Automated design of a new integrated intelligent computing paradigm for constructing a constitutive model applicable to predicting rock fractures

Peng, Kang; Amar, Menad Nait; Ouaer, Hocine; Motahari, Mohammad Reza; Hasanipanah, Mahdi

doi:10.1007/s00366-020-01173-x

Cited by 15 publications

(4 citation statements)

References 65 publications

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“…Although there are many related fields to explore in the indoor decoration design, it is not enough; there are many problems in the field of indoor decoration that need to be resolved. For example, how to make the interior layout design faster and how to generate various styles of design are worthy of in-depth study and exploration [10].…”

Section: Related Discussionmentioning

confidence: 99%

Artificial Neural Network Model for Indoor Decoration Intelligence Calculation and Automation Design

2022

Computational Intelligence and Neuroscience

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With the continuous development of science and technology, the indoor decoration industry has gradually changed toward mechanization, specialization, and intelligent direction. Based on the predecessor research, this study proposes an artificial neural network model for indoor decoration intelligence calculation and automation design. Based on scales, walls, doors, windows, and other specific components, digital image processing technology implements automatic identification of the apartment graph and completes the preprocessing of the floor plan map. Combined with the indoor decoration data set, the automated design model based on an artificial neural network is established, and the network structure and training process of the model are analyzed. Finally, the bedroom and the living room were experimentally designed. The results showed that as the number of training increased to 30 times, the MAE and MSE assessment indicators gradually decreased, and the error of the model was very small and gradually stabilized. This shows that artificial neural network automation design is better; second, artificial neural network algorithms can generate multiple layout schemes within 1 minute. The design layout is efficient and the plan is reasonable. It meets all requests such as circulation, openness, lighting, and functionality, saving a lot of human and time and providing users with more choices.

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Section: Related Discussionmentioning

confidence: 99%

Artificial Neural Network Model for Indoor Decoration Intelligence Calculation and Automation Design

2022

Computational Intelligence and Neuroscience

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“…However, the exploration of shear behavior for rock fractures is a complicated topic, especially the potential nonlinear and size effect make the prediction of shear behavior extremely difficult (Azinfar et al, 2019;Hasanipanah et al, 2021). Thus, a variety of methodologies for rock fractures shear prediction were presented and can be roughly divided into two categories: theoretical and empirical methods (Peng et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…For instance, based on a dataset with 84 direct shear samples, two kinds of models, namely biogeography-based optimization (BBO)-based support vector regression (SVR) and teaching-learning-based optimization-based adaptive neuro-fuzzy inference system (ANFIS) were proposed by Fattahi (2018, 2020) to develop the criterions for deformation and failure of rock joint shear. Peng et al (2022) Nevertheless, it can be seen that most of the existing models are developed for a single shear test of rock fracture, resulting in only one prediction of subsequent shear can be provided. For continuous shear, they usually ignore the correlation between each step.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic-hybridized MLS-SVR: a multi-output model for predicting continuous shear displacements of rock fractures

Zhou,

Wang,

et al. 2023

Preprint

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This study presents a metaheuristic-hybridized model based on sparrow search algorithm (SSA) and multi-output least-squares support vector regression machines (SSA-MLS-SVR) to predict the continuous shear displacements of rock fractures, which is closely related to the geo-structure stability and safety. To validate the reliability and potential of the proposed model, which was respectively developed by using two subsets of MDST database including 362 results of direct shear tests for rock fractures from laboratory and field. For the unsatisfactory generalization of preliminary model, three kinds of nonlinear transformations were utilized in data preprocessing to improve the data sensitivity of SSA-MLS-SVR. The performance of modified model indicated that the SSA-MLS-SVR can effectively grasp the correlation among each post-peak shear displacements in the continuous shear process of rock fractures. In addition, at the end of this paper, some interesting findings and conjectures about the potential connections among continuous shear displacements will be summarized. This study has a great significance of exploring the correlation among the continuous shear displacements of rock fractures, and the use of the proposed data processing method is valuable for further improving the model prediction performance.

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“…It can be widely used in many complex projects where the objective function is difficult to express in the form of explicit function of decision variables. Peng [ 12 ] applied the genetic algorithm to the artificial neural network model to determine the maintenance strategy of the parts and analyzed it in combination with examples. After experimental verification, the system selected the maintenance strategy and the actual value is in full compliance with the relative error between the predicted value of the maintenance cost and the actual value.…”

Section: Introductionmentioning

confidence: 99%

Research on the Application of GIS Technology Combined with RBFNN‐GA Algorithm in the Delineation of Geological Hazard Prone Areas

Lei¹,

Yao²,

Zhang

et al. 2021

Computational Intelligence and Neuroscience

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With the rapid development of the economy and society, geological disasters such as landslides, collapses, and mudslides have shown an intensifying trend, seriously endangering the safety of people’s lives and property, and affecting the sustainable development of the economy and society. Aiming at the problems of merging different data layers and determining the weighting of data stacking in the statistical analysis model based on GIS technology in the evaluation of the risk of geological disasters, this study proposes a logistic regression model combined with the RBFNN-GA algorithm, that is, the determination of the occurrence of geological disasters. The fusion coefficient (CF value) with the RBFNN-GA algorithm model, and with the help of SPSS statistical analysis software, solves the problem of factor selection, heterogeneous data merging, and weighting of each data layer in the risk assessment. In the experimental stage, this study adopts the method of geological hazard certainty coefficients to carry out the sensitivity analysis of the geological hazards in the study area. Using homogeneous grid division, the spatial quantitative evaluation of the risk of geological disasters is realized, and at the same time, the results of the spatial quantitative evaluation of the risk of geological disasters are tested according to the latest landslide points in the region. The existing classification mainly depends on the acquisition of land use/cover information or the processing method of the acquired information, but the existing information acquisition will be limited by time, space, and spectral resolution. The results show that the number of landslide points per unit area in the extremely unstable zone and the unstable zone is 0.0395 points/km2 and 0.0251 points/km2, respectively, which is much higher than 0.0038 points/km2 in the stable zone, indicating the evaluation results and actual landslide conditions.

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Automated design of a new integrated intelligent computing paradigm for constructing a constitutive model applicable to predicting rock fractures

Cited by 15 publications

References 65 publications

Artificial Neural Network Model for Indoor Decoration Intelligence Calculation and Automation Design

Artificial Neural Network Model for Indoor Decoration Intelligence Calculation and Automation Design

Metaheuristic-hybridized MLS-SVR: a multi-output model for predicting continuous shear displacements of rock fractures

Research on the Application of GIS Technology Combined with RBFNN‐GA Algorithm in the Delineation of Geological Hazard Prone Areas

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