Prediction of Probability of Liquefaction Using Soft Computing Techniques

Kumar, Divesh Ranjan; Samui, Pijush; Burman, Avijit

doi:10.1007/s40030-022-00683-9

Cited by 45 publications

(9 citation statements)

References 60 publications

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“…These studies have demonstrated promising results in terms of predictive accuracy. Moreover, the use of artificial neural network (ANN) prediction-based models, and adaptive neuro-fuzzy inference system (ANFIS) models have been implemented to forecast soil liquefaction, resulting in improved prediction accuracy 28 – 30 . Other researchers have investigated the potential of extreme learning machines (ELM), a modified version of ANN, for predicting soil liquefaction 31 .…”

Section: Introductionmentioning

confidence: 99%

“…In addition, several researchers demonstrated the effectiveness of supervised learning methods in modeling unconfined compressive strength prediction of stabilized soils, showcasing the potential of ML techniques in simulating geotechnical properties under real conditions 39 . Moreover, other studies have predicted the potential for earthquake-induced liquefaction in fine-grained soil with minimal uncertainty and human intervention, showcasing the effectiveness of machine learning methods in evaluating liquefaction prediction performance for practical earthquake engineering applications 40 , 41 . These studies collectively illustrate the wide-ranging applicability of ML methods in geotechnical engineering domain, motivating further exploration of ML-based approaches for tackling soil liquefaction prediction challenges.…”

Section: Introductionmentioning

confidence: 99%

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Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures

Hameed,

Masood,

Srivastava

et al. 2024

Sci Rep

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Liquefaction is a devastating consequence of earthquakes that occurs in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameter is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines Extreme Learning Machine (ELM) with Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with the classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m3), mean absolute percentage error (24.900%), mean absolute error (404.416 J/m3), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored for the ELM-DOA model, to assist engineers and researchers in maximizing the utilization of this predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures

Hameed,

Masood,

Srivastava

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…These studies have demonstrated promising results in terms of predictive accuracy. Moreover, the use of artificial neural network (ANN) prediction-based models, and adaptive neuro-fuzzy inference system (ANFIS) models have been implemented to forecast soil liquefaction, resulting in improved prediction accuracy [24][25][26] . Other researchers have investigated the potential of extreme learning machines (ELM), a modified version of ANN, for predicting soil liquefaction 27 .…”

Section: Introductionmentioning

confidence: 99%

Investigating a Hybrid Extreme Learning Machine Coupled with Dingo Optimization Algorithm for Liquefaction Triggering in Sand-Silt Mixtures

Hameed,

Masood,

Srivast

et al. 2024

Preprint

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Liquefaction is a devastating consequence of earthquakes that occur in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameters is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines the Extreme Learning Machine (ELM) with the Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m3), mean absolute percentage error (24.9%), mean absolute error (404.416 J/m3), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored to the ELM-DOA model, to aid engineers and researchers in effectively utilizing the predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.

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“…Tree ensemble models (such as XGboost or extreme gradient boosting [GB]) are frequently recommended for classification and regression problems utilizing tabular data. [40][41][42][43][44] However, a number of proposed deep learning models utilizing tabular data asserted to perform better than XG-Boost in various use scenarios. In their latest study, Shwartz et al 45 evaluate the performance of novel deep learning models with ensemble-based models like XG-Boost.…”

Section: Introductionmentioning

confidence: 99%

“…Choosing which models to employ is a crucial step in resolving practical data science issues. Tree ensemble models (such as XG‐boost or extreme gradient boosting [GB]) are frequently recommended for classification and regression problems utilizing tabular data 40–44 . However, a number of proposed deep learning models utilizing tabular data asserted to perform better than XG‐Boost in various use scenarios.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique

Kumar

Rai

Samui

2023

Structural Concrete

Self Cite

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Concrete which is the most commercialized construction material and thus it plays a key role in this era of development and hence its evolution is of utmost importance and therefore the evolution of the quality of concrete to that of its highly evolved type namely, ultra‐high performance concrete (UHPC) is undeniably the boon to this sector. Though, the correlations between the technical characteristics of UHPC and the composition of its mixture are complicated, nonlinear, and complex to characterize using standard statistical techniques. This paper is intended to use both deep neural network and ensemble machine learning algorithms namely gradient boosting, extreme gradient boosting, random forest regressor, extra tree regressor, and voting regressor trained with an 810 UHPC mixture collections with 15 input variables to predict its compressive strength. After adjusting a regression model, the prediction efficiency and generalization ability of the developed models are validated using a number of performance parameters. It was established that all employed models performed better at forecasting result, the extra tree regressor was the most accurate followed by extreme gradient boosting.

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Prediction of Probability of Liquefaction Using Soft Computing Techniques

Cited by 45 publications

References 60 publications

Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures

Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures

Investigating a Hybrid Extreme Learning Machine Coupled with Dingo Optimization Algorithm for Liquefaction Triggering in Sand-Silt Mixtures

A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique

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