Reliability analysis of settlement of pile group

Kumar, Manish; Samui, Pijush; Kumar, Deepak; Zhang, Wengang

doi:10.1007/s41062-020-00382-z

Cited by 22 publications

(5 citation statements)

References 35 publications

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“…The performance parameters may be divided into error measuring parameters (RMSE, MAE, RSR, and WMAPE) and parameters for trend measurement (R 2 , adj. R 2 , VAF, PI, WI, and NS) 67–69 Coefficient of determination (R 2 )

R^{2} goodbreak= \frac{\sum_{i = 1}^{n} {(d_{i} - d_{italicmean})}^{2} - \sum_{i = 1}^{n} {(d_{i} - y_{i})}^{2}}{\sum_{i = 1}^{n} {(d_{i} - d_{italicmean})}^{2}}

…”

Section: Performance Evaluationmentioning

confidence: 99%

A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique

Kumar

Rai

Samui

2023

Structural Concrete

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Concrete which is the most commercialized construction material and thus it plays a key role in this era of development and hence its evolution is of utmost importance and therefore the evolution of the quality of concrete to that of its highly evolved type namely, ultra‐high performance concrete (UHPC) is undeniably the boon to this sector. Though, the correlations between the technical characteristics of UHPC and the composition of its mixture are complicated, nonlinear, and complex to characterize using standard statistical techniques. This paper is intended to use both deep neural network and ensemble machine learning algorithms namely gradient boosting, extreme gradient boosting, random forest regressor, extra tree regressor, and voting regressor trained with an 810 UHPC mixture collections with 15 input variables to predict its compressive strength. After adjusting a regression model, the prediction efficiency and generalization ability of the developed models are validated using a number of performance parameters. It was established that all employed models performed better at forecasting result, the extra tree regressor was the most accurate followed by extreme gradient boosting.

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R^{2} goodbreak= \frac{\sum_{i = 1}^{n} {(d_{i} - d_{italicmean})}^{2} - \sum_{i = 1}^{n} {(d_{i} - y_{i})}^{2}}{\sum_{i = 1}^{n} {(d_{i} - d_{italicmean})}^{2}}

…”

Section: Performance Evaluationmentioning

confidence: 99%

A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique

Kumar

Rai

Samui

2023

Structural Concrete

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“…The GRNN has significant advantages in parameter optimization, kernel function selection and sample processing. Moreover, the value of the smoothing factor for a single parameter is empirical, so the GRNN algorithm is widely used in foundation pit displacement prediction, surface settlement prediction and other related tasks 15 – 20 . At the same time, scholars have targeted the optimization of intelligent algorithms applied for deformation prediction.…”

Section: Introductionmentioning

confidence: 99%

Time-series prediction of settlement deformation in shallow buried tunnels based on EMD-SSA-GRNN model

Li,

Shu,

Chang

2024

Sci Rep

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Tunnel settlement deformation monitoring is a complex task and can result in nonlinear dynamic changes. To overcome the disturbances caused by historical data and the difficulty in selecting input parameters during deformation prediction, a decomposition, reconstruction and optimization method for tunnel settlement deformation prediction is proposed. First, empirical mode decomposition (EMD) is used to decompose the in-situ monitoring data and reduce the interactions among information at different scales in sequences. Then, the monitoring data are decomposed into intrinsic mode functions (IMFs). Secondly, the smoothing factor of the generalized regression neural network (GRNN) is optimized by using the sparse search algorithm (SSA). An EMD-SSA-GRNN deformation prediction model is developed using the optimized GRNN algorithm and is used to predict the changes in the decomposed IMFs. Finally, using the measured deformation data from a shallowly buried tunnel along the Kaizhou-Yunyang Highway in Chongqing, China, the reliability and accuracy of different models are analysed. The results show that tunnel settlement deformation exhibited a trend and a slow change in the early stage, a rapid change in the middle stage and a slow change in the late stage, and the rate of change was significantly influenced by the excavation time and the upper and lower geological layers. The prediction accuracy of the EMD-SSA-GRNN model after EMD improved from 19.2 to 59.4% relative to that of the SSA-GRNN and single GRNN models. Moreover, we find that the three error evaluation indicators of the EMD-SSA-GRNN model are lower than those of the other models and that the results of the proposed model and are more strongly correlated with measured data.

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“…Contrarily, modern researchers have shifted their focus towards employing soft-computing-based modelling as a feasible alternative (Deng et al, 2005;Zhao, 2008). Machine learning techniques (MLTs), known for their capacity to derive general inference rules from data and their proficiency in non-linear modelling, provide practical tools for simulating and understanding a wide range of complicated processes, including the RA of pile foundations (Kumar et al, 2021;Samui, 2020, 2019). Previously, many MLTs have been employed in diverse technical fields to approximate the intended output(s).…”

Section: Introductionmentioning

confidence: 99%

“…Kumar and Samui (2020) utilized the methodologies of least-square support vector machine, group method of data handling, and Gaussian process regression to conduct RA of settlement of clayey soil layer for a group of piles. Kumar et al (2021) employed MLTs to conduct a RA on the settlement of a pile group. Ushakova (2023) conducted a study with the objective of developing a methodology to evaluate the reliability of a piling foundation considering uncertainties and partial causes.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic analysis of pile foundations using Monte Carlo and Subset simulations compared with FOSM-based hybrid ANN paradigm

Suman,

Burman,

Choudhary

2024

Preprint

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The presence of uncertainty and variability in soil and sub-soils is a fundamental aspect of pile design. Thus, extensive study has been conducted to accurately measure the reliability or likelihood of structural failure. This work investigates the suitability of Monte Carlo and Subset simulations for the risk assessment of piles. In addition, First-order second-moment method (FOSM)-based hybrid artificial neural network (ANN) frameworks were used. Specifically, five hybrid ANNs were constructed using swarm intelligence algorithms A comparative analysis of Monte Carlo, Subset, FOSM, and FOSM-based hybrid ANN was conducted at different co-efficient of variation levels. The effectiveness of the utilized hybrid ANNs was determined using diverse statistical indices. Based on the performance, the best-fitted hybrid ANN was selected and utilized for risk assessment of pile foundations. According to the results, the employed ANN-MPA framework exhibit the best-fitted estimation with 99.2% (R2 = 0.9920) accuracy. The probability of failure was subsequently determined using Monte Carlo, Subset simulation and FOSM-based ANN-MPA methods. According to the results, the FOSM-based ANN-MPA approach can be considered as an alternative tool for quick estimation of risk assessment of pile foundations under different coefficient of variations levels.

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Reliability analysis of settlement of pile group

Cited by 22 publications

References 35 publications

A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique

A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique

Time-series prediction of settlement deformation in shallow buried tunnels based on EMD-SSA-GRNN model

Probabilistic analysis of pile foundations using Monte Carlo and Subset simulations compared with FOSM-based hybrid ANN paradigm

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