• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.5
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• pp.161-170
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• 2022

The maintenance of bridges installed in rivers is carried out through facility safety inspection and repair & reinforcement procedures according to the results. Many studies have been so far conducted on the safety check of the bridge upperstructure because of the ease of access. However as it is impossible to directly investigate whether the pier foundation installed in the river has been scoured. Management of underwater foundations has remained based on theory. In this study, the scour of the bridge foundation installed in such a river was realized in 3D form by using an echo sounder and VRS. This made it possible to predict the scour pattern through comparison and analysis with the ground height of the riverbed at the time of the bridge installation. Based on these results, if the pier foundation is used as an initial data to determine whether or not local scour is present and to predict long-term scouring, bridge collapse due to foundation scour can be prevented.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.205-222
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• 2022

This study aims to improve the information service so that construction engineers and construction project participants without specialized knowledge can easily understand the important words and the interrelationships between them in construction practice. To this end, using text mining and network centrality, the frequency of occurrence of words, topic modeling, and network centrality in construction practice information such as technical information, case information, and cost reduction, which are most used in the Construction Technology Digital Library, were analyzed. Through this analysis, design, construction, project management, specifications, standards, and maintenance related to road construction such as roads, pavements, bridges, and tunnels were identified as important in construction practice. In addition, correlations were analyzed for words with high importance by measuring Degree Centrality and Eigenvector Centrality. The result was that more useful information could be provided if the technical information was expanded. Finally, we presented the limitations of the study results and additional studies according to the limitations.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Advances in materials Research
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• v.13 no.2
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• pp.115-128
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• 2024

Concrete is the most significant material in the construction industry which is required to construct several facilities like roads, buildings, and bridges etc. which leads to the economic development of a country. But now days, in view of sustainable development and environmental problems, plastic waste management is one of the major environmental issues due to its non-biodegradable nature which allows it to stay in the landfills until they are cleaned up. To overcome all these concerns, plastic waste may be used as a substitute of natural fine and coarse aggregate in concrete and a valuable solution to utilize the plastic items which causes several problems. In order to, present study is focused on the affecting properties of concrete as workability, compressive strength, and tensile strength of concrete with using plastic waste and without using plastic waste. Based on the detailed literature, it was observed that the plastic waste is not affecting the quality and consistency of concrete. However, as the number of PVC particles in the mixture increased, the drying shrinkage values decreased and the inclusion of plastic flakes can mitigate drying shrinkage cracking which leads the higher durability of concrete. Based on the comprehensive literature, it was also observed that the plastic aggregate found to be suitable for low and medium strength concrete. However, the investigation on the application of plastic aggregate in the high strength concrete is found limited. It was concluded that the optimum percentage of the plastic aggregate was found about 20%.

• Smart Structures and Systems
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• v.33 no.2
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• pp.93-103
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• 2024

Despite the rapid development of sensors, structural health monitoring (SHM) still faces challenges in monitoring due to the degradation of devices and harsh environmental loads. These challenges can lead to measurement errors, missing data, or outliers, which can affect the accuracy and reliability of SHM systems. To address this problem, this study proposes a classification method that detects anomaly patterns in sensor data. The proposed classification method involves several steps. First, data scaling is conducted to adjust the scale of the raw data, which may have different magnitudes and ranges. This step ensures that the data is on the same scale, facilitating the comparison of data across different sensors. Next, informative features in the time and frequency domains are extracted and used as input for a deep neural network model. The model can effectively detect the most probable anomaly pattern, allowing for the timely identification of potential issues. To demonstrate the effectiveness of the proposed method, it was applied to actual data obtained from a long-span cable-stayed bridge in China. The results of the study have successfully verified the proposed method's applicability to practical SHM systems for civil infrastructures. The method has the potential to significantly enhance the safety and reliability of civil infrastructures by detecting potential issues and anomalies at an early stage.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.6
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• pp.1-9
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• 2016

The paper aims at evaluating effects of preventive maintenance on life cycle cost(LCC) reduction of bridges. The preventive maintenance activities capable to delay bridge deteriorations can reduce overall maintenance costs and extend service life of a bridge by regularly providing maintenance activities and avoiding larger maintenance(repairs or rehabilitations) costs. Couple of prediction models were proposed in order to calculate LCC of a typical bridge: a health score model and repair rehabilitation cost model. In addition, the maintenance activities such as wash and painting were also suggested in order to consider effects of preventive maintenance in the analysis based on literature reviews. According to analysis results, new maintenance strategy(reactive maintenance + preventive maintenance) can save \0.5 billion per bridge for future life-cycle costs over 100 year analysis or \184 billion for entire HBMS(Highway Bridge Management System) inventory over 20 years. Small investments for preventive maintenance in improved bridge management can have a very significant return when considering the large bridge inventory.

