• Smart Structures and Systems
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• v.29 no.1
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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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• v.5 no.6
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• pp.663-679
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• 2009

The use of Micro-Electro-Mechanical Systems (MEMS) accelerometers in geotechnical instrumentation is relatively new but on the rise. This paper describes a new MEMS-based system for in situ deformation and vibration monitoring. The system has been developed in an effort to combine recent advances in the miniaturization of sensors and electronics with an established wireless infrastructure for on-line geotechnical monitoring. The concept is based on triaxial MEMS accelerometer measurements of static acceleration (angles relative to gravity) and dynamic accelerations. The dynamic acceleration sensitivity range provides signals proportional to vibration during earthquakes or construction activities. This MEMS-based in-place inclinometer system utilizes the measurements to obtain three-dimensional (3D) ground acceleration and permanent deformation profiles up to a depth of one hundred meters. Each sensor array or group of arrays can be connected to a wireless earth station to enable real-time monitoring as well as remote sensor configuration. This paper provides a technical assessment of MEMS-based in-place inclinometer systems for geotechnical instrumentation applications by reviewing the sensor characteristics and providing small- and full-scale laboratory calibration tests. A description and validation of recorded field data from an instrumented unstable slope in California is also presented.

• Tunnel and Underground Space
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• v.29 no.6
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• pp.367-378
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• 2019

As the development of IT technology, the way to manage the underground construction site in real time using wireless network communication is being considered. Underground space is limited area compared to the ground site due to the enclosed communication environment and spatial characteristics. BMN (Bluetooth Mesh Networking) technology is a recently introduced wireless communication technology that bluetooth device acts as a repeater to form a networking mesh and monitor individual devices. This article examines the areas to be considered for analyzing the applicability of BMN technology to underground spaces, and the cases studies of wireless communication monitoring systems currently being developed. And the BMN technology which is being developed for the underground application is also introduced.

• Smart Structures and Systems
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• v.26 no.4
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• pp.435-449
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• 2020

To effectively extract the vast wind resource, offshore wind turbines are designed with large rotor and slender tower, which makes them vulnerable to external vibration sources such as wind and wave loads. Substantial research efforts have been devoted to mitigate the unwanted vibrations of offshore wind turbines to ensure their serviceability and safety in the normal working condition. However, most previous studies investigated the vibration control of wind turbines in one direction only, i.e., either the out-of-plane or in-plane direction. In reality, wind turbines inevitably vibrate in both directions when they are subjected to the external excitations. The studies on both the in-plane and out-of-plane vibration control of wind turbines are, however, scarce. In the present study, the NREL 5 MW wind turbine is taken as an example, a detailed three-dimensional (3D) Finite Element (FE) model of the wind turbine is developed in ABAQUS. To simultaneously control the in-plane and out-of-plane vibrations induced by the combined wind and wave loads, another carefully designed (i.e., tuned) spring and dashpot are added to the perpendicular direction of each Tuned Mass Damper (TMD) system that is used to control the vibrations of the tower and blades in one particular direction. With this simple modification, a bi-directional TMD system is formed and the vibrations in both the out-of-plane and in-plane directions are simultaneously suppressed. To examine the control effectiveness, the responses of the wind turbine without control, with separate TMD system and the proposed bi-directional TMD system are calculated and compared. Numerical results show that the bi-directional TMD system can simultaneously control the out-of-plane and in-plane vibrations of the wind turbine without changing too much of the conventional design of the control system. The bi-directional control system therefore could be a cost-effective solution to mitigate the bi-directional vibrations of offshore wind turbines.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.248-255
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• 2020

In this study, we analyze the current state of domestic food waste (FW) recycling and propose a management plan for greenhouse gas (GHG) emissions. First, the composting potential of the GW demonstrates considerable promise. In particular, the GW (phytoplankton, periphyton, macrophyte, etc.) as a third-generation biomass shows strong performance as a functional additive that mitigates the disadvantages associated with composting FW and improves the quality of the final composted product. Alternatively, the final product (e.g., soil ameliorant) can be used to produce bio-filters that are effective pollutant buffers, with high applicability for green infrastructure. The proposed ecological approaches create new opportunities for FW as a resource for the reduction of GHG emissions, and are expected to contribute to the establishment of effective net-zero carbon systems in the future.

