scholarly journals A Prediction Model of Structural Settlement Based on EMD-SVR-WNN

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xianglong Luo ◽  
Wenjuan Gan ◽  
Lixin Wang ◽  
Yonghong Chen ◽  
Xue Meng
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Structural Deformation ◽  
Random Fluctuation ◽  
Structural Safety ◽  
Support Vector ◽  
Foundation Pit ◽  
Deformation Prediction

Timely and accurate prediction of structural settlement is of great significance to eliminate the hidden danger of structural and prevent structural safety accidents. Since the deformation monitoring data usually is nonstationary and nonlinear, the deformation prediction is a difficult problem in the structural monitoring research. Aiming at the problems in the structural deformation prediction model and considering the internal characteristics of deformation monitoring data and the influence of different components in the data on the prediction accuracy, a combined prediction model based on the Empirical Mode Decomposition, Support Vector Regression, and Wavelet Neural Network (EMD-SVR-WNN) is proposed. EMD model is used to decompose the structure settlement monitoring data, and the settlement data can be effectively divided into relatively stable trend terms and residual components of random fluctuation by energy matrix. According to the different characteristics of random items and trend items, WNN and SVR methods are, respectively, used for prediction, and the final settlement prediction is obtained by integrating the prediction results. The measured ground settlement data of foundation pit in subway construction is used to test the performance of the model, and the test results show that the prediction accuracy of the combined prediction model proposed in this paper reaches 99.19%, which is 77.30% higher than the traditional SVR, WNN, and DBN-SVR models. The experimental results show that the proposed prediction model is an effective model of structural settlement.

scholarly journals A Deep Learning Prediction Model for Structural Deformation Based on Temporal Convolutional Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xianglong Luo ◽  
Wenjuan Gan ◽  
Lixin Wang ◽  
Yonghong Chen ◽  
Enlin Ma
Keyword(s):  
Prediction Model ◽  
Structural Engineering ◽  
Orthogonal Experiment ◽  
Deformation Monitoring ◽  
Structural Deformation ◽  
Long Term Memory ◽  
Model Parameters ◽  
Deformation Prediction ◽  
Convolutional Networks ◽  
Proposed Model

The structural engineering is subject to various subjective and objective factors, the deformation is usually inevitable, the deformation monitoring data usually are nonstationary and nonlinear, and the deformation prediction is a difficult problem in the field of structural monitoring. Aiming at the problems of the traditional structural deformation prediction methods, a structural deformation prediction model is proposed based on temporal convolutional networks (TCNs) in this study. The proposed model uses a one-dimensional dilated causal convolution to reduce the model parameters, expand the receptive field, and prevent future information leakage. By obtaining the long-term memory of time series, the internal time characteristics of structural deformation data can be effectively mined. The network hyperparameters of the TCN model are optimized by the orthogonal experiment, which determines the optimal combination of model parameters. The experimental results show that the predicted values of the proposed model are highly consistent with the actual monitored values. The average RMSE, MAPE, and MAE with the optimized model parameters reduce 44.15%, 82.03%, and 66.48%, respectively, and the average running time is reduced by 45.41% compared with the results without optimization parameters. The average RMSE, MAE, and MAPE reduce by 26.88%, 62.16%, and 40.83%, respectively, compared with WNN, DBN-SVR, GRU, and LSTM models.

Ultrasonic Transceiver-Based Structural Deformation Monitoring with Location Ability

2014 ◽  
Vol 721 ◽  
pp. 442-445
Author(s):  
Wei Zheng ◽  
Chun Xian Wu ◽  
Rong Rong Cui
Keyword(s):  
Spatial Relations ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Structural Deformation ◽  
Monitoring Method ◽  
Regional Monitoring ◽  
Space Deformation ◽  
Structural Surface

Regional coverage monitoring for structural deformation remains a challenge for current technologies. A coverage regional monitoring method based on dual ultrasonic transceivers and exhibiting deformation location ability is presented. The spatial projecting model of dual ultrasonic beams is established to determine the monitoring scope of the structural surface in space. Deformation location principles are induced by analyzing the spatial relations of the monitoring data of dual ultrasonic transceivers. Finally, an experiment is proposed to illustrate the method.