• Korean Journal of Construction Engineering and Management
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• v.19 no.6
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• pp.46-54
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• 2018

In the 21st century, the Earth has continued its efforts to reduce carbon emissions to overcome the crisis caused by climate change. The construction industry, which is a representative industry that produces large amounts of the environmental load during construction, should actively reduce the amount of the environmental load. From the planning stage of the construction facility, it is necessary to consider the environmental load such as route selection and structure type selection to reduce the environmental load. However, the environmental load can be estimated based on the input resource amount. However, in the planning stage, it is difficult to accurately calculate the environmental load due to lack of information on the construction amount. The purpose of this study is to select the environmental load factors for IPC girder bridges to be used in the environmental load estimation model in the planning stage. Specific information related to the environmental load was selected from a list of information available in the planning stage, reflecting the Life Cycle Assessment(LCA), correlation, principal components analysis and expert opinion. The list of selected planning stage information is 10 such as span length and bridge extension, and it is expected to be used as a basic data for the future development of environmental load estimation model.

• Journal of the Korean Society for Railway
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• v.13 no.1
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• pp.71-77
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• 2010

It is increasing annually that the cost for bridge Maintenance Repair & Rehabilitation (MR&R) in developed countries. Based on Intelligent Technology, Bridge Management System (BMS) is developed for optimization of Life Cycle Cost (LCC) and reliability to predict long-term bridge deteriorations. However, such data are very limited amongst all the known bridge agencies, making it difficult to reliably predict future structural performances. To alleviate this problem, an Artificial Neural Network (ANN) based Backward Prediction Model (BPM) for generating missing historical condition ratings has been developed. Its reliability has been verified using existing condition ratings from the Maryland Department of Transportation, USA. The function of the BPM is to establish the correlations between the known condition ratings and such non-bridge factors as climate and traffic volumes, which can then be used to obtain the bridge condition ratings of the missing years. Since the non-bridge factors used in the BPM can influence the variation of the bridge condition ratings, well-selected non-bridge factors are critical for the BPM to function effectively based on the minimized discrepancy rate between the BPM prediction result and existing data (deck; 6.68%, superstructure; 6.61%, substructure; 7.52%). This research is on the generation of usable historical data using Artificial Intelligence techniques to reliably predict future bridge deterioration. The outcomes (Long-term Bridge deterioration Prediction) will help bridge authorities to effectively plan maintenance strategies for obtaining the maximum benefit with limited funds.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.232-239
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• 2001

Young Jong Grand Bridge is approach traffic road of New Inchon International Airport which covers hub airport function in northeast asia. The total span length of this bridge is $4,420{\cal}m$ and this main bridge type is, the first in the world, Double Deck Self Anchored Suspension Bridge, designed as double deck systems to be arranged by road and railroad. Approach bridges to be connected with main span also are composed double deck steel truss and steel box girder to consider a continuity with this span. Our company erected $1,375{\cal}m$(about 60,000tons) of double deck steel truss bridge type which is composed by 6 traffic lane on upper deck and 4 traffic lane and Double track railroad on lower deck. The original installation method of this bridge was planed to install about 75 meters bridge blocks to use floating crane, after temporary bent was constructed between permanent piers. But this method which had to construct many temporary bents in the sea had the matter that construction periods can become lengthen and construction cost can be risen. To overcome the uncertainty to ensure high qualify of bridge and economic project execution, our company developed new bridge erection method to assure both quality control and economic construction work. The new erection method which was developed by us was one that could transport and install long bridge block, $120{\cal}m$ unit at a time and that temporary bent was not required. We hope that this paper is used as technical data which will erect bridge in the western sea and others marine region.

• International Journal of Contents
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• v.11 no.2
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• pp.37-49
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• 2015

Sensors, equipment, ICT facilities and their corresponding software have a relatively short lifetime relative to that of constructional structure, so these devices have to be continuously fixed or exchanged during maintenance and management. Furthermore, software or analysis tools should be periodically upgraded according to advances in ICT and analysis technology. Conventional monitoring systems have serious problems in that it is difficult for site engineers to modify or upgrade hardware and analysis algorithms. Moreover, we depend on the original system developer when we want to modify or upgrade inner program structures. In this paper, we propose a novel design for integrated maintenance and management of a monitoring system by applying the mobile cloud concept. The system is intended for use in disaster prevention of constructional structures, including bridges, tunnels, and in traditional buildings in a local heritage village, we analyze the status of these structures over a long term or a short-term period as well as in disaster situations. Data are collected over a mobile cloud and future expectations are analyzed according to probabilistic and statistical techniques. We implement our integrated monitoring system to solve the existing problems mentioned above. The final goal of this study is to design and implement a monitoring system for more than 10,000 structures spread within Korea. Furthermore, we can specifically apply the monitoring system presented here to a bridge made from timber in Asan Oeam Village and a traditional house in Andong Hahoe Village to monitor for possible disasters. The entire system design and implementation can be developed on the LinkSaaS platform and the monitoring services can also be implemented on the platform. We prove that the proposed system has good performance by performing a TTA authentication test, web accommodation test, and operation test using emulated data.