Dongwei Qiu

Bio: Dongwei Qiu is an academic researcher from Beijing University of Civil Engineering and Architecture. The author has contributed to research in topics: Computer science & Vehicle routing problem. The author has an hindex of 4, co-authored 26 publications receiving 59 citations. Previous affiliations of Dongwei Qiu include Beijing Jiaotong University & Peking University.

An Improved Particle Filter Algorithm for Geomagnetic Indoor Positioning

Abstract: Geomagnetic indoor positioning is an attractive indoor positioning technology due to its infrastructure-free feature. In the matching algorithm for geomagnetic indoor localization, the particle filter has been the most widely used. The algorithm however often suffers filtering divergence when there is continuous variation of the indoor magnetic distribution. The resampling step in the process of implementation would make the situation even worse, which directly lead to the loss of indoor positioning solution. Aiming at this problem, we have proposed an improved particle filter algorithm based on initial positioning error constraint, inspired by the Hausdorff distance measurement point set matching theory. Since the operating range of the particle filter cannot exceed the magnitude of the initial positioning error, it avoids the adverse effect of sampling particles with the same magnetic intensity but away from the target during the iteration process on the positioning system. The effectiveness and reliability of the improved algorithm are verified by experiments.

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.

Application of virtual reality technology in bridge structure safety monitoring

Abstract: Virtual reality technology is an immersive interactive technology which is based on computer technology and data processing technology From virtual reality users can operate and apperceive all of the various objects in the virtual world and make access to the space and logical information implied among the virtual environment Bridge approach slabs are designed to function as a transitional roadway to the bridge deck Because of the stiffness difference and settlement deformation performance difference of bridge abutments and subgrade, it results in a differential settlement on the joint of bridge deck and road pavement which causes the steps or significant changes in lengthwise slope The bumps and jumps will occur when the high-speed vehicles get across and it leads to the phenomenon of bridge bump In this paper, the researched object is the bridge approach of Yushuguan Bridge in Beijing, China Virtual reality technology is used to build the three-dimensional model of the bridge, and on this basis, the advanced safety monitoring of bridge is implemented The establishment method of three-dimensional bridge scenery is introduced The terrestrial laser scanning is applied to given precision panorama deformation monitoring of bridge approach The 3D monitoring method is introduced here Firstly, establish a high-precision horizontal control network with GPS Secondly build elevation control network with the digital level Thirdly achieve 3D model reconstruction of the bridge approach using three-dimensional laser scanner by collecting the point cloud data, registering the laser point cloud data, building triangulation model and generating solid models Thus we can obtain the 3D image of the bridge approach deformation in a precision and stereo way effectively

Applying advanced technology to improve safety management in the construction industry: a literature review

Abstract: Technology application is deemed an effective way to further construction safety management. Various technologies have been adopted for construction safety, including information communication technology (ICT), sensor-based technology, 3S (GIS/GPS/RS) technology, radio frequency identification (RFID) and virtual reality. A review of previous studies in the area of technology applications for construction safety would be indispensable for the main stakeholders in this field to share innovative research findings and gain access to future research trends. A three-step method was used to obtain relevant publications (119 papers met the ultimate selection criteria) and compile a database of the findings. The results present a general review of technology application for construction safety from the aspects of number of papers published annually, publication type, publication name, country/region of distribution, research level, project phase and project type. Corresponding analysis was performed with the colle...

Applications of terrestrial laser scanning for tunnels : a review

Abstract: In recent years, the use of terrestrial laser scanning (TLS) technique in engineering surveys is gaining an increasing interest due to the advantages of non-contact, rapidity, high accuracy, and large scale. Millions of accurate 3D points (mm level accuracy) can be delivered by this technique with a high point density in a short time (up to 1 million points per second), which makes it a potential technique for large scale applications in engineering environments such as tunnels, bridges, and heritage buildings. Tunnels, in particular those with long lengths, create great challenges for surveyors to obtain the satisfactory scanned data. This paper presents a short history of TLS techniques used for tunnels. A general overview of TLS techniques is given, followed by a review of several applications of TLS for tunnels. These applications are classified as: detecting geological features of drilling tunnels, monitoring the geometry of tunnels during excavation, making deformation measurements, and extracting features. The review emphasizes how TLS techniques can be used to measure various aspects of tunnels. It is clear that TLS techniques are not yet a common tool for tunnel investigations, but there is still a huge potential to excavate.

Research on the construction risk control technology of shield tunnel underneath an operational railway in sand pebble formation: a case study

Abstract: It’s inevitable that, various types of construction works emerge within proximity during the large-scale construction of urban rail transit, thus posing huge challenges for the actual construction....

Indoor Positioning Method Using WiFi RTT Based on LOS Identification and Range Calibration

Abstract: WiFi-based indoor positioning methods have attracted extensive attention due to the wide installation of WiFi access points (APs). Recently, the WiFi standard was modified and introduced into a new two-way approach based on round trip time (RTT) measurement, which brings some changes for indoor positioning based on WiFi. In this work, we propose a WiFi RTT positioning method based on line of sight (LOS) identification and range calibration. Given the complexity of the indoor environment, we design a non-line of sight (NLOS) and LOS identification algorithm based on scenario recognition. The positioning scenario is recognized to assist NLOS and LOS distances identification, and gaussian process regression (GPR) is utilized to construct the scenario recognition model. Meanwhile, the calibration model for LOS distance is presented to correct the measuring distance and the scenario information is utilized to constrain the estimated position. When there is a positioning request, the positioning scenario is identified with the scenario recognition model, and LOS measuring distance is obtained based on the recognized scenario. The LOS range measurements are first calibrated and then utilized to estimate the position of the smartphone. Finally, the positioning scenario is used to constrain the estimation location to avoid it beyond the scenario. The experimental results show that the positioning effect of the proposed method is far better than that of the Least Squares (LS) algorithm, achieving a mean error (ME) of 0.862 m and root-mean-square error (RMSE) of 0.989 m.