Tong Wang

Bio: Tong Wang is an academic researcher from Beijing University of Civil Engineering and Architecture. The author has contributed to research in topics: Lofting & Inertial navigation system. The author has an hindex of 3, co-authored 6 publications receiving 19 citations.

A Deformation Prediction Approach for Supertall Building Using Sensor Monitoring System

Abstract: Using high-precision sensors to monitor and predict the deformation trend of supertall buildings is a hot research topic for a long time. And in terms of deformation trend prediction, the main way to realized deformation trend prediction is the deep learning algorithm, but the accuracy of prediction result needs to be improved. To solve the problem described above, firstly, based on the conditional deep belief network (CDBN) model, the levenberg-marquardt (LM) was used to optimize the CDBN model; the LM-CDBN model has been constructed. Then taking CITIC tower, the tallest building in Beijing as the research object, the real-time monitoring data of the shape acceleration array (SAA) as an example, we used LM-CDBN model to analyse and predict the building deformation. Finally, to verify the accuracy and robustness of LM-CDBN model, the prediction results of the LM-CDBN model are compared with the prediction results of the CDBN model, the extreme learning machine (ELM) model, and the unscented Kalman filter-support vector regression (UKF-SVR) model, and we evaluated the result from three aspects: training error, fitness, and stability of prediction results. The results show that the LM-CDBN model has higher precision and fitting degree in the prediction of deformation trend of supertall buildings. And the MRE, MAE, and RMSE of the LM-CDBN model prediction results are only 0.0060, 0.0023mm, and 0.0031mm, and the prediction result was more in line with the actual deformation trend.

A rainwater control optimization design approach for airports based on a self-organizing feature map neural network model.

Abstract: To address the problems of high overflow rate of pipe network inspection well and low drainage efficiency, a rainwater control optimization design approach based on a self-organizing feature map neural network model (SOFM) was proposed in this paper. These problems are caused by low precision parameter design in various rainwater control measures such as the diameter of the rainwater pipe network and the green roof area ratio. This system is to be combined with the newly built rainwater pipe control optimization design project of China International Airport in Daxing District of Beijing, China. Through the optimization adjustment of the pipe network parameters such as the diameter of the rainwater pipe network, the slope of the pipeline, and the green infrastructure (GI) parameters such as the sinking green area and the green roof area, reasonable control of airport rainfall and the construction of sustainable drainage systems can be achieved. This research indicates that compared with the result of the drainage design under the initial value of the parameter, the green roof model and the conceptual model of the mesoscale sustainable drainage system, in the case of a hundred-year torrential rainstorm, the overflow rate of pipe network inspection wells has reduced by 36% to 67.5%, the efficiency of drainage has increased by 26.3% to 61.7%, which achieves the requirements for reasonable control of airport rainwater and building a sponge airport and a sustainable drainage system.

Automatic point lofting robot and method

Abstract: The invention relates to the technical field of engineering construction lofting, and specifically discloses an automatic point lofting robot and a method. The automatic point lofting robot comprisesa crawler type trolley and a point alignment module, wherein the point alignment module comprises a 360-degree prism, a minitype prism bar, a minitype range finder, a prism point finding and driving device, an automatic leveling device and a point marking module; the 360-degree prism and the point marking module are connected through the minitype prism bar and are always in coaxial arrangement; the prism point finding and driving device is connected with the upper part of the minitype prism bar and is used for moving the minitype prism bar so as to accurately align to to-be-lofted points; theautomatic leveling device is connected with the upper part of the minitype prism bar and is used for leveling the point alignment module; the minitype range finder is arranged on the minitype prism bar and is used for measuring elevation of the points; the point marking module is arranged at the lowest part of the point alignment module and is used for marking the to-be-lofted points. By adoptingthe automatic point lofting robot disclosed by the invention, automatic lofting of the points can be realized, the efficiency is high, short time is consumed, and the point accuracy is higher.

