learning (ML) techniques have been successfully implemented in the area of engineering and sciences [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] for the last 25 years. The same models were used to solve the slope stability problems [3,11,[33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Slope Stability Classification under Seismic Conditions Using Several Tree-Based Intelligent Techniques

et al. 2022

Self Cite

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Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety and reliability. Before the widespread usage of computers, slope stability analysis was conducted through semi analytical methods, or stability charts. Presently, engineers have developed many computational tools to perform slope stability analysis more efficiently. The challenge associated with furthering slope stability methods is to create a reliable design solution to perform reliable estimations involving a number of geometric and mechanical variables. The objective of this study was to investigate the application of tree-based models, including decision tree (DT), random forest (RF), and AdaBoost, in slope stability classification under seismic loading conditions. The input variables used in the modelling were slope height, slope inclination, cohesion, friction angle, and peak ground acceleration to classify safe slopes and unsafe slopes. The training data for the developed computational intelligence models resulted from a series of slope stability analyses performed using a standard geotechnical engineering software commonly used in geotechnical engineering practice. Upon construction of the tree-based models, the model assessment was performed through the use and calculation of accuracy, F1-score, recall, and precision indices. All tree-based models could efficiently classify the slope stability status, with the AdaBoost model providing the highest performance for the classification of slope stability for both model development and model assessment parts. The proposed AdaBoost model can be used as a screening tool during the stage of feasibility studies of related infrastructure projects, to classify slopes according to their expected status of stability under seismic loading conditions.

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“…Artificial intelligence methods have been widely used to solve various geotechnical engineering problems (Hajihassani et al 2014;Asteris and Nikoo 2019;Huang et al 2019;Armaghani et al 2016Armaghani et al , 2020aZhang et al 2021;Zeng et al 2021;Zhu et al 2021;Hasanipanah and Bakhshandeh Amnieh 2021). These methods have also been widely used for prediction of ground settlement, tunnel convergence, TBM advance rate, risk evaluation and anticipation of the ground condition in front of tunnel face (Liu et al 2019;Mikaeil et al 2019;Salimi et al 2019;Koopialipoor et al 2019;Hajihassani et al 2019a, Hasanpour et al 2019Adoko and Wu 2012;Mahdevari and Torabi 2012;Rafiai and Moosavi 2012;Hajihassani et al 2019a, b;Shi et al 2019;Moeinossadat and Ahangari 2019;Chen et al 2019;Tsuruta et al 2019;Jung et al 2019;Hayashi et al 2019;Harandizadeh et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment of Building Damage Induced by Tunnelling Through a Gene Expression Programming Model

Namazi¹,

Hajihassani

Gilani

et al. 2022

Geotech Geol Eng

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A gene expression programming (GEP) model has been used for predicting the risk of building damage due to ground movement induced by tunnelling. A critical displacement parameter, namely deflection ratio, and building major affecting factors have been identified from the previous empirical method. A general GEP model is trained and evaluated using the actual collected data from Karaj Railway in Iran, and published case histories from other projects around the world. The general model results are compared with the actual field measurements for the training of the model purpose. The trained network is used to estimate the risk of damage for a few case histories. It is shown that not only a GEP model learns a relationship between the risk of building damage with appropriate ground movement and building parameters, but it can also develop the learning to predict damage for very different geological and tunnel and building geometric conditions.

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A generalized artificial intelligence model for estimating the friction angle of clays in evaluating slope stability using a deep neural network and Harris Hawks optimization algorithm

Cited by 35 publications

References 76 publications

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

Slope Stability Classification under Seismic Conditions Using Several Tree-Based Intelligent Techniques

Risk Assessment of Building Damage Induced by Tunnelling Through a Gene Expression Programming Model

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