• Structural Monitoring and Maintenance
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• v.1 no.3
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• pp.293-308
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• 2014

In the aeronautical environment, numerous regulatory and communication protocols exist that cover interconnection of on-board equipment inside the aircraft. Developed and implemented by the airlines since the 1960s, these communication systems are reliable, strong, certified and able to contact different sensors distributed throughout the aircraft. However, the scenario is slightly different in the structural health monitoring (SHM) field as the requirements and specifications that a global SHM communication system must fulfill are distinct. The number of SHM sensors installed in the aircraft rises into the thousands, and it is impossible to maintain all of the SHM sensors in operation simultaneously because the overall power consumption would be of thousands of Watts. This design of a new communication system must consider aspects as management of the electrical power supply, topology of the network for thousands of nodes, sampling frequency for SHM analysis, data rates, selected real-time considerations, and total cable weight. The goal of the research presented in this paper is to describe and present a possible integration scheme for the large number of SHM sensors installed on-board an aircraft with low power consumption. This paper presents a new communications system for SHM sensors known as the Bi-Instruction Link Bi-Operator (BILBO).

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.675-687
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• 2010

Structural Health Monitoring (SHM) is the science and technology of monitoring and assessing the condition of aerospace, civil and mechanical infrastructures using a sensing system integrated into the structure. Impedance-based SHM measures impedance of a structure using a PZT (Lead Zirconate Titanate) patch. This paper presents a low-power wireless autonomous and active SHM node called Autonomous SHM Sensor 2 (ASN-2), which is based on the impedance method. In this study, we incorporated three methods to save power. First, entire data processing is performed on-board, which minimizes radio transmission time. Considering that the radio of a wireless sensor node consumes the highest power among all modules, reduction of the transmission time saves substantial power. Second, a rectangular pulse train is used to excite a PZT patch instead of a sinusoidal wave. This eliminates a digital-to-analog converter and reduces the memory space. Third, ASN-2 senses the phase of the response signal instead of the magnitude. Sensing the phase of the signal eliminates an analog-to-digital converter and Fast Fourier Transform operation, which not only saves power, but also enables us to use a low-end low-power processor. Our SHM sensor node ASN-2 is implemented using a TI MSP430 microcontroller evaluation board. A cluster of ASN-2 nodes forms a wireless network. Each node wakes up at a predetermined interval, such as once in four hours, performs an SHM operation, reports the result to the central node wirelessly, and returns to sleep. The power consumption of our ASN-2 is 0.15 mW during the inactive mode and 18 mW during the active mode. Each SHM operation takes about 13 seconds to consume 236 mJ. When our ASN-2 operates once in every four hours, it is estimated to run for about 2.5 years with two AAA-size batteries ignoring the internal battery leakage.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.546-548
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• 2010

This paper proposes a new baseline-free TR-based SHM method in which the time-reversal (TR) property of the guided Lamb waves is utilized. The new TR-based SHM method has two distinct features when compared with the other existing SHM techniques: (1) The measurement- based backward TR process is replaced by the computation-based process (2) In place of the comparison method most commonly used for SHM, the TOF information of the damage signal extracted from the reconstructed signal is utilized for the damage diagnosis. For the damage diagnosis, the imaging method is adopted to efficiently detect damage by representing the damage as an image. The proposed TR-based SHM technique is then validated through the damage diagnosis experiment for an aluminum plate with a damage at different locations.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.197-205
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• 2018

Nowadays, wireless sensor networks (WSNs) have been widely adopted in structural health monitoring (SHM) systems for social overhead capital (SOC) public infrastructures. Structural health information, environmental disturbances and sudden changes of weather conditions, damage detections, and external load quantizing are among the capabilities required of SHM systems. These information requires an efficient transmission with which an efficient mobility management support for wireless networks can provide. This paper deals with the analysis of mobility management schemes in order to address the real-time requirement of data traffic delivery for critical SHM information. The host-based and network-based mobility management protocols have been identified and the advantages of network mobility (NEMO) enabled Proxy Mobile Internet Protocol version 6 (PMIPv6) have been leveraged in order to address the SHM information transmission needs. The scheme allows an efficient information transmission as it improves the handover performance due to shortened handover latency as well as reduced signaling overhead.

