A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets

Assessing the behavior of earth dams under dynamic loads is one of the most significant problems with the design of such large structures. The purpose of this study is to provide new models for predicting dam dispersion in real earthquake conditions. In the first phase, 103 real cases of deformation in earth dams were collected and analyzed due to earthquakes that occurred over recent years. Using nonlinear and machine learning techniques, i.e., group method of data handling (GMDH) and multivariate adaptive regression splines (MARS), two models for prediction of the slope deformation in earth dams under the various types of earthquakes were applied and developed. The main parameters used in these simulation techniques were earthquake magnitude (Mw), fundamental period ratio (Td/Tp), yield acceleration ratio (ay/amax) as inputs and value of slope deformation (Dave) as output. Finally, in order to check the accuracy of the results of the new models, a comparison was made with the previous relations and models in seismic conditions for the slope deformation in earth dams. The results showed that the MARS model, which is able to provide a mathematical equation, has a better result than the GMDH model. These new models are recommended to be used for future analyses based on their flexible capabilities.

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“…In this way, new neurons are made. Input and output data are linked and correspond with each other based on the same process [17,83].…”

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques

et al. 2020

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“…The present study used five performance indices to assess the performance of the models developed. These included coefficient of determination (R 2 ), mean absolute error (MAE), root-mean-square error (RMSE), variance accounted for (VAF), and a20-index [32,[40][41][42]44,[94][95][96][97]. These indices were widely used in previous studies for the performance assessment of ML models.…”

Section: Assessment Of the Proposed Modelsmentioning

confidence: 99%

A Combination of Feature Selection and Random Forest Techniques to Solve a Problem Related to Blast-Induced Ground Vibration

et al. 2020

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In mining and civil engineering applications, a reliable and proper analysis of ground vibration due to quarry blasting is an extremely important task. While advances in machine learning led to numerous powerful regression models, the usefulness of these models for modeling the peak particle velocity (PPV) remains largely unexplored. Using an extensive database comprising quarry site datasets enriched with vibration variables, this article compares the predictive performance of five selected machine learning classifiers, including classification and regression trees (CART), chi-squared automatic interaction detection (CHAID), random forest (RF), artificial neural network (ANN), and support vector machine (SVM) for PPV analysis. Before conducting these model developments, feature selection was applied in order to select the most important input parameters for PPV. The results of this study show that RF performed substantially better than any of the other investigated regression models, including the frequently used SVM and ANN models. The results and process analysis of this study can be utilized by other researchers/designers in similar fields.

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“…Armaghani et al [3] successfully showed that their GEP model is able to perform better than statistical model. Some other techniques e.g., particle swarm optimization (PSO)-ANN, GA-ANN, imperialism competitive algorithm (ICA)-ANN, adoptive neuro-fuzzy inference system-GMDH-PSO and genetic programming were introduced for pile bearing capacity prediction, in the studies carried out by Armaghani et al [7], Momeni et al [15], Moayedi and Armaghani [16], Harandizadeh et al [17] and Chen et al [18], respectively. As reported in the literature, piling loads, UCS of the rock, piling geometry parameters, pile length in different layers (i.e., soil and rock) and SPT-N value are model inputs of reasonable importance to predict the settlement of rock-socketed piles.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Neuro-Swarm System to Forecast the Pile Settlement

et al. 2020

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In civil engineering applications, piles (deep foundations) are pushed into the ground in order to perform as steady support of structures. As these type of foundations are able to carry a huge amount of load, they should be carefully designed in terms of their settlement. Therefore, the control and estimation of settlement is a significant issue in pilling design and construction. The objective of the present study is to introduce a modeling process of a hybrid intelligence system namely neural network optimized by particle swarm optimization (neuro-swarm) for estimation of pile settlement. To do that, properties results of several piles socketed into rock mass together with their settlements were considered as established databased to propose neuro-swarm model. Then, several sensitivity analyses were carried out to determine the most influential particle swarm optimization parameters for pile settlement prediction. Eventually, five neuro-swarm models were constructed to understand the behavior of this hybrid model on them in pile settlement prediction. As a result, according to results of five performance indices, dataset number 4 showed the highest prediction capacity among all five datasets. The coefficient of determination (R2) and system error values of (0.851 and 0.079) and (0.892 and 0.099) were obtained respectively for train and test stages of the best neuro-swarm model which reveal the capability level of this hybrid model in predicting pile settlement. The modeling process introduced in this study can be useful for the researchers who are interested to work on the same hybrid technique.

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A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets

Cited by 101 publications

References 88 publications

Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques

Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques

A Combination of Feature Selection and Random Forest Techniques to Solve a Problem Related to Blast-Induced Ground Vibration

On the Use of Neuro-Swarm System to Forecast the Pile Settlement

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