Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles

Armaghani, Danial Jahed; Shoib, Raja Shahrom Nizam Shah Raja; Faizi, Koohyar; Rashid, Ahmad Safuan A.

doi:10.1007/s00521-015-2072-z

Cited by 176 publications

(53 citation statements)

References 54 publications

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“…Among all advantages of PSO, a high learning speed and using less memory compared to GA should be noted. In PSO, to find the best position, a swarm of particles searches the best personal (p best ) and the best global (g best ) positions [35]. In other hands, in each system iteration, the particle moves toward finding the best positions (p best and g best ).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…Such networks are good tools for forecasting issues, however, they have several limitations such as low learning speed and falling into local minima [32][33][34]. As mentioned in literatures [20,23,[35][36][37][38][39], using efficient optimization algorithms (OAs), these limitations can be overcome. Various OAs such as particle swarm optimization (PSO), imperialism competitive algorithm (ICA) and genetic algorithm (GA) can be applied to solve continuous and discontinuous problems.…”

Section: Introductionmentioning

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: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

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

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“…However, in order to have a comprehensive and accurate model, especially in the case of CPRFs for high rise buildings with large number of piles, there will be a large number of structural elements and the calculation time makes the analysis so difficult. To cope with this difficulty, recently, some research works are focused on the application of soft computing techniques, as the speedy and powerful tool and alternative for other timeconsuming and expensive methods, to the problem of piles and piled raft foundations behaviors [14,15]. In the research of Armaghani et al (2015) [14], the ultimate bearing capacity is investigated, while Baziar et al (2014) [15] evaluated the static induced settlements.…”

Section: Introductionmentioning

confidence: 99%

“…To cope with this difficulty, recently, some research works are focused on the application of soft computing techniques, as the speedy and powerful tool and alternative for other timeconsuming and expensive methods, to the problem of piles and piled raft foundations behaviors [14,15]. In the research of Armaghani et al (2015) [14], the ultimate bearing capacity is investigated, while Baziar et al (2014) [15] evaluated the static induced settlements. Hence, the dynamically loaded induced settlements of piled rafts and the application of soft computing techniques to this problem have not been completely studied.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the application of neural network modeling and evolutionary polynomial regressions (EPRs), which are believed to be common ways to accurately and timely predict engineering complicated functions, can be examined. Different attempts to apply neural networks and EPRs to model different civil and geotechnical problems are presented in the literature [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. They are well-applied in a wide range of problems from deep soil stabilizations, concrete, and their related structures, compressive strength of soils, rocks, and stabilized samples, bearing capacity of shallow and deep foundations, lateral spreading, rock mechanics, rock engineering, and soil mechanics [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Ghorbani

Niavol

2017

Applied Computational Intelligence and Soft Computing

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Coupled Piled Raft Foundations (CPRFs) are broadly applied to share heavy loads of superstructures between piles and rafts and reduce total and differential settlements. Settlements induced by static/coupled static-dynamic loads are one of the main concerns of engineers in designing CPRFs. Evaluation of induced settlements of CPRFs has been commonly carried out using threedimensional finite element/finite difference modeling or through expensive real-scale/prototype model tests. Since the analyses, especially in the case of coupled static-dynamic loads, are not simply conducted, this paper presents two practical methods to gain the values of settlement. First, different nonlinear finite difference models under different static and coupled static-dynamic loads are developed to calculate exerted settlements. Analyses are performed with respect to different axial loads and pile's configurations, numbers, lengths, diameters, and spacing for both loading cases. Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. Also, using a sensitivity analysis based on Cosine Amplitude Method, axial load is introduced as the most influential parameter, while the ratio l/d is reported as the least effective parameter on the settlements of CPRFs.

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Settlement Prediction of Shallow Foundations on Cohesionless Soil Using Hybrid PSO-ANN Approach

Pradeep

Sankar

Chandrakaran

2021

Lecture Notes in Civil Engineering

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Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles

Cited by 176 publications

References 54 publications

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Settlement Prediction of Shallow Foundations on Cohesionless Soil Using Hybrid PSO-ANN Approach

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