Abstract
Soil liquefaction is a dangerous phenomenon for structures that lose their shear strength and soil resistance, occurring during seismic shocks such as earthquakes or sudden stress conditions. Determining the liquefaction and nonliquefaction capacity of soil is a difficult but necessary job when constructing structures in earthquake zones. Usually, the possibility of soil liquefaction is determined by laboratory tests on soil samples subjected to dynamic loads, and this is time-consuming and costly. Therefore, this study focuses on the development of a machine learning model called a Forward Neural Network (FNN) to estimate the activation of soil liquefaction under seismic condition. The database is collected from the published literature, including 270 liquefaction cases and 216 nonliquefaction case histories under different geological conditions and earthquakes used for construction and confirming the model. The model is built and optimized for hyperparameters based on a technique known as random search (RS). Then, the L2 regularization technique is used to solve the overfitting problem of the model. The analysis results are compared with a series of empirical formulas as well as some popular machine learning (ML) models. The results show that the RS-L2-FNN model successfully predicts soil liquefaction with an accuracy of 90.33% on the entire dataset and an average accuracy of 88.4% after 300 simulations which takes into account the random split of the datasets. Compared with the empirical formulas as well as other machine learning models, the RS-L2-FNN model shows superior performance and solves the overfitting problem of the model. In addition, the global sensitivity analysis technique is used to detect the most important input characteristics affecting the activation prediction of liquefied soils. The results show that the corrected SPT resistance (N1)60 is the most important input variable, affecting the determination of the liquefaction capacity of the soil. This study provides a powerful tool that allows rapid and accurate prediction of liquefaction based on several basic soil properties.
Highlights
Liquefaction is the phenomenon in which granular material changes from solid to liquid, with an increase in the water pressure in the pore [1]
An Feedforward Neural Network (FNN) model was developed to predict soil liquefaction potential. e FNN model contains many hyperparameters that are important for model training and execution. erefore, it is necessary to find out the optimal hyperparameters as well as the model architecture
A set of 5 hyperparameters including the number of hidden neurons, training algorithm, activation function, number of training epochs, and learning rate are considered to be the key hyperparameters of the FNN model. ese hyperparameters are searched based on a random search technique of 1000 times
Summary
Liquefaction is the phenomenon in which granular material changes from solid to liquid, with an increase in the water pressure in the pore [1]. Liquefaction and its control factors are important issues [3,4,5,6]. In earthquakes, when the pore water pressure reaches the total initial stress level, the increase in the pore water pressure effectively reduces the stress, the soil particles are floating in the water, and soil liquefaction will occur [7]. Liquefaction is believed to be a major cause of ground failures in earthquakes and a major cause of damage to infrastructure and civil works [8]. Ree types of damage can occur due to soil liquefaction: e first is ground spread and landslide incidents, especially problems with dam embankment [10]. Erefore, the assessment of the potential for earthquake liquefaction at a site is an important task of earthquake geotechnical engineering Manifestations of liquefaction include reduced soil stress, resulting in loss of bearing capacity [9]. ree types of damage can occur due to soil liquefaction: e first is ground spread and landslide incidents, especially problems with dam embankment [10]. e second is the occurrence of sand blows and lateral spread damage and cracks in the ground [8]. e third is the settlement of the foundation structure of the building, the structure inclination, and the crack of the road surface are serious consequences of soil liquefaction [1]. erefore, the assessment of the potential for earthquake liquefaction at a site is an important task of earthquake geotechnical engineering
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