• Structural Engineering and Mechanics
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• v.41 no.1
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• pp.139-155
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• 2012

Structural health monitoring systems are often installed on bridges to provide assessments of the need for structural maintenance and repair. Damage or deterioration may be detected by observation of changes in bridge characteristics evaluated from measured structural responses. However, construction materials such as concrete and steel cables exhibit certain time-dependent behaviour, which also results in changes in structural characteristics. If these are not accounted for properly, false alarms may arise. This paper proposes a systematic and efficient method to study the time-dependent effects on the dynamic properties of cable-stayed bridges. After establishing the finite element model of a cable-stayed bridge taking into account geometric nonlinearities and time-dependent behaviour, long-term time-dependent analysis is carried out by time integration. Then the dynamic properties of the bridge after a certain period can be obtained. The effects of time-dependent behaviour of construction materials on the dynamic properties of typical cable-stayed bridges are investigated in detail.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.4
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• pp.86-93
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• 2023

A bridge health monitoring technology is under development for the safety management of aged bridges. The bridge health monitoring technology has been developed mainly for single bridge management at a large scale, so it uses wire-based systems for power supply and data transfer. However, the wire-based systems need to be improved for the sporadically distributed small-sized bridges on local roads. This study proposed a wireless structural health monitoring system for small-sized bridges. The proposed monitoring system overcomes the limitations of wired systems by providing wireless power through solar power and utilizing LTE technology to transmit measurement data. In addition, a remote control system and power management plan were proposed to ensure the stability of the bridge measurement system. The proposed measurement system was installed on 32 bridges on fields and verified the operability by collecting 80.6% of measurement data for one year. The proposed system can support the health monitoring of aged bridges on local roads.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.2
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• pp.113-119
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• 2020

The IoT-based sensor network is one of the methods that can be efficiently applied to maintain the facilities, such as bridges, at a low cost. In this study, based on LoRa LPWAN, one of the IoT communications, sensor board for cable tension monitoring, data acquisition board for constructing sensor network along with existing measurement sensors, are developed to create bridge structural health monitoring system. In addition, we designed and manufactured a smart sensor node for LoRa communication and established a sensor network for monitoring. Further, we constructed a test bed at the Yeonggwang Bridge to verify the performance of the system. The test bed verification results suggested that the LoRa LPWAN-based sensor network can be applied as one of the technologies for monitoring the bridge structure soundness; this is excellent in terms of data rate, accuracy, and economy.

• Structural Monitoring and Maintenance
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• v.10 no.3
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• pp.191-205
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• 2023

In this paper, through operational modal analysis and ambient vibration tests, the dynamic characteristics of a multi-span simply-supported reinforced concrete highway bridge deck was determined and the results were used to assess the quality of construction of the individual spans. Supporting finite element (FE) models were created and analyzed according to the design drawings. After carrying out the dynamic tests and extracting the modal properties of the deck, the quality of construction was relatively assessed by comparing the results obtained from all the tests from the individual spans and the FE results. A comparison of the test results among the different spans showed a maximum difference value of around 9.3 percent between the superstructure's natural frequencies. These minor differences besides the obtained values of modal damping ratios, in which the differences were not more than 5 percent, can be resulted from suitable performance, health, and acceptable construction quality of the bridge.

• Earthquakes and Structures
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• v.22 no.5
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• pp.503-515
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• 2022

This paper presents the development of seismic fragility curves for a precast reinforced concrete bridge instrumented with a structural health monitoring (SHM) system. The bridge is located near an active seismic fault in the Dominican Republic (DR) and provides the only access to several local communities in the aftermath of a potential damaging earthquake; moreover, the sample bridge was designed with outdated building codes and uses structural detailing not adequate for structures in seismic regions. The bridge was instrumented with an SHM system to extract information about its state of structural integrity and estimate its seismic performance. The data obtained from the SHM system is integrated with structural models to develop a set of fragility curves to be used as a quantitative measure of the expected damage; the fragility curves provide an estimate of the probability that the structure will exceed different damage limit states as a function of an earthquake intensity measure. To obtain the fragility curves a digital twin of the bridge is developed combining a computational finite element model and the information extracted from the SHM system. The digital twin is used as a response prediction tool that minimizes modeling uncertainty, significantly improving the predicting capability of the model and the accuracy of the fragility curves. The digital twin was used to perform a nonlinear incremental dynamic analysis (IDA) with selected ground motions that are consistent with the seismic fault and site characteristics. The fragility curves show that for the maximum expected acceleration (with a 2% probability of exceedance in 50 years) the structure has a 62% probability of undergoing extensive damage. This is the first study presenting fragility curves for civil infrastructure in the DR and the proposed methodology can be extended to other structures to support disaster mitigation and post-disaster decision-making strategies.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.809-824
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• 2017

