Evaluating the modulus of elasticity of soil using soft computing system

Sharma, Laxmi Kant; Singh, Rajesh; Umrao, Ravi Kumar; Sharma, Kunwar Mrityunjai; Singh, T. N.

doi:10.1007/s00366-016-0486-6

Cited by 73 publications

(15 citation statements)

References 36 publications

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“…The Levenberg-Marquardt (LM) training algorithm can be defined as a data driven computing method based on artificial intelligence (AI) concepts, which, more specifically, is able to correlate inversely and numerically, the nonlinear relationships between a set of individual variables (IVs) and outputs via their characteristic mathematical topology (Nguyen-Truong and Le, 2015;Ahmadi et al, 2016;Jaeel et al, 2016). The basic concept behind the LM method is to correlate the connections between IVs and model output, without assuming a prior formula defining this correlation (Sharma et al, 2017). In this study, supervised training involving feed-forward, multi-layer perceptions (MLPs) using a back-propagation learning process based on a MATLAB (R2017a) environment, was built, and used to fully capture pile load-settlement.…”

Section: The Lm Model Developmentmentioning

confidence: 99%

“…In essence, the complex non-linear patterns between the individual variables (IVs) and the model target are precisely addressed, identified and mapped with high dimensional input space (Sun et al, 2014). Furthermore, the advantages of LM modelling includes its ability to resample the complex relationship between pile load-settlement and the parameters affecting it, without the need for any assumptions (Sharma et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis of an evolutionary LM algorithm to model the load-settlement response of steel piles embedded in sandy soil

et al. 2019

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This study was implemented to examine pile load-settlement response and to develop a rapid, highly efficient predictive intelligent model, using a new computational intelligence (CI) algorithm. To achieve this aim, a series of experimental pile load tests were performed on steel, closed-ended pile models consisting of three piles with aspect ratios of 25, 17, and 12 in an attempt to make site in-situ pile-load tests unnecessary. An optimised, evolutionary, supervised Levenberg-Marquardt (LM) training algorithm was used for this process due to its remarkably robust performance. The model piles were penetrated and tested in three sand relative densities; dense, medium, and loose. Applied load (P), pile effective length (lc), pile flexural rigidity (EA), pile slenderness ratio (lc/d) and interface friction angle (δ) were identified, based on a comprehensive statistical analysis, as these parameters play a key role in governing pile settlement. To evaluate the efficiency and the generalisation ability of the proposed algorithm, graphical comparisons were made between the proposed algorithm and the experimental results with further comparisons made with conventional prediction approaches. The results revealed outstanding agreement between the targeted and predicted pile-load settlement with a coefficient of correlation of 0.985 and a Pearson's correlation coefficient, P = 2.22 x10-32 and root mean square error (RMSE) of 0.059 respectively. This, in parallel with a non-significant mean square error level (MSE) of 0.002, validates the feasibility of the proposed method and its potential in future applications.

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Section: The Lm Model Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance analysis of an evolutionary LM algorithm to model the load-settlement response of steel piles embedded in sandy soil

et al. 2019

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“…Numerous other researchers have investigated the development of nonlinear and simple mathematical models according to biological neuron [27,28]. These studies allow for the production of a big number of structures (e.g., topologies) and network learning algorithms [29][30][31][32][33][34][35]. With using a randomly selected testing database, ANN-based models run the dataset in a training network and can also analyze the predicted result (i.e., less than 30% of the whole datasets) [36][37][38].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing

et al. 2019

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In the present work, we employed artificial neural network (ANN) that is optimized with two hybrid models, namely imperialist competition algorithm (ICA) as well as particle swarm optimization (PSO) in the case of the problem of bearing capacity of shallow circular footing systems. Many types of research have shown that ANNs are valuable techniques for estimating the bearing capacity of the soils. However, most ANN training models have some drawbacks. This study aimed to focus on the application of two well-known hybrid ICA–ANN and PSO–ANN models to the estimation of bearing capacity of the circular footing lied in layered soils. In order to provide the training and testing datasets for the predictive network models, extensive finite element (FE) modelling (a database includes 2810 training datasets and 703 testing datasets) are performed on 16 soil layer sets (weaker soil rested on stronger soil and vice versa). Note that all the independent variables of ICA and PSO algorithms are optimized utilizing a trial and error method. The input includes upper layer thickness/foundation width (h/B) ratio, footing width (B), top and bottom soil layer properties (e.g., six of the most critical soil characteristics), vertical settlement of circular footing (s), where the output was taken ultimate bearing capacity of the circular footing (Fult). Based on coefficient of determination (R2) and Root Mean Square Error (RMSE), amounts of (0.979, 0.076) and (0.984, 0.066) predicted for training dataset and amounts of (0.978, 0.075) and (0.983, 0.066) indicated in the case of the testing dataset of proposed PSO–ANN and ICA–ANN models of prediction network, respectively. It demonstrates a higher reliability of the presented PSO–ANN model for predicting ultimate bearing capacity of circular footing located on double sandy layer soils.

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“…In engineering sciences, the use of ANNs (as a branch of artificial intelligence) has been highlighted by many investigators [25][26][27][28][29][30][31]. 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].…”

Section: Introductionmentioning

confidence: 99%

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Koopialipoor

Fallah

Armaghani

et al. 2018

Engineering with Computers

149

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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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Evaluating the modulus of elasticity of soil using soft computing system

Cited by 73 publications

References 36 publications

Performance analysis of an evolutionary LM algorithm to model the load-settlement response of steel piles embedded in sandy soil

Performance analysis of an evolutionary LM algorithm to model the load-settlement response of steel piles embedded in sandy soil

Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

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