• Smart Structures and Systems
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• 제24권5호
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Smart Structures and Systems
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• 제24권3호
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• pp.319-332
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• 2019

Structural health monitoring (SHM) is of great importance to super high-rise buildings. The Shanghai Tower is currently the tallest building in China, and a complete SHM system was simultaneously constructed at the beginning of the construction of the tower. Due to the variety of sensor types and the large number of measurement points in the SHM system, an online automatic structural health assessment method with few computations and no manual intervention is needed. This paper introduces a structural health assessment method for the Shanghai Tower that uses the coefficients of an autoregressive (AR) time series model as structural state indicators. An analysis of collected data indicates that the coefficients of the AR model are affected by environmental factors, and the principal component analysis method is used to remove the influence of environmental factors. Finally, the control chart method is used to track the changes in structural state indicators, and a plan for online automatic structure health state evaluation is proposed. This method is applied to long-term acceleration and inclination data from the Shanghai Tower and successfully identifies the changes in the structural state. Overall, the structural state indicators of the Shanghai Tower are stable, and the structure is in a healthy state.

• Wind and Structures
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• 제31권1호
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• pp.21-29
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• 2020

The wind field measurement of severe winds such as hurricanes (or typhoons), thunderstorm downbursts and other gales is important issue in wind engineering community, both for the construction and health monitoring of the wind-sensitive structures. Although several wireless data transmission systems have been available for the wind field measurement, most of them are not specially designed for the wind data measurement in structural wind engineering. Therefore, the field collection is still dominant in the field of structural wind engineering at present, especially for the measurement of the long-term and high-frequency wind speed data. In this study, for remote wind field measurement, a novel wireless long-term and high-frequency wind data acquisition system with the functions such as remote control and data compression is developed. The system structure and the collector are firstly presented. Subsequently, main functions of the collector are introduced. Also novel functions of the system and the comparison with existing systems are presented. Furthermore, the performance of this system is evaluated. In addition to as the wireless transmission for wind data and hardware integration for the collector, the developed system possesses a few novel features, such as the modification of wind data collection parameters by the remote control, the remarkable data compression before the data wireless transmission and monitoring the data collection by the cell phone application. It can be expected that this system would have wide applications in wind, meteorological and other communities.

• 한국환경복원기술학회지
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• 제18권1호
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• pp.135-151
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• 2015

DMZ is considered as an ecologically sensitive landscape and one of the highest biodiversity regions in the Republic of Korea. There have been, albeit the significant value, increased interests in developing this region for a variety of purposes including tourism and commemorative events. As this region has been already facing a range of problems derived from previous development, natural disaster and invasive species, the necessity for active management of ecological health within this region has been increased, which weighs the importance of executing ecological restoration. The objective of this study was to develop evaluation indices as an effective management means of properly evaluating ecological restoration and sustainably maintaining the restored conditions on a long-term scale. Through literature review existing evaluation indices related to restoration were collected, and then the most suitable indices were selected based upon two interviews and one questionnaire survey targeting experts in the relevant field to ecological restoration. They were categorized by two major division and their subclasses (Ecological base - vegetation structure & composition, habitat characteristics, soil environment; landscape ecology - connectivity, landscape patch, boundary & surrounding) and 40 indices. These indices were considered helpful to comprehensively evaluate ecological restoration on degraded environments within ecologically sensitive areas, and sustainably manage target areas by employing a long-term monitoring approach. As this result played a meaningful role in providing the fundamentals of evaluating ecological restoration, it should develop a suitable evaluation system through further research.

• Smart Structures and Systems
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• 제29권1호
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Smart Structures and Systems
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• 제1권4호
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• pp.355-368
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• 2005

Large and complex structures are being built now-a-days and, they are required to be functional even under extreme loading and environmental conditions. In order to meet the safety and maintenance demands, there is a need to build sensors integrated structural system, which can sense and provide necessary information about the structural response to complex loading and environment. Sophisticated tools have been developed for the design and construction of civil engineering structures. However, very little has been accomplished in the area of monitoring and rehabilitation. The employment of appropriate sensor is therefore crucial, and efforts must be directed towards non-destructive testing techniques that remain functional throughout the life of the structure. Fiber optic sensors are emerging as a superior non-destructive tool for evaluating the health of civil engineering structures. Flexibility, small in size and corrosion resistance of optical fibers allow them to be directly embedded in concrete structures. The inherent advantages of fiber optic sensors over conventional sensors include high resolution, ability to work in difficult environment, immunity from electromagnetic interference, large band width of signal, low noise and high sensitivity. This paper brings out the potential and current status of technology of fiber optic sensors for civil engineering applications. The importance of employing fiber optic sensors for health monitoring of civil engineering structures has been highlighted. Details of laboratory studies carried out on fiber optic strain sensors to assess their suitability for civil engineering applications are also covered.

