Slope Stability Prediction using Artificial Neural Network (ANN)

Chakraborty, Arunav; Goswami, Diganta

doi:10.18535/ijecs/v6i6.49

Cited by 10 publications

(10 citation statements)

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“…Accordingly, this study suggests using ANN models to estimate this parameter rapidly. Indeed, previous studies have mainly focused on adopting the feed-forward back-propagation neural network architecture for slope stability estimation [32,33,34].…”

Section: Methodsmentioning

confidence: 99%

Comparative Assessment of Various Artificial Neural Network Techniques for Estimating the Safety Factor of Road Embankments

Alqawasmeh¹

2023

GEOMATE

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The slope-stability analysis is one of the most important parameters for ensuring a safe design of road embankments. Currently, various traditional approaches to computing this variable can be seen in the literature. Among them, the finite element method is considered an accurate way to define the safety factor of road embankments. Previous research has investigated the capability of artificial neural networks for rapid safety-factor estimation to overcome the long process of modeling and calculations required in the aforementioned approach. However, most of these studies have focused on a single type of neural network and did not investigate the capabilities of other approaches. Therefore, this study is intended to evaluate the performance of various artificial neural network techniques in predicting the safety factor of road embankments. Within this context, the feed-forward back-propagation, cascade forward neural networks, and general regression neural network results will be compared and benchmarked against various methods used to predict this parameter. Moreover, it is intended to report the influence of neural network architecture on the accuracy of the estimation. Generally, the study results have shown that an artificial neural network provides a rapid and accurate method for calculating road embankments' safety factors. Besides, the best neural network model achieved a coefficient of determination of about 0.91 and a root mean square error of 0.236, which proves the efficiency of this technique. Moreover, the reliability assessment by comparing the neural network models against the traditional methods has shown that they provide better agreement with the finite element technique.

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Section: Methodsmentioning

confidence: 99%

Comparative Assessment of Various Artificial Neural Network Techniques for Estimating the Safety Factor of Road Embankments

Alqawasmeh¹

2023

GEOMATE

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“…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]. ANN is also used in prediction of dynamic compaction [30] and slope stability [31].…”

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

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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).

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“…It may also be employed in selfdiagnosis and reinforcement learning approaches [10]. In addition, artificial intelligence was adopted to predict concrete compressive strength [11][12][13][14] and assess slope stability [15,16]. Furthermore, it has been applied to the damage detection of steel portal frames relying on modal vibration parameters [17], prediction of the structural response of twostory shear structures [18], and estimation of the earthquake magnitude using seismicity indicators [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Inelastic Response of Base Isolated Structures Utilizing Regression Analysis and Artificial Neural Network

Al-Rawashdeh

Yousef²,

Al-Nawaiseh

2022

Civ Eng J

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Indeed, utilizing a base isolation system in RC structures can remarkably minimize the possibility of failure, particularly in seismic-prone countries. Despite that, the design of these structures is a long procedure that consists of choosing the appropriate isolator to optimize the nonlinear behavior of the superstructure. Moreover, the numerical simulations require huge computational effort when high accuracy is required. In recent decades, scientists and engineers have applied numerous estimation approaches such as multiple linear regression and artificial neural networks to decrease the required cost and time for daily design problems. Thus, this study's main objective is to solve the difficulty of rapid response prediction by using soft-computing techniques. Additionally, it aims to study the capability of multiple linear regression and artificial neural networks in estimating the seismic performance of base-isolated RC structures under earthquakes. A nonlinear response history analysis of four different lead rubber-bearing isolated RC structures will be performed in order to determine the responses of these structures. Subsequently, the prediction models will be developed using the responses of the structures as inputs for multiple linear regression and artificial neural networks. Lastly, the reliability of both estimation approaches in terms of the response of base-isolated structures will be investigated by comparing the prediction models' capability. In general, the results of the study show that artificial neural networks provide considerably better accuracy in estimating base-isolated structures compared to multiple linear regression, and their performance results in reliable prediction. Doi: 10.28991/CEJ-2022-08-06-07 Full Text: PDF

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Slope Stability Prediction using Artificial Neural Network (ANN)

Cited by 10 publications

References 0 publications

Comparative Assessment of Various Artificial Neural Network Techniques for Estimating the Safety Factor of Road Embankments

Comparative Assessment of Various Artificial Neural Network Techniques for Estimating the Safety Factor of Road Embankments

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

Predicting the Inelastic Response of Base Isolated Structures Utilizing Regression Analysis and Artificial Neural Network

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