Study on Prediction Models for Time-Dependent Settlement of Soft Road Foundation

2012 ◽  
Vol 204-208 ◽  
pp. 1880-1885
Author(s):  
Ai Zhao Zhou ◽  
Wei Wei Gu ◽  
Wei wang
Keyword(s):  
Prediction Models ◽  
Soft Clay ◽  
Gompertz Model ◽  
Weibull Model ◽  
Time Dependent ◽  
Observation Data ◽  
Soil Consolidation ◽  
Proposed Model ◽  
Total Settlement ◽  
Ultimate Settlement

The characteristics of soft clay roadbed settlement prediction model are studied in this paper. Firstly, based on one-dimension soil consolidation theory, the shape of time-dependent settlement process curve was analysed. Then, Mathematical analysis of traditional settlement models, including Gompertz model and Logistic model, was conducted, and the mathematical deficiency of above two traditional models were pointed out, which the settlement corresponding to inflection point has a constant ratio to the ultimate settlement. Further, Weibull model was proposed to describe the time-dependent settlement process of roadbed. This proposed model overcomes the deficiency of above two traditional models, and exponent model is one of its degraded expressions. Moreover, it can predict the total settlement processes of both the instantaneous load and the ramp load conditions. Finally, according to a group settlement observation data, the prediction results of different models are compared, and Weibull model has a good agreements.

scholarly journals Finite Element Study on Calculation of Nonlinear Soil Consolidation Using Compression and Recompression Indexes

2020 ◽  
Vol 10 (14) ◽  
pp. 4737
Author(s):  
Chao Xu ◽  
Suli Pan
Keyword(s):  
Finite Element ◽  
Case History ◽  
Time Dependent ◽  
Discrete Equation ◽  
Soil Consolidation ◽  
Coefficient Of Consolidation ◽  
Finite Element Study ◽  
Nonlinear Consolidation ◽  
Element Study

The coefficient of consolidation is traditionally considered as a constant value in soil consolidation calculations. This paper uses compression and recompression indexes to calculate the solution-dependent nonlinear compressibility, thus overconsolidation and normal consolidation are separated during the calculations. Moreover, the complex nonlinear consolidation can be described using the nonlinear compressibility and a nonlinear permeability. Then, the finite element discrete equation with consideration of the time-dependent load is derived, and a corresponding program is developed. Subsequently, a case history is conducted for verifying the proposed method and the program. The results show that the method is sufficiently accurate, indicating the necessity of considering nonlinearity for consolidation calculations. Finally, three cases are compared to reveal the importance of separating the overconsolidation and normal consolidation. Overall, this study concluded that it is inadequate to consider just one consolidation status in calculations, and that the proposed method is more reasonable for guiding construction.

scholarly journals A New Combined Adjustment Model for Geolocation Accuracy Improvement of Multiple Sources Optical and SAR Imagery

2021 ◽  
Vol 13 (3) ◽  
pp. 491
Author(s):  
Niangang Jiao ◽  
Feng Wang ◽  
Hongjian You
Keyword(s):  
Remote Sensing Data ◽  
Observation Data ◽  
Multiple Sources ◽  
Accuracy Improvement ◽  
Geometric Calibration ◽  
Proposed Model ◽  
Calibration Methods ◽  
Earth Observation Data ◽  
Sar Imagery ◽  
Geolocation Accuracy

Numerous earth observation data obtained from different platforms have been widely used in various fields, and geometric calibration is a fundamental step for these applications. Traditional calibration methods are developed based on the rational function model (RFM), which is produced by image vendors as a substitution of the rigorous sensor model (RSM). Generally, the fitting accuracy of the RFM is much higher than 1 pixel, whereas the result decreases to several pixels in mountainous areas, especially for Synthetic Aperture Radar (SAR) imagery. Therefore, this paper proposes a new combined adjustment for geolocation accuracy improvement of multiple sources satellite SAR and optical imagery. Tie points are extracted based on a robust image matching algorithm, and relationships between the parameters of the range-doppler (RD) model and the RFM are developed by transformed into the same Geodetic Coordinate systems. At the same time, a heterogeneous weight strategy is designed for better convergence. Experimental results indicate that our proposed model can achieve much higher geolocation accuracy with approximately 2.60 pixels in the X direction and 3.50 pixels in the Y direction. Compared with traditional methods developed based on RFM, our proposed model provides a new way for synergistic use of multiple sources remote sensing data.

scholarly journals Predicting Corporate Financial Sustainability Using Novel Business Analytics

2018 ◽  
Vol 11 (1) ◽  
pp. 64 ◽  
Author(s):  
Kyoung-jae Kim ◽  
Kichun Lee ◽  
Hyunchul Ahn
Keyword(s):  
Financial Distress ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Support Vector ◽  
Model Parameters ◽  
Financial Sustainability ◽  
Business Analytics ◽  
Financial Distress Prediction ◽  
Proposed Model ◽  
Distress Prediction

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

scholarly journals Crack-Considered Elastic Net Monitoring Model of Concrete Dam Displacement

