Assessment and Prediction of Liquefaction Potential Using Different Artificial Neural Network Models: A Case Study

Shahri, Abbas Abbaszadeh

doi:10.1007/s10706-016-0004-z

Cited by 41 publications

(4 citation statements)

References 26 publications

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“…Recent applications of AI in geotechnical engineering include geotextile [23,24], tunnelling [25], geothermal energy [26], unsaturated flow [27], geo-structural health monitoring [28,29], liquefaction [30], nanotechnology [31], carbon sequestration [32], and soil properties and behaviour prediction [33][34][35]. The ML techniques applied in these past investigations include artificial neural network (ANN), support vector machine (SVM), genetic algorithms (GA), fuzzy logic, image analysis, and adaptive neurofuzzy inference systems (ANFIS).…”

Section: Introductionmentioning

confidence: 99%

Shrink–swell index prediction through deep learning

Teodosio

Wasantha

Yaghoubi

et al. 2022

Neural Comput & Applic

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Growing application of artificial intelligence in geotechnical engineering has been observed; however, its ability to predict the properties and nonlinear behaviour of reactive soil is currently not well considered. Although previous studies provided linear correlations between shrink–swell index and Atterberg limits, obtained model accuracy values were found unsatisfactory results. Artificial intelligence, specifically deep learning, has the potential to give improved accuracy. This research employed deep learning to predict more accurate values of shrink–swell indices, which explored two scenarios; Scenario 1 used the features liquid limit, plastic limit, plasticity index, and linear shrinkage, whilst Scenario 2 added the input feature, fines percentage passing through a 0.075-mm sieve (%fines). Findings indicated that the implementation of deep learning neural networks resulted in increased model measurement accuracy in Scenarios 1 and 2. The values of accuracy measured in this study were suggestively higher and have wider variance than most previous studies. Global sensitivity analyses were also conducted to investigate the influence of each input feature. These sensitivity analyses resulted in a range of predicted values within the variance of data in Scenario 2, with the %fines having the highest contribution to the variance of the shrink–swell index and a relevant interaction between linear shrinkage and %fines. The proposed model Scenario 2 was around 10–65% more accurate than the preceding models considered in this study, which can then be used to expeditiously estimate more accurate values of shrink–swell indices.

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Section: Introductionmentioning

confidence: 99%

Shrink–swell index prediction through deep learning

Teodosio

Wasantha

Yaghoubi

et al. 2022

Neural Comput & Applic

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“…Samui & Sitharam (2011), A. Shahri (2016), Kumar & Rawat (2017) assessed and predicted the liquefaction potential using different artificial neural network models. Umar et al(2018) have been employed the deterministic and probabilistic study of liquefaction for many areas in Bihar.…”

Section: Introductionmentioning

confidence: 99%

A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

Umar

Kumari

Samui

et al. 2021

International Journal of Applied Metaheuristic Computing

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In any construction projects,assessment of liquefaction potential induced due to seismic excitation during earthquake is a critical concern.The objective of present model development is to classify and assess liquefaction potential of soil.This paper addresses Emotional Neural Network(ENN), Cultural Algorithm(CA) and biogeography optimized(BBO) based adaptive neuro-fuzzy inference system (ANFIS) for liquefaction study.The performance of neural emotional network and cultural algorithm has been also discussed. BBO-ANFIS combines the biogeography features to optimize the ANFIS parameters to achieve higher prediction accuracy.The model is trained with case history of liquefaction databases.Two parameters are used as input such as the cyclic stress ratio and standard penetration test (SPT) value.The performance of these models was assessed using different indexes i.e. sensitivity, specificity, FNR, FPR and accuracy rate.The performance of all models is compared.Among the models, the BBO-ANFIS model has been outperformed and can be adopted as new reliable technique for liquefaction study.

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“…Additionally, although each input variable may have a different nonlinear relationship with the bearing capacity, both studies considered the same equation type of input variables (exponential or power). In recent years, using artificial intelligence (AI) techniques to address problems in civil engineering has amplified [19][20][21]: the artificial neural network (ANN) is an AI technique that is applied to estimate ultimate bearing capacity [22,23]. ANN is known as a "black box model" because explaining the weights/parameters of a network is infeasible, and it has disadvantages, such as overfitting and vanishing gradient problems.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Ultimate Bearing Capacity of Aggregate Pier Reinforced Clay Using Multiple Regression Analysis and Deep Learning

Bong

Kim

2020

Applied Sciences

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Aggregate piers have been widely used to increase bearing pressure and reduce settlement under structural footings. The ultimate bearing capacity of aggregate pier-reinforced ground is affected by the soil strength, replacement ratio of piles, and construction conditions. Various prediction models have been proposed to predict the ultimate bearing capacity. However, existing models have shown a broad range of bias, variation, and error, and they are at times unsuitable for practical design. In this study, multiple regression analysis was performed using field loading test results to predict the ultimate bearing capacity of ground reinforced by aggregate piers, and the number and type of the most efficient input variables were evaluated to build a robust predictive model. Accordingly, a multiple regression equation for predicting the ultimate bearing capacity was proposed, and a sensitivity analysis was conducted to identify the effect of input variables. In addition, a deep neural network was applied to estimate the ultimate bearing capacity. The optimal structure was selected on the basis of cross-validation results to prevent overtraining. Prediction errors for two approaches were evaluated and then compared with those of existing models.

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Assessment and Prediction of Liquefaction Potential Using Different Artificial Neural Network Models: A Case Study

Cited by 41 publications

References 26 publications

Shrink–swell index prediction through deep learning

Shrink–swell index prediction through deep learning

A Liquefaction Study Using ENN, CA, and Biogeography Optimized-Based ANFIS Technique

Prediction of Ultimate Bearing Capacity of Aggregate Pier Reinforced Clay Using Multiple Regression Analysis and Deep Learning

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