Multifunctional vertical survey-connection survey integrated three-dimensional coordinate transmission device and method

Abstract: The invention relates to the technical field of engineering survey, and discloses a multifunctional vertical survey-connection survey integrated three-dimensional coordinate transmission device and amultifunctional vertical survey-connection survey integrated three-dimensional coordinate transmission method. The multifunctional vertical survey-connection survey integrated three-dimensional coordinate transmission device comprises an upper device and a bottom device, wherein the upper device comprises a level-adjustable pedestal, vertical rods and horizontal rods; the vertical rods are vertically arranged on the pedestal of the upper device; the horizontal rods are arranged at the upper parts of the vertical rods and are vertical to the vertical rods; laser ranging collimators are arrangedon the horizontal rods; 360-degree prisms are arranged on the laser ranging collimators; the centers of the laser ranging collimators are superposed with the optical centers of the 360-degree prismsthereon; the bottom device comprises a level-adjustable pedestal and a laser receiving target; the laser receiving target is arranged on the pedestal of the bottom device; a 360-degree prism is arranged at the lower part of the laser receiving target; the center of the laser receiving target is coaxial with the optical center of the 360-degree prism at the lower part thereof. The multifunctional vertical survey-connection survey integrated three-dimensional coordinate transmission device has multiple functions of connection survey and vertical survey and is applicable to many conditions, and survey results are easy to check.

Research on FBG-Based Sensor Networks and Their Coexistence with Fiber Optical Transmission Systems

Abstract: Market forecasts and trends for the usage of fiber optical sensors confirm that demand for them will continue to increase in the near future. This article focuses on the research of fiber Bragg grating (FBG) sensor network, their applications in IoT and structural health monitoring (SHM), and especially their coexistence with existing fiber optical communication system infrastructure. Firstly, the spectrum of available commercial optical FBG temperature sensor was experimentally measured and amplitude-frequency response data was acquired to further develop the simulation model in the environment of RSoft OptSim software. The simulation model included optical sensor network, which is combined with 8-channel intensity-modulated wavelength division multiplexed (WDM) fiber optical data transmission system, where one shared 20 km long ITU-TG.652 single-mode optical fiber was used for transmission of both sensor and data signals. Secondly, research on a minimal allowable channel spacing between sensors’ channels was investigated by using MathWorks MATLAB software, and a new effective and more precise determination algorithm of the exact center of the sensor signal’s peak was proposed. Finally, we experimentally show successfully operating coexistence concept of the spectrum-sliced fiber optical transmission system with embedded scalable FBG sensor network over one shared optical fiber, where the whole system is feed by only one broadband light source.

Comparative investigation on deformation monitoring and numerical simulation of the deepest excavation in Beijing

Abstract: The investigation on the mechanical mechanism of the retaining and protection structure of superdeep excavations is an important subject, and its deformation control research is particularly important. Taking the deepest foundation pit with full-section overall excavation in Beijing as an example, this paper studies the stress and deformation characteristics of composite soil nailing wall and anchored soldier pile wall combined retaining system under complex geological conditions of multi-layer groundwater during the process of excavating 31.4 m deep. The Midas simulation software and monitoring data are used to analyze the construction process of foundation excavation. The simulated results and monitored values of anchor force, ground settlement, and soil deformation during foundation excavation are analyzed and discussed to verify the reliability of the model. The spatial effect of internal force and deformation of composite retaining and protection structure for superdeep foundation excavation is discussed, and the parameters affecting the retaining structure are analyzed and investigated. The research shows that with the increase of excavation depth, both the extremum of the bending moment and the extremum of lateral displacement of retaining piles are increasing, and the position of the extremum of bending moment is located near the excavation face; then, the bending moment reaches the maximum when the foundation pit is excavated to the bottom. Within a certain range, increasing the rigidity or the embedded depth of the retaining pile or the prestress of the anchor can effectively control the lateral displacement of the excavation. However, when the rigidity of retaining pile is too large, its lateral displacement does not change significantly. Similarly, when the embedded depth is too long or the prestress of the anchor is too powerful, the effect of controlling deformation is also not obvious. The research results will provide theoretical basis and practical experience for the design and construction of superdeep excavations.

Marker-Based Structural Displacement Measurement Models with Camera Movement Error Correction Using Image Matching and Anomaly Detection.

Abstract: To prevent collapse accidents at construction sites, the marker-based displacement measurement method was developed. However, it has difficulty in obtaining accurate measurements at long distances (>50 m) in an outdoor environment because of camera movements. To overcome this problem, marker-based structural displacement measurement models using image matching and anomaly detection were designed in this study. Then, the performance of each model in terms of camera movement error correction was verified through comparison with that of a conventional model. The results show that the systematic errors due to camera movements (<1.7°) were corrected. The detection rate of markers with displacement reached 95%, and the probability that the error size would be less than 10 mm was ≥ 95% with a 95% confidence interval at a distance of more than 100 m. Moreover, the normalized mean square error was less than 0.1. The models developed in this study can measure the pure displacement of an object without the systematic errors caused by camera movements. Furthermore, these models can be used to measure the displacements of distant structures using closed-circuit television cameras and markers in an outdoor environment with high accuracy.

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

Abstract: 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.