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jiingmei Zhang ◽  
Chongshi Gu
Keyword(s):  
Water Pressure ◽  
Elastic Net ◽  
Arch Dam ◽  
Time Dependent ◽  
Concrete Dams ◽  
Displacement Monitoring ◽  
Concrete Dam ◽  
Monitoring Model ◽  
Proposed Model ◽  
Dependent Component

Displacement monitoring data modeling is important for evaluating the performance and health conditions of concrete dams. Conventional displacement monitoring models of concrete dams decompose the total displacement into the water pressure component, temperature component, and time-dependent component. And the crack-induced displacement is generally incorporated into the time-dependent component, thus weakening the interpretability of the model. In the practical engineering modeling, some significant explaining variables are selected while the others are eliminated by applying commonly used regression methods which occasionally show instability. This paper proposes a crack-considered elastic net monitoring model of concrete dam displacement to improve the interpretability and stability. In this model, the mathematical expression of the crack-induced displacement component is derived through the analysis of large surface crack’s effect on the concrete dam displacement to improve the interpretability of the model. Moreover, the elastic net method with better stability is used to solve the crack-considered displacement monitoring model. Sequentially, the proposed model is applied to analyze the radial displacement of a gravity arch dam. The results demonstrate that the proposed model contributes to more reasonable explaining variables’ selection and better coefficients’ estimation and also indicate better interpretability and higher predictive precision.

Probabilistic model predictive control for extended prediction horizons

2021 ◽  
Vol 69 (9) ◽  
pp. 759-770
Author(s):  
Tim Brüdigam ◽  
Johannes Teutsch ◽  
Dirk Wollherr ◽  
Marion Leibold ◽  
Martin Buss
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Prediction Models ◽  
Computational Effort ◽  
Probabilistic Constraints ◽  
Detailed Model ◽  
Control Approach ◽  
Proposed Model ◽  
Sampling Times

Abstract Detailed prediction models with robust constraints and small sampling times in Model Predictive Control yield conservative behavior and large computational effort, especially for longer prediction horizons. Here, we extend and combine previous Model Predictive Control methods that account for prediction uncertainty and reduce computational complexity. The proposed method uses robust constraints on a detailed model for short-term predictions, while probabilistic constraints are employed on a simplified model with increased sampling time for long-term predictions. The underlying methods are introduced before presenting the proposed Model Predictive Control approach. The advantages of the proposed method are shown in a mobile robot simulation example.

scholarly journals Group Scheduling Problems with Time-Dependent and Position-Dependent DeJong’s Learning Effect

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Li Sun ◽  
Lei Ning ◽  
Jia-zhen Huo
Keyword(s):  
Polynomial Time ◽  
Completion Time ◽  
Learning Effect ◽  
Time Dependent ◽  
Total Weighted Completion Time ◽  
Group Scheduling ◽  
Scheduling Problems ◽  
Scheduling Model ◽  
Proposed Model ◽  
Weighted Completion Time

In this paper, we introduce a group scheduling model with time-dependent and position-dependent DeJong’s learning effect. The objectives of scheduling problems are to minimize makespan, the total completion time, and the total weighted completion time, respectively. We show that the problems remain solvable in polynomial time under the proposed model.

Time-dependent deformations of concrete columns under different construction load histories

2019 ◽  
Vol 22 (8) ◽  
pp. 1845-1854 ◽  
Author(s):  
Dujian Zou ◽  
Chengcheng Du ◽  
Tiejun Liu ◽  
Jun Teng ◽  
Hanbin Cheng
Keyword(s):  
Prediction Models ◽  
Plain Concrete ◽  
Concrete Columns ◽  
In Situ Monitoring ◽  
Time Dependent ◽  
Construction Stage ◽  
Inappropriate Use ◽  
High Rise ◽  
Multi Stage ◽  
Axial Shortening

The adverse effects caused by differential axial shortening in high-rise buildings have received increasing attention with growing building height. However, the axial shortening analysis still lacks accuracy compared to the in-situ monitoring results of practical high-rise buildings during construction stage. It is imperative to identify the error sources, and the applicability of the current shortening prediction models should be test verified. In this study, 14 plain concrete columns were cast, and the multi-stage load method was applied to approximately simulate the loading history of axial concrete members during construction stage. The time-dependent deformations of loaded concrete specimens were measured, and a comparative analysis was conducted between test results and numerical prediction values. It is found that the measured deformations of multi-stage loading cases are all underestimated compared with predicted results, and this underestimation may be mainly caused by the inappropriate use of elastic modulus. It further indicates that the axial shortening analysis of high-rise buildings tends to underestimate the actual shortening value when the traditional calculation method is used. This study provides a reference for explaining the mismatch between the analytical results and the actual shortening values.

scholarly journals Spatiotemporal Traffic Flow Prediction with KNN and LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Xianglong Luo ◽  
Danyang Li ◽  
Yu Yang ◽  
Shengrui Zhang
Keyword(s):  
Support Vector Regression ◽  
Traffic Flow ◽  
Prediction Models ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Proposed Model

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

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