• Journal of the Korea Computer Graphics Society
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• v.14 no.1
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• pp.11-16
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• 2008

This paper introduces a novel real-time soft shadowing method applicable for height maps. As well as supporting self-shadowing of the height map, our method allows shadows to be caught on other objects. The method is very suitable for dynamically changing height maps because it requires no precomputation. A shadow height map (SHM) is a new structure which represents the height of the shadow at each discretized coordinate of a height map. Constructing the SHM is O(n), where n is the number of texels in the SHM. Shadow can be computed from this map quickly and simply, using a pixel shader. Examples demonstrate good real-time performance and plausible visual quality.

• Smart Structures and Systems
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• v.15 no.3
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• pp.555-576
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• 2015

The large number of long-span bridges constructed in China motivates the applications of structural health monitoring (SHM) technology. Many bridges have been equipped with sophisticated SHM systems in the mainland of China and in Hong Kong of China. Recently, SHM technology has been extended to field test systems. In this view, SHM can serve as a tool to develop the methods of life-cycle performance design, evaluation, maintenance and management of bridges; to develop new structural analysis methods through validation and feedback from SHM results; and to understand the behavior of bridges under natural and man-made disasters, rapidly assess the damage and loss of structures over large regions after disasters, e.g., earthquake, typhoon, flood, etc. It is hoped that combining analytical methods, numerical simulation, small-scale tests and accelerated durability tests with SHM could become the main engine driving the development of bridge engineering. This paper demonstrates the above viewpoint.

• Resources Recycling
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• v.13 no.5
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• pp.3-16
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• 2004

There are over 190,000 tons per year of the sludge containing heavy metals (SHM) generated from industries in Korea. The SHM is so hazardous waste, it needs proper intermediate treatment before final disposal. At present, the common intermediate treatment and final disposal technologies of SHM are solidification and landfill. However, the future treatment and disposal technologies of SHM will be carry out to fulfill in both the environmental aspect and resource recycling. Thus, how to reduce the generation of SHM and recycle the valuable metal from SHM become the major subjects in the global world. In this article, in order to prospect the effective processing of SHM, the generation and processing of the sludge from plating wastewater, the research and development of valuable metal recycling technology and problems were summarized.

• Smart Structures and Systems
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• v.29 no.1
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• Smart Structures and Systems
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• v.31 no.2
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• pp.101-111
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• 2023

Structural health monitoring (SHM) covers a range of damage detection strategies for buildings. In real-time, SHM provides a basis for rapid decision making to optimise the speed and economic efficiency of post-event response. Previous work introduced an SHM method based on identifying structural nonlinear hysteretic parameters and their evolution from structural force-deformation hysteresis loops in real-time. This research extends and generalises this method to investigate the impact of a wide range of flag-shaped or pinching shape nonlinear hysteretic response and its impact on the SHM accuracy. A particular focus is plastic stiffness (K_p), where accurate identification of this parameter enables accurate identification of net and total plastic deformation and plastic energy dissipated, all of which are directly related to damage and infrequently assessed in SHM. A sensitivity study using a realistic seismic case study with known ground truth values investigates the impact of hysteresis loop shape, as well as added noise, on SHM accuracy using a suite of 20 ground motions from the PEER database. Monte Carlo analysis over 22,000 simulations with different hysteresis loops and added noise resulted in absolute percentage identification error (median, (IQR)) in K_p of 1.88% (0.79, 4.94)%. Errors were larger where five events (Earthquakes #1, 6, 9, 14) have very large errors over 100% for resulted K_p as an almost entirely linear response yielded only negligible plastic response, increasing identification error. The sensitivity analysis shows accuracy is reduces to within 3% when plastic drift is induced. This method shows clear potential to provide accurate, real-time metrics of non-linear stiffness and deformation to assist rapid damage assessment and decision making, utilising algorithms significantly simpler than previous non-linear structural model-based parameter identification SHM methods.

• Geophysics and Geophysical Exploration
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• v.27 no.2
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• pp.144-153
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• 2024

Recent research is increasingly focused on utilizing seismic waves for structure health monitoring (SHM). Specifically, seismic interferometry, a technique applied in geophysical surveys using ambient noise, is widely applied in SHM. This method involves analyzing the response of buildings to propagating seismic waves. This enables the estimation of changes in structural stiffness and the evaluation of the location and presence of damage. Analysis of seismic interferometry applied to SHM, along with case studies, indicates its highly effective application for assessing structural stability and monitoring building conditions. Seismic interferometry is thus recognized as an efficient approach for evaluating building integrity and damage detection in SHM and monitoring applications.