Research on Combined Prediction Model for Busy Telephone Traffic

2014 ◽  
Vol 610 ◽  
pp. 789-796
Author(s):  
Jiang Bao Li ◽  
Zhen Hong Jia ◽  
Xi Zhong Qin ◽  
Lei Sheng ◽  
Li Chen
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Low Frequency ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Frequency Component ◽  
Least Square ◽  
High Frequency Component ◽  
Support Vector ◽  
Telephone Traffic

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.

scholarly journals Deformation prediction analysis of vertical displacement of deep foundation pit based on LIBSVM

2020 ◽  
Vol 206 ◽  
pp. 01021
Author(s):  
Yueyao Zhao ◽  
Jiawei Zhang ◽  
Haojie Li
Keyword(s):  
Support Vector Machine Model ◽  
Hot Spot ◽  
Vertical Displacement ◽  
Support Vector ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Machine Model ◽  
Deformation Prediction ◽  
Deep Foundation Pit ◽  
Good Agreement

The reliable prediction of the surface vertical displacement deformation of deep foundation pits is of great significance to the excavation of large foundation pits. The support vector machine model (LIBSVM) has become a hot spot in the prediction of deep foundation pit deformation and provides a new prediction for the deformation of deep foundation pits. In this paper, taking the deep foundation pit of Daoxianghu Road Station in xx as an example, a prediction model of vertical displacement on the ground is established based on LIBSVM and analysis shows that the prediction results based on the model are in good agreement with the measured data, and the MSE reaches 0.0323. The model is effective and has an effective prospective skill.

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

2021 ◽  
pp. 0309524X2110568
Author(s):  
Lian Lian ◽  
Kan He
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Power Grid ◽  
Wavelet Denoising ◽  
Slime Mold ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Prediction

The accuracy of wind power prediction directly affects the operation cost of power grid and is the result of power grid supply and demand balance. Therefore, how to improve the prediction accuracy of wind power is very important. In order to improve the prediction accuracy of wind power, a prediction model based on wavelet denoising and improved slime mold algorithm optimized support vector machine is proposed. The wavelet denoising algorithm is used to denoise the wind power data, and then the support vector machine is used as the prediction model. Because the prediction results of support vector machine are greatly affected by model parameters, an improved slime mold optimization algorithm with random inertia weight mechanism is used to determine the best penalty factor and kernel function parameters in support vector machine model. The effectiveness of the proposed prediction model is verified by using two groups actually collected wind power data. Seven prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, the performance indicators, the Pearson’s correlation coefficient, the DM test, box-plot distribution, the results show that the proposed prediction model has high prediction accuracy.

scholarly journals Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days

Machines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 80
Author(s):  
Yalong Li ◽  
Fan Yang ◽  
Wenting Zha ◽  
Licheng Yan
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Pearson Correlation ◽  
Combination Method ◽  
Random Fluctuation ◽  
Short Term ◽  
Data Set ◽  
Model Based

With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method.

A Data-Driven Two-Stage Prediction Model for Train Primary-Delay Recovery Time

2020 ◽  
Vol 30 (07) ◽  
pp. 921-940
Author(s):  
Bowen Gao ◽  
Dongxiu Ou ◽  
Decun Dong ◽  
Yusen Wu
Keyword(s):  
Prediction Model ◽  
Knowledge Base ◽  
Recovery Time ◽  
Prediction Accuracy ◽  
Gradient Boosting ◽  
Support Vector ◽  
Model Framework ◽  
Two Stage ◽  
Delay Propagation ◽  
Proposed Model

Accurate prediction of train delay recovery is critical for railway incident management and providing passengers with accurate journey time. In this paper, a two-stage prediction model is proposed to predict the recovery time of train primary-delay based on the real records from High-Speed Railway (HSR). In Stage 1, two models are built to study the influence of feature space and model framework on the prediction accuracy of buffer time in each section or station. It is found that explicitly inputting the attribute features of stations and sections to the model, instead of implicit simulation, will improve the prediction accuracy effectively. For validation purpose, the proposed model has been compared with several alternative models, namely, Logistic Regression (LR), Artificial Neutral Network (ANN), Support Vector Machine (SVM) and Gradient Boosting Tree (GBT). The results show that its remarkable performance is better than other schemes. Specifically, when the error is extended to 3[Formula: see text]min, the proposed model can achieve up to the accuracy of 94.63%. It proves that our method has high value in practical engineering application. Considering the delay propagation of trains is a complex process, our future study will focus on building delay propagation knowledge base and dispatcher experience knowledge base.