The accurate evaluation of wind characteristics and wind-induced structural responses during a typhoon is of significant importance for bridge design and safety assessment. This paper presents an expectation maximization (EM) algorithm-based angular-linear approach for probabilistic modeling of field-measured wind characteristics. The proposed method has been applied to model the wind speed and direction data during typhoons recorded by the structural health monitoring (SHM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. In the summer of 2015, three typhoons, i.e., Typhoon Chan-hom, Typhoon Soudelor and Typhoon Goni, made landfall in the east of China and then struck the Jiubao Bridge. By analyzing the wind monitoring data such as the wind speed and direction measured by three anemometers during typhoons, the wind characteristics during typhoons are derived, including the average wind speed and direction, turbulence intensity, gust factor, turbulence integral scale, and power spectral density (PSD). An EM algorithm-based angular-linear modeling approach is proposed for modeling the joint distribution of the wind speed and direction. For the marginal distribution of the wind speed, the finite mixture of two-parameter Weibull distribution is employed, and the finite mixture of von Mises distribution is used to represent the wind direction. The parameters of each distribution model are estimated by use of the EM algorithm, and the optimal model is determined by the values of $R^2$ statistic and the Akaike's information criterion (AIC). The results indicate that the stochastic properties of the wind field around the bridge site during typhoons are effectively characterized by the proposed EM algorithm-based angular-linear modeling approach. The formulated joint distribution of the wind speed and direction can serve as a solid foundation for the purpose of accurately evaluating the typhoon-induced fatigue damage of long-span bridges.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.345-365
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• 2020

At present, many machine leaning and data mining methods are used for analyzing and predicting structural response characteristics. However, the platform that combines big data analysis methods with online and offline analysis modules has not been used in actual projects. This work is dedicated to developing a multifunctional Hadoop-Spark big data platform for bridges to monitor and evaluate the serviceability based on structural health monitoring system. It realizes rapid processing, analysis and storage of collected health monitoring data. The platform contains offline computing and online analysis modules, using Hadoop-Spark environment. Hadoop provides the overall framework and storage subsystem for big data platform, while Spark is used for online computing. Finally, the big data Hadoop-Spark platform computational performance is verified through several actual analysis tasks. Experiments show the Hadoop-Spark big data platform has good fault tolerance, scalability and online analysis performance. It can meet the daily analysis requirements of 5s/time for one bridge and 40s/time for 100 bridges.

• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.4
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• pp.137-147
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• 1998

The neural net application was tried to develop the technique for monitoring the health status of a steel truss bridge which was scaled down to 1/15 of the real bridge for the laboratory experiments. The damage scenarios were chosen as 7 cases. The dynamic behavior, which was changed due to the breakage of the members, of the bridge was investigated by finite element analysis. The bridge consists of single spam, and eight (8) main structural subsystems. The loading vehicle, which weighs as 100 kgf, was operated by the servo-motor controller. The accelerometers were bonded on the surface of 7 cross-beams to measure the dynamic behavior induced by the abnormal structural condition. Artificial neural network technique was used to determine the severity of the damage. At first, the neural net was learnt by the results of finite element analysis, and also, the maximum detection error was 3.65 percents. Another neural net was also learnt, and verified by the experimental results, and in this case, the maximum detection error was 1.05 percents. In future study, neural net is necessary to be learnt and verified by various data from the real bridge.

• Smart Structures and Systems
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• v.24 no.6
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• pp.723-732
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• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.

• Structural Engineering and Mechanics
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• v.69 no.3
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• pp.347-359
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• 2019

This paper aims to evaluate the behavior of Dorim-Goh bridge in Seoul, Korea, under static and dynamic loads effects by ambient trucks. The prestressed concrete (PSC) girders and reinforcement concrete (RC) slab of the bridge are evaluated and assessed. A short period monitoring system is designed which comprises displacement, strain and accelerometer sensors to measure the bridge performance under static and dynamic trucks loads. The statistical analysis is used to assess the static behavior of the bridge and the wavelet analysis and probabilistic using Weibull distribution are used to evaluate the frequency and reliability of the dynamic behavior of the bridge. The results show that the bridge is safe under static and dynamic loading cases. In the static evaluation, the measured neutral axis position of the girders is deviated within 5% from its theoretical position. The dynamic amplification factor of the bridge girder and slab are lower than the design value of that factor. The Weibull shape parameters are decreased, it which means that the bridge performance decreases under dynamic loads effect. The bridge girder and slab's frequencies are higher than the design values and constant under different truck speeds.