After Construction Settlement Prediction of the High Rock Filled Embankment Based on Improved GM

2011 ◽  
pp. 121-124
Author(s):  
Chengzhong Yang ◽  
Xiaomin Tao ◽  
Guoxian He
Keyword(s):  
Settlement Prediction

scholarly journals Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods

2021 ◽  
Vol 13 (7) ◽  
pp. 3744
Author(s):  
Mingcheng Zhu ◽  
Shouqian Li ◽  
Xianglong Wei ◽  
Peng Wang
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Good Prediction ◽  
Foundation Settlement ◽  
Machine Method ◽  
Settlement Prediction ◽  
Soft Foundation ◽  
Simulation Results ◽  
Learning Machine ◽  
The Stability

Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.

The Uneven Settlement Cause and Settlement Prediction of Xi Ying Sluice

2015 ◽  
Vol 733 ◽  
pp. 116-119
Author(s):  
Qing Yuan Zhu ◽  
Li Ting Qiu ◽  
Ting Jiang
Keyword(s):  
Three Dimensional ◽  
Normal Operation ◽  
Wall Structure ◽  
Construction Period ◽  
Settlement Prediction ◽  
Concrete Pile ◽  
Operation Period ◽  
Dimensional Finite Element ◽  
Three Dimensional Finite Element ◽  
Design Construction

Xi Ying sluice built in Xishi River, Changzhou City, is a single span sluice with width of 6m. The chamber is pier wall structure of depressed reinforced concrete floor, when the chamber had a filling and discharging water during construction period, we found that the chamber appeared large uneven subsidence. According to the design, construction and other specific circumstances of Xi Ying sluice, by using three-dimensional finite element method to calculate and analyzed the settlement of the sluice, we studied on the genesis of the uneven settlement and predicted the settlement after the running. Analysis shows that the chamber of the uneven settlement is due to the jacking effect of concrete pile. The settlement has been basically completed caused by chamber weight, there will not be a substantial settlement; In the case of blocking water during operation period, chamber’s settlement increment outside the river side and inside the river side are respectively 0.3mm and 0.4mm; through processing, the settlement of chamber won’t affect the normal operation of sluice.

Neural network based stochastic design charts for settlement prediction

2005 ◽  
Vol 42 (1) ◽  
pp. 110-120 ◽  
Author(s):  
M A Shahin ◽  
M B Jaksa ◽  
H R Maier
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Shallow Foundations ◽  
Granular Soils ◽  
Traditional Methods ◽  
Settlement Prediction ◽  
Design Charts ◽  
Artificial Neural ◽  
Level Of Risk

Traditional methods of settlement prediction of shallow foundations on granular soils are far from accurate and consistent. This can be attributed to the fact that the problem of estimating the settlement of shallow foundations on granular soils is very complex and not yet entirely understood. Recently, artificial neural networks (ANNs) have been shown to outperform the most commonly used traditional methods for predicting the settlement of shallow foundations on granular soils. However, despite the relative advantage of the ANN based approach, it does not take into account the uncertainty that may affect the magnitude of the predicted settlement. Artificial neural networks, like more traditional methods of settlement prediction, are based on deterministic approaches that ignore this uncertainty and thus provide single values of settlement with no indication of the level of risk associated with these values. An alternative stochastic approach is essential to provide more rational estimation of settlement. In this paper, the likely distribution of predicted settlements, given the uncertainties associated with settlement prediction, is obtained by combining Monte Carlo simulation with a deterministic ANN model. A set of stochastic design charts, which incorporate the uncertainty associated with the ANN method, is developed. The charts are considered to be useful in the sense that they enable the designer to make informed decisions regarding the level of risk associated with predicted settlements and consequently provide a more realistic indication of what the actual settlement might be.Key words: settlement prediction, shallow foundations, neural networks, Monte Carlo, stochastic simulation.

scholarly journals Applicability of settlement prediction method to peaty ground

2016 ◽  
Vol 56 (1) ◽  
pp. 144-151 ◽  
Author(s):  
Hirochika Hayashi ◽  
Satoshi Nishimoto ◽  
Takahiro Yamanashi
Keyword(s):  
Prediction Method ◽  
Settlement Prediction

A novel soil-pile interaction model for vertical pile settlement prediction

2021 ◽  
Author(s):  
Yunpeng Zhang ◽  
Wenbing Wu ◽  
Haikuan Zhang ◽  
M. Hesham El Naggar ◽  
Kuihua Wang ◽  
...  
Keyword(s):  
Interaction Model ◽  
Settlement Prediction ◽  
Pile Interaction ◽  
Pile Settlement

Settlement Prediction of a RAP-Supported Footing in Liquefiable Soils Subjected to a Seismic Loading

IFCEE 2021 ◽  
2021 ◽  
Author(s):  
Tat Shing Thum ◽  
Alba Yerro ◽  
Russell A. Green ◽  
Evin Ye ◽  
Angela Saade ◽  
...  
Keyword(s):  
Seismic Loading ◽  
Settlement Prediction

scholarly journals Foundation Settlement Prediction Based on a Novel NGM Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Peng-Yu Chen ◽  
Hong-Ming Yu
Keyword(s):  
Prediction Accuracy ◽  
Forecasting Model ◽  
Foundation Settlement ◽  
Engineering Construction ◽  
Settlement Prediction ◽  
Exponential Law ◽  
Grey Forecasting ◽  
Index Sequence ◽  
Settlement Time ◽  
Time Sequences

Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM(1,1,k,c)model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM(1,1,k,c)model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM(1,1,k,c)model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

Sign in / Sign up

Export Citation Format

Share Document