• Journal of the Korean Geosynthetics Society
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• v.18 no.4
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• pp.107-114
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• 2019

In this paper, the P-wave multiple detection system for the fast and accurate earthquake early warning nearby the epicenter was developed. The developed systems were installed in five selected public buildings for the validation. During the monitoring, a magnitude 2.3 earthquake occurred in Pohang on 26 September 2019. P-wave initial detection algorithms were operated in three out of four systems installed in Pohang area and recorded as seismic events. At the nearest station, 5.5 km from the epicenter, P-wave signal was detected 1.2 seconds after the earthquake, and S-wave was reached 1.02 seconds after the P-wave reached, providing some alarm time. The maximum accelerations recorded in three different stations were 6.28 gal, 6.1 gal, and 5.3 gal, respectively. The alarm algorithm did not work, due to the high threshold of the maximum ground acceleration (25.1 gal) to operate it. If continuous monitoring and analysis are to be carried out in the future, the developed system could use a highly effective earthquake warning system suitable for the domestic situation.

• Geomechanics and Engineering
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• v.24 no.5
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• pp.431-441
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• 2021

Rock-socketed drilled shafts are widely used to transfer the heavy loads from the superstructure especially in mountainous area. Extensive research has been done on the behavior of rock-socketed drilled shafts under compressive load. However, little attention has been paid to uplift behavior of drilled shaft in rock, which govern the overall behavior of the foundation system. In this paper, a series of centrifuge tests have been performed to investigate the uplift response of rock-socketed drilled shafts. The pull-out tests of drilled shafts installed in layered rocks having various strengths were conducted. The load-displacement response, axial load distributions in the shaft and the unit skin friction distribution under pull-out loads were investigated. The effects of the strength of rock socket on the initial stiffness, ultimate capacity and mobilization of friction of the foundation, were also examined. The results indicated that characteristics of rock-socket has a significant influence on the uplift behavior of drilled shaft. Most of the applied uplift load were carried by socketed rock when the drilled shaft was installed in the sand over rock layer, whereas substantial load was carried by both upper and lower rock layers when the drilled shaft was completely socketed into layered rock. The pattern of mobilized shaft friction and point where the maximum unit shaft friction occurred were also found to be affected by the socket condition surrounding the drilled shaft.

• Smart Structures and Systems
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• v.24 no.4
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• pp.553-566
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• 2019

This study aimed to categorize pervious rubberized concrete into fresh and hardened concrete analyzing its durability, permeability and strength. During the globalization of modern life, growing population and industry rate have signified a sustainable approach to all aspects of modern life. In recent years, pervious concrete (porous concrete) has significantly substituted for pavements due to its mechanical and environmental properties. On the other hand, scrap rubber tire has been also contributed with several disposal challenges. Considering the huge amount of annually tire wastes tossing out, the conditions become worse. Pervious concrete (PC) gap has graded surface assisted with storm water management, recharging groundwater sources and alleviate water run-offs. The results have shown that the use of waste tires as aggregate built into pervious concrete has tremendously reduced the scrap tire wastes enhancing environmental compliance.

• 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.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.481-504
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• 2010

Wireless smart sensor networks (WSSNs) have been proposed by a number of researchers to evaluate the current condition of civil infrastructure, offering improved understanding of dynamic response through dense instrumentation. As focus moves from laboratory testing to full-scale implementation, the need for multi-hop communication to address issues associated with the large size of civil infrastructure and their limited radio power has become apparent. Multi-hop communication protocols allow sensors to cooperate to reliably deliver data between nodes outside of direct communication range. However, application specific requirements, such as high sampling rates, vast amounts of data to be collected, precise internodal synchronization, and reliable communication, are quite challenging to achieve with generic multi-hop communication protocols. This paper proposes two complementary reliable multi-hop communication solutions for monitoring of civil infrastructure. In the first approach, termed herein General Purpose Multi-hop (GPMH), the wide variety of communication patterns involved in structural health monitoring, particularly in decentralized implementations, are acknowledged to develop a flexible and adaptable any-to-any communication protocol. In the second approach, termed herein Single-Sink Multi-hop (SSMH), an efficient many-to-one protocol utilizing all available RF channels is designed to minimize the time required to collect the large amounts of data generated by dense arrays of sensor nodes. Both protocols adopt the Ad-hoc On-demand Distance Vector (AODV) routing protocol, which provides any-to-any routing and multi-cast capability, and supports a broad range of communication patterns. The proposed implementations refine the routing metric by considering the stability of links, exclude functionality unnecessary in mostly-static WSSNs, and integrate a reliable communication layer with the AODV protocol. These customizations have resulted in robust realizations of multi-hop reliable communication that meet the demands of structural health monitoring.