Soil compaction parameters prediction using <i>GMDH</i>-type neural network and genetic algorithm

Ardakani, Alireza; Kordnaeij, Afshin

doi:10.1080/19648189.2017.1304269

Cited by 78 publications

(31 citation statements)

References 18 publications

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“…The use of emotions in machine learning is being extensively researched in recent past [24][25][26][27]. GMDH is self-organizing type neural networks (NN) which has been found to be reliable computational method to replace classical methods [28][29][30]. ANFIS has been applied in various fields in literature [31][32][33], and in the current study its developed for pile bearing capacity detection.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of Pile Foundation Using Soft Computing Techniques: A Comparative Study

et al. 2021

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Uncertainty and variability are inherent to pile design and consequently, there have been considerable researches in quantifying the reliability or probability of failure of structures. This paper aims at examining and comparing the applicability and adaptability of Minimax Probability Machine Regression (MPMR), Emotional Neural Network (ENN), Group Method of Data Handling (GMDH), and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the reliability analysis of pile embedded in cohesionless soil and proposes an AI-based prediction method for bearing capacity of pile foundation. To ascertain the homogeneity and distribution of the datasets, Mann–Whitney U (M–W) and Anderson–Darling (AD) tests are carried out, respectively. The performance of the developed soft computing models is ascertained using various statistical parameters. A comparative study is implemented among reliability indices of the proposed models by employing First Order Second Moment Method (FOSM). The results of FOSM showed that the ANFIS approach outperformed other models for reliability analysis of bearing capacity of pile and ENN is the worst performing model. The value of R2 for all the developed models is close to 1. The best RMSE value is achieved for the training phase of the ANFIS model (0 in training and 2.13 in testing) and the poorest for the ENN (2.03 in training and 31.24 in testing) model. Based on the experimental results of reliability indices, the developed ANFIS model is found to be very close to that computed from the original data.

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Section: Introductionmentioning

confidence: 99%

Reliability Analysis of Pile Foundation Using Soft Computing Techniques: A Comparative Study

et al. 2021

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“…Finally, a model which can be used to evaluate the elastic properties and local stress state of dam concrete is obtained. Based on the artificial database generated by the finite difference method (FDM), Zheng et al [23] proposed the support vector machine (SVM) and artificial neural network (ANN) models to predict the uplift displacement caused by the tunnel liqu In order to solve a large number of complex problems in engineering applications, many experts have introduced many complex and effective neural network models [24], [25]. The combination of genetic algorithm and neural network is the current research hotspot.…”

Section: Introductionmentioning

confidence: 99%

Inversion Method of In-situ Stress and Rock Damage Characteristics in Dam Site Using Neural Network and Numerical Simulation—A Case Study

Gan

et al. 2020

IEEE Access

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In mountain and ravine region of southwest China, several hydropower stations have been built in this area. It is the main national base for hydropower energy development, but under the influence of geological structure and surface erosion, the in-situ stress environment in this area is more complex. The main purpose of this paper is to establish the relationship between measured in-situ stress data and numerical calculation by means of neural network. Therefore, it can be used to analyze the distribution characteristics of large-scale in-situ stress and the failure of regional geological bodies under the action of in-situ stress in Xiluodu area. This paper analyzes the geological conditions and the measured data of in-situ stress in Xiluodu area. The results show that there is obvious surface weathering phenomenon in this area, the stress environment is complex. The depth has a positive impact on the level of in-situ stress, but the impact degree is different. The genetic algorithm-BP artificial neural networks (G-P) method is trained by the measured in-situ stress data. Based on the field measurement data, neural network algorithm and numerical simulation technology, the three-dimensional in-situ stress field distribution characteristics of the project area are carried out. The research shows that the scheme of combining the actual measurement, numerical analysis and neural network inversion is reliable; Depth is an important factor affecting the maximum horizontal stress value in Xiluodu area; Under the action of in-situ stress, the fissures of geological body are relatively developed, and three types of original water storage and flow spaces are formed before dam storage period; The flow and storage space of groundwater includes limestone fracture, basalt fracture and slope fracture aquifer; After the impoundment of the reservoir area, the reservoir water is transmitted downward through the vertical fissures, and finally the reservoir water forms a hydraulic connection with the groundwater. After impoundment, the hydrogeological conditions of the reservoir area would be changed. INDEX TERMS In-situ stress, numerical simulation, neural network, in-situ stress inversion, geological rock mass damage, hydraulic connection.

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“…New GMDH approach with online and stable learning algorithm is suggested to deal with unknown dynamics of PV, battery and other units. In many studies and applications it is shown that GMDH based FNNs are more effective than conventional FNNs in nonlinear problems with high uncertainties such as: forecasting applications [21], modeling nonlinear systems [22], soil compaction analysis [23], electrical load studies [24], feature extraction problems [25], classifier systems [26], and many others. The main advantages and contribution of current study are:…”

Section: Introductionmentioning

confidence: 99%

Voltage Regulation for Photovoltaics-Battery-Fuel Systems Using Adaptive Group Method of Data Handling Neural Networks (GMDH-NN)

et al. 2020

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Soil compaction parameters prediction using GMDH-type neural network and genetic algorithm

Cited by 78 publications

References 18 publications

Reliability Analysis of Pile Foundation Using Soft Computing Techniques: A Comparative Study

Reliability Analysis of Pile Foundation Using Soft Computing Techniques: A Comparative Study

Inversion Method of In-situ Stress and Rock Damage Characteristics in Dam Site Using Neural Network and Numerical Simulation—A Case Study

Voltage Regulation for Photovoltaics-Battery-Fuel Systems Using Adaptive Group Method of Data Handling Neural Networks (GMDH-NN)

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