Modelling the Pull-out Capacity of Ground Anchors Using Multi-objective Feature Selection

Mohanty, Ranajeet; Suman, Shakti; Das, Sarat Kumar

doi:10.1007/s13369-016-2361-6

Cited by 13 publications

(4 citation statements)

References 26 publications

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“…Previous studies illustrate that the MGGP technique solves the problems more precisely and efficiently than the traditional GP (Kurugodu et al, 2018;Soleimani et al, 2018). Besides, the MGGP is a powerful tool to estimate or predict geotechnical parameters based on the provided measured data (Gandomi and Alavi, 2012a;Mohanty et al, 2017;Soleimani et al, 2018;Suman, Khan, et al, 2016;Suman, Mahamaya, et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Prediction Models For UCS Of Cement/Lime Stabilized QLD Soil

Pham¹

2022

AGJ

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The study aims to develop proposed predictive formulas for determining the unconfined compression strength (UCS) of cement/lime stabilized Queensland soil based on Multi-Gene Genetic Programming (MGGP) and Artificial Neural Network (ANN). The models evaluate the effect of three independent variables, including the binder type (cement and lime), the binder content, and the curing time, on the UCS of the stabilized soil. The results show that the selected optimal MGGP and ANN models can predict the target values with high correlation coefficients (R-value approximately of 0.992 and 0.998, respectively), and low errors (e.g., RMSE and MAE). The sensitivity analysis of the MGGP and ANN models provide the same results, in which the curing time has the greatest influence on the UCS value, followed by the binder content and binder type. The performances of the MGGP and ANN models are compared based on statistical parameters, several external criteria, and distribution properties. The study finds that both models show their generalization capabilities with robust, powerful, and accurate prediction ability; however, the ANN model slightly outperforms the MGGP model. The proposed predictive equations formulated from the selected optimal MGGP and ANN models could help engineers and consultants to choose the suitable binder and the reasonable amount of binder in the pre-planning and pre-design period.

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Section: Introductionmentioning

confidence: 99%

Intelligent Prediction Models For UCS Of Cement/Lime Stabilized QLD Soil

Pham¹

2022

AGJ

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“…Mohanty et al. ( 2017 ) brought an effective prediction model for determining the pull-out capacity of small ground anchors where NN is a learning algorithm and NSGA-II performs feature subset selection.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

“…( 2017 ) ENORA Online Sales Prediction Fast Medium High Mohanty et al. ( 2017 ) NSGA-II Pull-Out Capacity Prediction Medium Low Low Mistry et al. ( 2016 ) mGA & PSO Facial Emotion Recognition Fast Medium Medium Hassan and Mohammed ( 2020 ) Binary CSO Facial Emotion Recognition Medium Medium Low Mlakar et al.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

A survey on binary metaheuristic algorithms and their engineering applications

Pan

Snåšel

et al. 2022

Artif Intell Rev

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This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.

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“…The MGGP technique could solve the problems more efficiently than the GP [9,10]. In addition, the MGGP has been applied to estimate geotechnical parameters [9,[11][12][13][14][15]. 1289 (2023) 012102 IOP Publishing doi:10.1088/1757-899X/1289/1/012102 2 Artificial Neural Network (ANN) is another machine learning technique.…”

Section: Introductionmentioning

confidence: 99%

Estimating Unconfined Compression Strength of Fly Ash and Cement Stabilised Clayey Soil using Artificial intelligence Techniques

Pham,

Oh,

Ong

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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The study aims to develop proposed predictive formulas for determining the unconfined compression strength (UCS) of fly ash and cement stabilised clayey soil based on Multi-Gene Genetic Programming (MGGP) and Artificial Neural Network (ANN) techniques. Thirteen parameters, including the soil characteristics, the binder types, the binder contents, the curing period, the mixing method, and the fly ash characteristics, such as calcium oxide (CaO) content, CaO/SiO2 ratio, loss of ignition, were considered as the independent variables in the model. The results show that the selected optimal ANN and MGGP models can predict the target values with high correlation coefficients (R-value approximately 0.994 and 0.973, respectively), and low errors. The performances of the MGGP and ANN models were compared based on statistical parameters and several external criteria. The study finds that both models show their generalisation capabilities with robust, powerful, and accurate prediction ability; however, the ANN model slightly outperforms the MGGP model. The proposed predictive equations formulated from the selected optimal MGGP and ANN models could help engineers and consultants to choose the suitable binder and a reasonable amount of fly ash in the pre-planning and pre-design period.

show abstract

Modelling the Pull-out Capacity of Ground Anchors Using Multi-objective Feature Selection

Cited by 13 publications

References 26 publications

Intelligent Prediction Models For UCS Of Cement/Lime Stabilized QLD Soil

Intelligent Prediction Models For UCS Of Cement/Lime Stabilized QLD Soil

A survey on binary metaheuristic algorithms and their engineering applications

Estimating Unconfined Compression Strength of Fly Ash and Cement Stabilised Clayey Soil using Artificial intelligence Techniques

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