• Smart Structures and Systems
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• 제12권3_4호
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• pp.415-426
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• 2013

Condition assessment and monitoring of bridges is critical for safe passenger travel, public transportation, and efficient freight. In monitoring, displacement measurement capability is important to keep track of performance of bridge, in part or as whole. One of the most important parts of a bridge is the expansion joint, which accommodates continuous cyclic thermal expansion of the whole bridge. Though expansion joint is critical for bridge performance, its inspection and monitoring has not been considered significantly because the monitoring requires long-term data using cost intensive equipment. Recently, a wireless smart sensor network (WSSN) has drawn significant attention for transportation infrastructure monitoring because of its merits in low cost, easy installation, and versatile on-board computation capability. In this paper, a rapid wireless displacement monitoring system, wireless hybrid sensor (WHS), has been developed to monitor displacement of expansion joints of bridges. The WHS has been calibrated for both static and dynamic displacement measurement in laboratory environment, and deployed on an in-service highway bridge to demonstrate rapid expansion joint monitoring. The test-bed is a continuous steel girder bridge, the Founders Bridge, in East Hartford, Connecticut. Using the WHS system, the static and dynamic displacement of the expansion joint has been measured. The short-term displacement trend in terms of temperature is calculated. With the WHS system, approximately 6% of the time has been spent for installation, and 94% of time for the measurement showing strong potential of the developed system for rapid displacement monitoring.

• Smart Structures and Systems
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• 제20권1호
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• pp.11-22
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• 2017

The long-term effect of ambient temperature on bridge strain is an important and challenging problem. To investigate this issue, one year data of strain and ambient temperature of a long-span cable-stayed bridge is studied in this paper. The measured strain-time history is decomposed into two parts to obtain the strains due to vehicle load and temperature alone. A linear regression model between the temperature and the strain due to temperature is established. It is shown that for every $1^{\circ}C$ increase in temperature, the stress is increased by 0.148 MPa. Furthmore, the extreme value distributions of the strains due to vehicle load, temperature and the combination effect of them during the remaining service period are estimated by the average conditional exceedance rate approach. This approach avoids the problem of declustering of data to ensure independence. The estimated results demonstrate that the 95% quantile of the extreme strain distribution due to temperature is up to $1.488{\times}10^{-4}$ which is 2.38 times larger than that due to vehicle load. The study also indicates that the estimated extreme strain can reflect the long-term effect of temperature on bridge strain state, which has reference significance for the reliability estimation and safety assessment.

• Journal of Korean Society for Atmospheric Environment
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• 제19권E3호
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• pp.99-120
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• 2003

Visibility impairment in an urban area is mainly caused by airborne fine particulate matters. Visibility in a clean air environment is more sensitive to the change of PM$_{2.5}$ particle concentrations. However, a proportionally larger reduction in fine particle concentration is needed to achieve a small increment of visibility improvement in polluted areas. Continuous optical monitoring of atmospheric visibility and extensive aerosol measurements have been made in the urban atmosphere of Kwangju, Korea. The mean for fine particulate mass from 1999 to 2002 at Kwangju was measured to be 23.6$\pm$20.3 $\mu\textrm{g}$/㎥. The daily average seasonal visual range was measured to be 13.1, 9.2, 11.0, and 13.9 km in spring, summer, fall, and winter, respectively. The mean light extinction budgets by sulfate, nitrate, organic carbon, and elemental carbon aerosol were observed to be 27, 14, 22, and 12%, respectively. It is highly recommended that a new visibility standard and/or a fine particle standard be established in order to protect the health and welfare of general public. Much more work needs to be done in visibility studies, including long-term monitoring of visibility, improvement of visibility models, and formulating integrated strategies for managing fine particles to mitigate the visibility impairment and climate change.e.

• Smart Structures and Systems
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• 제24권4호
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• pp.459-474
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• 2019

This study develops a novel recursive algorithm to significantly enhance the computation efficiency of a recently proposed stochastic subspace identification (SSI) methodology based on an alternative stabilization diagram. Exemplified by the measurements taken from the two investigated office buildings, it is first demonstrated that merely one sixth of computation time and one fifth of computer memory are required with the new recursive algorithm. Such a progress would enable the realization of on-line and almost real-time monitoring for these two steel framed structures. This recursive SSI algorithm is further applied to analyze 20 months of monitoring data and comprehensively assess the environmental effects. It is certified that the root-mean-square (RMS) response can be utilized as an excellent index to represent most of the environmental effects and its variation strongly correlates with that of the modal frequency. More detailed examination by comparing the monthly correlation coefficient discloses that larger variations in modal frequency induced by greater RMS responses would typically lead to a higher correlation.