• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.623-629
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• 2012

During the construction of crossing engineering one of the important measures to ensure the safety of subway operation is the implementation of deformation surveying to the existing subway tunnel. Guangzhou new subway line 2 engineering which crosses the existing tunnel is taken as the background. How to achieve intelligent and automatic deformation surveying forecast during the subway tunnel construction process is studied. Because large amount of surveying data exists in the subway construction, deformation analysis is difficult and prediction has low accuracy, a subway intelligent deformation prediction model based on the PBIL and support vector machine is proposed. The PBIL algorithm is used to optimize the exact key parameters combination of support vector machine though probability analysis and thereby the predictive ability of the model deformation is greatly improved. Through applications on the Guangzhou subway across deformation surveying deformation engineering the prediction method's predictive ability has high accuracy and the method has high practicality. It can support effective solution to the implementation of the comprehensive and accurate surveying and early warning under subway operation conditions with the environmental interference and complex deformation.

• Smart Structures and Systems
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• v.32 no.4
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

• Structural Monitoring and Maintenance
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• v.1 no.3
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• pp.249-271
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• 2014

This paper presents a unique study of Structural Health Monitoring (SHM) for the maintenance decision making about a real life movable bridge. The mechanical components of movable bridges are maintained on a scheduled basis. However, it is desired to have a condition-based maintenance by taking advantage of SHM. The main objective is to track the operation of a gearbox and a rack-pinion/open gear assembly, which are critical parts of bascule type movable bridges. Maintenance needs that may lead to major damage to these components needs to be identified and diagnosed timely since an early detection of faults may help avoid unexpected bridge closures or costly repairs. The fault prediction of the gearbox and rack-pinion/open gear is carried out using two types of Artificial Neural Networks (ANNs): 1) Multi-Layer Perceptron Neural Networks (MLP-NNs) and 2) Fuzzy Neural Networks (FNNs). Monitoring data is collected during regular opening and closing of the bridge as well as during artificially induced reversible damage conditions. Several statistical parameters are extracted from the time-domain vibration signals as characteristic features to be fed to the ANNs for constructing the MLP-NNs and FNNs independently. The required training and testing sets are obtained by processing the acceleration data for both damaged and undamaged condition of the aforementioned mechanical components. The performances of the developed ANNs are first evaluated using unseen test sets. Second, the selected networks are used for long-term condition evaluation of the rack-pinion/open gear of the movable bridge. It is shown that the vibration monitoring data with selected statistical parameters and particular network architectures give successful results to predict the undamaged and damaged condition of the bridge. It is also observed that the MLP-NNs performed better than the FNNs in the presented case. The successful results indicate that ANNs are promising tools for maintenance monitoring of movable bridge components and it is also shown that the ANN results can be employed in simple approach for day-to-day operation and maintenance of movable bridges.

• Journal of KIBIM
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• v.11 no.2
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• pp.24-32
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• 2021

Recently, starting with smart construction research, interest in technology that automates construction site management using artificial intelligence technology is increasing. In order to automate construction site management, it is necessary to recognize objects such as construction equipment or workers, and automatically analyze the relationship between them. For example, if the relationship between workers and construction equipment at a construction site can be known, various use cases of site management such as work productivity, equipment operation status monitoring, and safety management can be implemented. This study derives a real-time object detection platform architecture that is required when performing construction site management using deep learning technology, which has recently been increasingly used. To this end, deep learning models that support real-time object detection are investigated and analyzed. Based on this, a deep learning model development process required for real-time construction site object detection is defined. Based on the defined process, a prototype that learns and detects construction site objects is developed, and then platform development considerations and architecture are derived from the results.

• Smart Structures and Systems
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• v.26 no.1
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• pp.11-21
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• 2020

The hinge joint is the key to the overall cooperative working performance of the assembled beam bridge, and it is also the weakest part during the service period. This paper proposes a method for monitoring and evaluating the lateral cooperative working performance of fabricated beam bridges based on dynamic strain correlation coefficient indicator. This method is suitable for monitoring and evaluation of hinge joints status between prefabricated girders and overall cooperative working performance of bridge, without interruption of traffic and easy implementation. The remote cloud monitoring and diagnosis system was designed and implemented on a real assembled beam bridge. The algorithms of data preprocessing, online indicator extraction and status diagnosis were given, and the corresponding software platform and scientific computing environment for cloud operation were developed. Through the analysis of real bridge monitoring data, the effectiveness and accuracy of the method are proved and it can be used in the health monitoring system of such bridges.

• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.1-8
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• 2005

Measurement of convergence was/is carried out manually throughout the world for tunnels under construction. However, manual method has certain limitations in terms of applicability for the tunnels in operation. This paper describes state of the art of convergence monitoring systems which are available for measuring displacement of existing tunnels. These technologies are analyzed as follows: 1 The Sofo system using the fiber optic sensors has been applied to the stress measurement of the tunnel lining. It has not yet been used for the monitoring of tunnel convergence because of its cost and reliability 2. A TPMS(Tunnel Profile Monitoring System) using tilt sensors and displacement sensors is used for the convergence monitoring of highway tunnels, subway tunnels and underground ducts. 3. A BCS(Bassett Convergence System) using a pair of tilt sensors can be used for the convergence monitoring of tunnels, however the accuracy of the measurement has to be improved because it uses AC input voltage during data acquisition. The system has to be validated before it can be applied to the tunnels in operation. Convergence monitoring systems using TPMS and/or BCS are recommended to be evaluated and improved by a series or tests in tunnels under construction in order to be applied to the main measuring section and the tunnels in operation.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.714-716
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• 2004

Owing to the environmental problems as well as increasing energy prices and power plant construction costs, many researches have been made for the safe operation of distributed generations. In order to be more popularly used in parallel with the distribution network, the distributed generation and its correlation with the power system should be exactly monitored at any time. This paper presents a monitoring system which displays the important states of the distributed generation in operation and stores various measurements of the system. The proposed system constructs a data-base for developing algorithms against any faults of the interconnected system, and monitors efficiently at any place with the communication network function.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.04a
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• pp.3-16
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• 2000

The ultimate safety-goal of nuclear power plants should be targeted at preventing release of nuclear radiation compared to general structures, Accordingly the phases of siting design construction and operation of NPPs are severely regulated by codes of aseismic design so as to assure safety of NPPs. To accomplish this goal strict quality assurace and seismic qualification tests should be conducted for all phases of NPP construction. In addition seismic monitoring systems should be installed and always in operation to provide proper post-earhquake procedures. Besides periodic safety review should be performed during operation along with the seismic margin assessment. In this paper general procedures to secure seismic safety of NPPs are systematically reviewed and additional considerations for improvement are suggested.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.2
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• pp.493-500
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• 2015

The importance of the air and vessel traffic control center is increasing rapidly after the recent accident of 'Sewol ferry'. Aviation, marine, and the logistics sectors are already using monitoring and control technology widely. However, the monitoring and control system for complex and dangerous construction sites operation has yet to be employed. A monitoring/control system is required for effective communication between the control center and the construction equipment fleet at a construction site, and also the exact role that notifies accurate process and identification of hazards on construction sites as needed. Therefore, this paper presents the study about communication between the construction equipment fleet and the control center through the comparison of air traffic, marine, and logistics control systems for the development of construction equipment fleet management system.

• Structural Monitoring and Maintenance
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• v.5 no.1
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• pp.1-13
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• 2018

The Zhuhai Opera House has an external structure consisting of a type of spatial steel, where the stress of steel elements varies with the ambient temperature. A structural health monitoring system was implemented at Zhuhai Opera House, and the temperatures and stresses of the structures were monitored in real time. The relationship between the stress distribution and temperature variations was analysed by measuring the temperature and stresses of the steel elements. In addition to measurements of the structure stresses and temperatures, further simulation analysis was carried out to provide the detailed relationship between the stress distributions and temperature variations. The limited temperature measurements were used to simulate the structure temperature distribution, and the stress distributions of all steel elements of the structure were analysed by building a finite element model of the Zhuhai Opera House spatial steel structure. This study aims to reveal the stress distributions of steel elements in a real-world project based on temperature variations, and to supply a basic database for the optimal construction time of a spatial steel structure. This will not only provide convenient, rapid and safe early warnings and decision-making for the spatial steel structure construction and operation processes, but also improve the structural safety and construction accuracy of steel space structures.