Arch Dam Deformation Prediction Model Based on PSO-SVM

2013 ◽  
Vol 351-352 ◽  
pp. 1306-1311 ◽  
Author(s):  
Jing Yang Liu ◽  
He Zhi Liu
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Small Sample ◽  
Arch Dam ◽  
Support Vector ◽  
Model Parameters ◽  
Deformation Prediction ◽  
Dam Foundation ◽  
Model Based ◽  
Dam Deformation

Arch dam has gradually evolved as one of dam type as main large-scale hydraulic project, dam deformation prediction is an important part of dam safety monitoring, and it is difficult to forecast because of the complicated nonlinear characteristics of the monitoring data. Support Vector Machine (SVM) could solve the small sample, nonlinear high dimension problem due to the excellent generalization ability, and hence it has been widely used in the forecast of arch dam deformation. However, the forecast results considerably depend on the choice of SVM model parameters. In this paper, Particle Swarm Optimization (PSO), which has the characteristic of fast global optimization, was applied to optimize the parameters in SVM, and then the dam deformation prediction model based on PSO-SVM could be established. The model is applied to a certain arch dam foundation prediction. The accuracy of this employed approach was examined by comparing it with multiple regression method. In a word, the experimental results indicate that the proposed method based on PSO-SVM can be used in arch dam deformation prediction.

scholarly journals Safety Management System Prototype/Framework of Deep Foundation Pit Based on BIM and IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Wenhan Fan ◽  
Jianliang Zhou ◽  
Jianming Zhou ◽  
Dandan Liu ◽  
Wenjing Shen ◽  
...  
Keyword(s):  
Management System ◽  
Safety Management ◽  
Deformation Monitoring ◽  
Structural Deformation ◽  
Underground Construction ◽  
Construction Sites ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Safety Management System ◽  
Deep Foundation Pit

With the huge demand for building underground spaces, deep foundation pits are becoming more and more common in underground construction. Due to the serious effects associated with accidents that occur in deep foundation pits, it is very important for underground construction safety management to be proactive, targeted, and effective. This research develops a conceptual framework adopting BIM and IoT to aid the identification and evaluation of hazards in deep foundation pit construction sites using an automated early warning system. Based on the accident analysis, the system framework of Safety Management System of Deep Foundation Pits (SMSoDFP) is proposed; it includes a function requirement, system modules, and information needs. Further, the implementation principles are studied; they cover hazardous areas, namely, visualization, personnel position monitoring, structural deformation monitoring, and automatic warning. Finally, a case study is used to demonstrate the effectiveness and feasibility of the system proposed. This research provides suggestions for on-site management and information integration of deep foundation pits, with a view to improving the safety management efficiency of construction sites and reducing accidents.

scholarly journals A Fractal Prediction Method for Safety Monitoring Deformation of Core Rockfill Dams

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liang Pei ◽  
Jiankang Chen ◽  
Jingren Zhou ◽  
Huibao Huang ◽  
Zhengjun Zhou ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Prediction Method ◽  
Safety Monitoring ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Dimension Theory ◽  
Deformation Prediction ◽  
The Core ◽  
Dam Deformation ◽  
And Control

Deformation mechanism in the core rockfill dams with heavy load and high-stress level is difficult to predict and control, which is one of the key problems to be solved in the dam operation safety management and control. Aiming at the large error problems obtained by the parameter-based functional models (regression model, grey theory model, etc.) in the deformation prediction of the core rockfill dams, a fractal prediction method and its technical process by combining the variable dimension fractal dimension and the "metabolism" of prediction data are proposed through analyzing the fractal adaptability and deformation characteristics of original monitoring data based on the resealed-range (R/S) method and fractal dimension theory. It effectively solves the error in the process of constant dimension fractal accumulation and transformation greatly in dam deformation prediction and provides a new way for dam safety monitoring deformation prediction and early warning. The trend analysis of deformation monitoring data of the Pubugou core rockfill dam and the deformation prediction show that the fractal prediction information of dam deformation has a good corresponding relationship with its physical causes, which is in line with the actual deformation trend and operation state of the dam. Compared with the traditional stepwise regression method, the prediction results obtained by the proposed method in this paper are of high accuracy, implying that the improved fractal prediction of dam deformation is effective and the Hurst fractal index is applicable in the evaluation of the dam deformation trend.

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