Prediction of Settlement of Shallow Foundation on Cohesionless Soil Using Artificial Neural Network

Debnath, Sinjan; Sultana, Parbin

doi:10.1007/978-981-16-6456-4_49

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…As a result, it must therefore be as high as possible. MAE and MSE should be kept to a minimum [43]. The complete correlation between the actual and anticipated PGA is shown in Figure 6.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

Challagulla,

Suluguru,

Noroozinejad Farsangi

et al. 2023

The Journal of Engineering

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The seismic resilience of a structure has been evaluated using peak ground acceleration (PGA). Ground motion parameters such as source characteristics, local site conditions are used to forecast the PGA of the ground motion. This paper aims to develop an Artificial Neural Network (ANN) based model to predict the PGA. Here, hypocentral distance (Rhypo${R}_{hypo}$), shear wave velocity (Vs30${V}_{s30}$), and moment magnitude (Mw${M}_w$), are used as input parameters. The model uses 12,706 ground motion recordings from 283 earthquakes from the revised NGA‐West2 database supplied by Pacific Engineering Research Centre. Among the whole data, 70% of the data is set for training, 15% for validation, and 15% for testing the network. The R value derived from the testing dataset is 0.952, indicating the excellent performance of a network. An extensive parametric study is conducted with the PGA values, and the results indicate that the PGA increases with the magnitude and decreases with the hypocentral distance. The predicted PGA values from the present study are comparable with those from the existing relationships in the global database. The generated ANN model is further verified by comparing the predicted and recorded PGA values of an actual recorded event.

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“…As a result, it must therefore be as high as possible. MAE and MSE should be kept to a minimum [43]. The complete correlation between the actual and anticipated PGA is shown in Figure 6.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

Challagulla,

Suluguru,

Noroozinejad Farsangi

et al. 2023

The Journal of Engineering

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show abstract

“…The R-value of the model should be as high as possible since it gives the relative correlation and goodness of the fit between the measured and predicted values. RMSE, MSE, and MAE are errors, and they should be as low as possible [28]. A strong correlation exists between the measured and predicted values if R > 0.8 [29].…”

Section: Proposed Design Equation For 𝑻 𝒏𝒆𝒘mentioning

confidence: 99%

Effect of the Sliding of Stacked Live Loads on the Seismic Response of Structures

Challagulla¹,

Parimi²,

Thiruvikraman³

2020

EngJ

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Dynamic interaction between sliding live loads and the structure they act on is significant in the seismic analysis and design of the structure. The problem becomes more complex when the live loads are in the form of stacks. This paper presents a numerical model to simulate the dynamic interaction between a primary structure (PS) and a set of stacked bodies lying on it. Individual bodies in the stack were termed as secondary bodies (SBs) in this study. The lowest SB in the stack interacts with the structure through friction. Similar frictional forces also exist between different levels of the stack. This numerical model was verified with a Finite Element model. A parametric study was performed on the seismic response by varying the dynamic properties of the structure and SBs. The energy dissipation is found to be significant due to sliding within the stack. A novel methodology is proposed to calculate a modified structural period (𝑇 𝑛𝑒𝑤 ) of the structure to use in its design. It was found that the 𝑇 𝑛𝑒𝑤 varies significantly with the structural period, mass ratios, and coefficients of friction. Finally, design equations are proposed to calculate the 𝑇 𝑛𝑒𝑤 . Two Indian seismic hazard levels were considered for this study.

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Improving Accuracy in Shallow Foundation Settlement Prediction Using Rock Engineering System Method

Fattahi,

Ghaedi

2024

Indian Geotech J

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Prediction of Settlement of Shallow Foundation on Cohesionless Soil Using Artificial Neural Network

Cited by 4 publications

References 7 publications

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

Application of metaheuristic algorithms in prediction of earthquake peak ground acceleration

Effect of the Sliding of Stacked Live Loads on the Seismic Response of Structures

Improving Accuracy in Shallow Foundation Settlement Prediction Using Rock Engineering System Method

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