A TLBO-optimized artificial neural network for modeling axial capacity of pile foundations

Benali, Amel; Hachama, Mohammed; Bounif, Aouda; Nechnech, Ammar; Karray, Mourad

doi:10.1007/s00366-019-00847-5

Cited by 25 publications

(11 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The iTLBO-ANN outperformed other ANN models trained by GA and PSO by predicting building energy consumption with better accuracy and computational speed. Another TLBO-optimized ANN [58] was proposed to improve the performance in predicting the axial capacity of pile foundations. TLBO was used to train the weights of the ANN model by minimizing the mean square error produced when predicting the ultimate capacity of both driven and drilled shaft piles embedded in uncemented soils.…”

Section: Application Of Msas In Training Ann Modelsmentioning

confidence: 99%

A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks

et al. 2022

View full text Add to dashboard Cite

Artificial neural networks (ANNs) have achieved great success in performing machine learning tasks, including classification, regression, prediction, image processing, image recognition, etc., due to their outstanding training, learning, and organizing of data. Conventionally, a gradient-based algorithm known as backpropagation (BP) is frequently used to train the parameters’ value of ANN. However, this method has inherent drawbacks of slow convergence speed, sensitivity to initial solutions, and high tendency to be trapped into local optima. This paper proposes a modified particle swarm optimization (PSO) variant with two-level learning phases to train ANN for image classification. A multi-swarm approach and a social learning scheme are designed into the primary learning phase to enhance the population diversity and the solution quality, respectively. Two modified search operators with different search characteristics are incorporated into the secondary learning phase to improve the algorithm’s robustness in handling various optimization problems. Finally, the proposed algorithm is formulated as a training algorithm of ANN to optimize its neuron weights, biases, and selection of activation function based on the given classification dataset. The ANN model trained by the proposed algorithm is reported to outperform those trained by existing PSO variants in terms of classification accuracy when solving the majority of selected datasets, suggesting its potential applications in challenging real-world problems, such as intelligent condition monitoring of complex industrial systems.

show abstract

Section: Application Of Msas In Training Ann Modelsmentioning

confidence: 99%

A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Apart from the classification problems, the FNN models optimized by MSAs are also widely applied to tackle other types of real-world prediction problems with prominent performance. A TLBO-optimized FNN [31] was proposed to improve axial capacity predictions of pile foundations. The hybrid model is used to predict the ultimate capacity of both driven and drilled shaft piles embedded in un-cemented soils.…”

Section: Applications Of Msas In Training Fnn Modelsmentioning

confidence: 99%

“…In contrast to most existing MSAs, TLBO has a prominent characteristic of free from algorithm-specific-parameters. Given these appealing features, several TLBO approaches were proposed to solve various challenging optimization problems consisting of complex fitness landscapes such as those described in the constrained problems [28][29][30], training of FNN classifiers [31][32][33] and etc.…”

Section: Introductionmentioning

confidence: 99%

Optimal Training of Feedforward Neural Networks Using Teaching-Learning-Based Optimization with Modified Learning Phases

Ang

Lim

Tiang

et al. 2021

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Feedforward neural network (FNN) emerges as a promising machine learning algorithm widely used for crucial tasks such as prediction, classification, clustering, regression, and etc. due to its excellent performance in training, learning and organizing data. Conventional approaches such as gradient-based algorithm is commonly used to train FNNs, but it has drawbacks of being trapped into local optima easily, slow convergence rate and high sensitivity to initial solutions generated. Metaheuristic search algorithm such as teaching-learning-based optimization (TLBO) is envisioned as a potential solution to train FNNs due to their stochastic nature and excellent global search ability. In this paper, a new TLBO variant named as teaching-learning-based optimization with modified learning phases (TLBO-MLPs) is designed as the training algorithm of FNN, aiming to optimize its neuron weights, biases and selection of activation functions by referring to the datasets of given classification problems. The classification performances of FNN trained by TLBO-MLPs are evaluated by using the real-life classification datasets from UCI Machine Learning Repository and compared with those optimized by the other well-established TLBO variants. Rigorous simulation studies show that the FNN trained by TLBO-MLPs outperforms most of its peer algorithms significantly in solving different classification problems. Particularly, the FNN classifiers optimized by proposed TLBO-MLPs are reported to outperform its competitors up to 15.72% and 29.07% in terms of classification accuracy for both training and testing datasets, respectively.

show abstract

“…Until now, there have been many researches, especially in the field of geotechnical engineering, using this artificial neural network capability. Several related studies such as in determining foundation behavior like prediction of shallow foundation reliability [11], pile raft foundation [12], axial capacity of pile foundation [13], shaft resistance [14], elastic settlement [15], settlement shallow foundation [16] and loading-unloading pile static load [17]. Other related research such as predicting soil physical and mechanical properties like prediction of CBR value [18], uniaxial compressive strength [19], undrained shear strength [20]- [21], bearing capacity [22]- [23], unit weight [24], compression index & compression ratio [25], classification [26], compression coefficient [27], liquefaction [28], and electrical resistivity of soil [29].…”

Section: Table 1 Summarize Of Literature Reviewmentioning

confidence: 99%

Prediction of standard penetration test value on cohesive soil using artificial neural networks

Nugroho¹,

Fernando²,

Suryanita³

2021

Jurnal Informatika

View full text Add to dashboard Cite

Soil investigation is the main key in starting a construction. Standard Penetration Test (SPT) and Cone Penetration Test (CPT) are field tests that are often used in estimating soil parameters for foundation design purposes. The SPT value shows a correlation with the CPT value and other soil parameters. At present, there have been many conventional correlations examining these correlations, but the nonlinear nature of the soil due to very complex soil formations means that this correlation cannot be used in all situations. Artificial neural networks (ANN) are often used to estimate a complex and nonlinear value. In this study, that will predict the value of SPT on cohesive soil based on CPT test data and soil physical properties using artificial neural network capabilities using the Backpropagation algorithm and the activation function is bipolar sigmoid. This study uses 284 data from several places in Sumatra Island, Indonesia with data input are tip resistance (qc), shaft resistance (fs), effective overburden pressure (σ'0), percentage of liquid limit, plastic limit, sand, silt and clay. This study shows that the artificial neural network is able and effective in predicting the N-SPT value with a small error value and a strong regression equation. In this study, RMSE 3,441, MAE 2,318 and R 2 0,9451 for training data and RMSE 2,785, MAE 2,085, R 2 0,9792 for test data. This model is hereinafter referred to as NN_Nspt(C).

show abstract

A TLBO-optimized artificial neural network for modeling axial capacity of pile foundations

Cited by 25 publications

References 29 publications

A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks

A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks

Optimal Training of Feedforward Neural Networks Using Teaching-Learning-Based Optimization with Modified Learning Phases

Prediction of standard penetration test value on cohesive soil using artificial neural networks

Contact Info

Product

Resources

About