Modeling of transport demand – analyzing, calculating, and forecasting transport demand

Vuren, T Van

doi:10.1080/01441647.2019.1635226

Cited by 12 publications

(8 citation statements)

References 2 publications

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“…where cov denotes covariance, σ m denotes the standard deviation of m, and σ n denotes the standard deviation of n. |ρ| > 0.8 represents a strong relationship among m and n, values between 0.3 and 0.8 represents medium relationship, while |ρ| < 0.30 represents weak relationship [47]. According to Song et al [48], correlation is considered as "strong" if |ρ| > 0.8.…”

Section: Correlation Analysismentioning

confidence: 99%

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

et al. 2022

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The major criteria that control pile foundation design is pile bearing capacity (Pu). The load bearing capacity of piles is affected by the various characteristics of soils and the involvement of multiple parameters related to both soil and foundation. In this study, a new model for predicting bearing capacity is developed using an extreme gradient boosting (XGBoost) algorithm. A total of 200 driven piles static load test-based case histories were used to construct and verify the model. The developed XGBoost model results were compared to a number of commonly used algorithms—Adaptive Boosting (AdaBoost), Random Forest (RF), Decision Tree (DT) and Support Vector Machine (SVM) using various performance measure metrics such as coefficient of determination, mean absolute error, root mean square error, mean absolute relative error, Nash–Sutcliffe model efficiency coefficient and relative strength ratio. Furthermore, sensitivity analysis was performed to determine the effect of input parameters on Pu. The results show that all of the developed models were capable of making accurate predictions however the XGBoost algorithm surpasses others, followed by AdaBoost, RF, DT, and SVM. The sensitivity analysis result shows that the SPT blow count along the pile shaft has the greatest effect on the Pu.

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Section: Correlation Analysismentioning

confidence: 99%

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

et al. 2022

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“…where cov indicates covariance, σ u represents standard deviation of u, and σ v defines standard deviation of v. |ρ| > 0.8 signifies a strong relation among u and v, values from 0.3-0.8 represents a moderate relationship, while |ρ| < 0.30 signifies a weak relation [39]. According to Song et al [40], a relation is considered as "strong" if |ρ| > 0.8.…”

Section: Correlation Analysismentioning

confidence: 99%

Prediction of Liquefaction-Induced Lateral Displacements Using Gaussian Process Regression

et al. 2022

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During severe earthquakes, liquefaction-induced lateral displacement causes significant damage to designed structures. As a result, geotechnical specialists must accurately estimate lateral displacement in liquefaction-prone areas in order to ensure long-term development. This research proposes a Gaussian Process Regression (GPR) model based on 247 post liquefaction in-situ free face ground conditions case studies for analyzing liquefaction-induced lateral displacement. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination, coefficient of correlation, Nash–Sutcliffe efficiency coefficient, root mean square error (RMSE), and ratio of the RMSE to the standard deviation of measured data. The developed GPR model predictive ability is compared to that of three other known models—evolutionary polynomial regression, artificial neural network, and multi-layer regression available in the literature. The results show that the GPR model can accurately learn complicated nonlinear relationships between lateral displacement and its influencing factors. A sensitivity analysis is also presented in this study to assess the effects of input parameters on lateral displacement.

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“…where cov denotes covariance, σ m denotes the standard deviation of m, and σ n denotes the standard deviation of n. |ρ| > 0.8 represents a strong correlation between m and n, values between 0.3 and 0.8 represents a moderate relationship, and |ρ| > 0.30 represents a weak relationship [21].…”

Section: Dataset Collection and Correlation Analysismentioning

confidence: 99%

Extreme Gradient Boosting Algorithm for Predicting Shear Strengths of Rockfill Materials

et al. 2022

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For the safe and economical construction of embankment dams, the mechanical behaviour of the rockfill materials used in the dam’s shell must be analyzed. The characterization of rockfill materials with specified shear strength is difficult and expensive due to the presence of particles greater than 500 mm in diameter. This work investigates the feasibility of using an extreme gradient boosting (XGBoost) computing paradigm to estimate the shear strength of rockfill materials. To train and validate the proposed XGBoost model, a total of 165 databases obtained from the literature are chosen. The XGBoost model was compared against support vector machine (SVM), adaptive boosting (AdaBoost), random forest (RF), and K-nearest neighbor (KNN) models described in the literature. XGBoost beats SVM, RF, AdaBoost, and KNN models in terms of performance evaluation metrics such as coefficient of determination (R2), Nash–Sutcliffe coefficient (NSE), and error in the root mean square ratio (RMSE) to the standard deviation of the measured data (RSR). The results demonstrated that the XGBoost model has the highest prediction performance with (R2 = 0.9707, NSE = 0.9701, and RSR = 0.1729), followed by the SVM model with (R2 = 0.9655, NSE = 0.9639, and RSR = 0.1899), RF (R2 = 0.9545, NSE = 0.9542, and RSR = 0.2140), the AdaBoost model with (R2 = 0.9390, NSE = 0.9388, and RSR = 0.2474) and the KNN model with (R2 = 0.6233, NSE = 0.6180, and RSR = 0.6181). A sensitivity analysis has been conducted to ascertain the impact of each investigated input parameter. This study demonstrates that the established XGBoost model for estimating the shear strength of rockfill materials is reliable.

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Modeling of transport demand – analyzing, calculating, and forecasting transport demand

Cited by 12 publications

References 2 publications

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

Prediction of Liquefaction-Induced Lateral Displacements Using Gaussian Process Regression

Extreme Gradient Boosting Algorithm for Predicting Shear Strengths of Rockfill Materials

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