Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN

Momeni, Ehsan; Назир, Рамли; Armaghani, Danial Jahed; Maizir, Harnedi

doi:10.1016/j.measurement.2014.08.007

Cited by 343 publications

(139 citation statements)

References 31 publications

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“…In addition, GA is considered as a problematic algorithm due to its limitations such as determining various parameters of the algorithm (population size and genetic operator rates) and creating the proper function. To determine these values, the designer should be very careful while they will affect the convergence of the algorithm and also its results [51,52]. In GA, chromosomes have a fixed length that encodes issues to linear binary strings between 0 and 1.…”

Section: Genetic Algorithmmentioning

confidence: 99%

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Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Koopialipoor

Fallah

Armaghani

et al. 2018

Engineering with Computers

155

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Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasting through applying three hybrid intelligent systems, namely imperialist competitive algorithm (ICA)-artificial neural network (ANN), genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. In fact, ICA, PSO and GA were used to adjust weights and biases of ANN model. To achieve the aim of this study, a database composed of 262 datasets with six model inputs including burden to spacing ratio, blast-hole diameter, powder factor, stemming length, the maximum charge per delay, and blast-hole depth and one output (flyrock distance) was established. Several parametric investigations were conducted to determine the most effective factors of GA, ICA and PSO algorithms. Then, at the end of modelling process of each hybrid model, eight models were constructed and their results were checked considering two performance indices, i.e., root mean square error (RMSE) and coefficient of determination (R 2 ). The obtained results showed that although all predictive models are able to approximate flyrock, PSO-ANN predictive model can perform better compared to others. Based on R 2 , values of (0.943, 0.958 and 0.930) and (0.958, 0.959 and 0.932) were found for training and testing of ICA-ANN, PSO-ANN and GA-ANN predictive models, respectively. In addition, RMSE values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of ICA-ANN, PSO-ANN and GA-ANN predictive models, respectively. These results show higher efficiency of the PSO-ANN model in predicting flyrock distance resulting from blasting. Moreover, sensitivity analysis shows that hole diameter is more effective than others.

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Section: Genetic Algorithmmentioning

confidence: 99%

“…[51,[61][62][63][64][65]). Due to the weakness of BP in finding the accurate global minimum, the ANN model may achieve undesirable results [66].…”

Section: Hybrid Algorithmsmentioning

confidence: 99%

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Koopialipoor

Fallah

Armaghani

et al. 2018

Engineering with Computers

155

View full text Add to dashboard Cite

show abstract

“…As stated by Momeni et al (2014), reproduction is a step in which the best chromosomes are selected according to their scaled values and based on the given criteria of fitness, subsequently they will be passed to the next generation. Crossover, on the other hand, generates offspring (also called new individuals) by combining certain parts of the parents.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study

Rezaei

Назир

Momeni

2016

J. Zhejiang Univ. Sci. A

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Thin-walled spread foundations are used in coastal projects where the soil strength is relatively low. Developing a predictive model of bearing capacity for this kind of foundation is of interest due to the fact that the famous bearing capacity equations are proposed for conventional footings. Many studies underlined the applicability of artificial neural networks (ANNs) in predicting the bearing capacity of foundations. However, the majority of these models are built using conventional ANNs, which suffer from slow rate of learning as well as getting trapped in local minima. Moreover, they are mainly developed for conventional footings. The prime objective of this study is to propose an improved ANN-based predictive model of bearing capacity for thin-walled shallow foundations. In this regard, a relatively large dataset comprising 145 recorded cases of related footing load tests was compiled from the literature. The dataset includes bearing capacity (Q u ), friction angle, unit weight of sand, footing width, and thin-wall length to footing width ratio (L w /B). Apart from Q u , other parameters were set as model inputs. To enhance the diversity of the data, four more related laboratory footing load tests were conducted on the Johor Bahru sand, and results were added to the dataset. Experimental findings suggest an almost 0.5 times increase in the bearing capacity in loose and dense sands when L w /B is increased from 0.5 to 1.12. Overall, findings show the feasibility of the ANN-based predictive model improved with particle swarm optimization (PSO). The correlation coefficient was 0.98 for testing data, suggesting that the model serves as a reliable tool in predicting the bearing capacity.

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“…Momeni et al have applied hybrid genetic algorithm-based ANN for the prediction of pile bearing capacity. From this study it was concluded that using the optimum Genetic Algorithm (GA) parameters, the hybrid model outperformed the conventional ANN model in the prediction process [8].…”

Section: Introductionmentioning

confidence: 97%

Hybrid Intelligent Warning System for Boiler tube Leak Trips

2017

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Abstract. Repeated boiler tube leak trips in coal fired power plants can increase operating cost significantly. An early detection and diagnosis of boiler trips is essential for continuous safe operations in the plant. In this study two artificial intelligent monitoring systems specialized in boiler tube leak trips have been proposed. The first intelligent warning system (IWS-1) represents the use of pure artificial neural network system whereas the second intelligent warning system (IWS-2) represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. The Extreme Learning Machine (ELM) methodology was also adopted in IWS-1 and compared with traditional training algorithms. Genetic algorithm (GA) was adopted in IWS-2 to optimize the ANN topology and the boiler parameters. An integrated data preparation framework was established for 3 real cases of boiler tube leak trip based on a thermal power plant in Malaysia. Both the IWSs were developed using MATLAB coding for training and validation. The hybrid IWS-2 performed better than IWS-1.The developed system was validated to be able to predict trips before the plant monitoring system. The proposed artificial intelligent system could be adopted as a reliable monitoring system of the thermal power plant boilers.

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Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN

Cited by 343 publications

References 31 publications

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study

Hybrid Intelligent Warning System for Boiler tube Leak Trips

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