• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.04a
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• pp.309-316
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• 1998

In this paper, a newly derived formulation of a crack detection model is presented and its feasibility to detect cracks in structures is verified experimentally. To meet this objective, the followig approach is utilized. Firstly, the crack detection scheme which consists of the damage localization model and the crack detection model is formulated. Secondly, the feasibility and practicality of the complete procedure of the crack detection model is evaluated by locating and sizing cracks in clamped-clamped beams for which a f3w modal parameters were measured for sixteen uncracked and cracked states. Major results observed from the crack detection exercises include that far most damage cases, the predicted crack locations falls within very close to the inflicted locations of cracks in the test beam and the size of crack values estimated at the predicted locations are very close to the inflicted magnitudes.

• Computers and Concrete
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• v.20 no.6
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• pp.689-704
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• 2017

The paper presents experimental and numerical investigations of prefabricated composite structural building reinforced concrete slabs with the insulating material for a residential building construction. The building slabs were composed of concrete and expanded polystyrene. In experiments, the slabs in the full-scale 1:1 were subjected to vertical concentrated loads and failed along a diagonal shear crack. The experiments were numerically evaluated using the finite element method based on two different constitutive continuum models for concrete. First, an elasto-plastic model with the Drucker-Prager criterion defined in compression and with the Rankine criterion defined in tension was used. Second, a coupled elasto-plastic-damage formulation based on the strain equivalence hypothesis was used. In order to describe strain localization in concrete, both models were enhanced in the softening regime by a characteristic length of micro-structure by means of a non-local theory. Attention was paid to the formation of critical diagonal shear crack which was a failure precursor.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.77-93
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• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

• Smart Structures and Systems
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• v.24 no.5
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• pp.669-681
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• 2019

Bridge collapses may deliver a huge impact on our society in a very negative way. Out of many reasons why bridges collapse, poor maintenance is becoming a main contributing factor to many recent collapses. Furthermore, the aging of bridges is able to make the situation much worse. In order to prevent this unwanted event, it is indispensable to conduct continuous bridge monitoring and timely maintenance. Visual inspection is the most widely used method, but it is heavily dependent on the experience of the inspectors. It is also time-consuming, labor-intensive, costly, disruptive, and even unsafe for the inspectors. In order to address its limitations, in recent years increasing interests have been paid to the use of unmanned aerial vehicles (UAVs), which is expected to make the inspection process safer, faster and more cost-effective. In addition, it can cover the area where it is too hard to reach by inspectors. However, this strategy is still in a primitive stage because there are many things to be addressed for real implementation. In this paper, a typical procedure of bridge inspection using UAVs consisting of three phases (i.e., pre-inspection, inspection, and post-inspection phases) and the detailed tasks by phase are described. Also, three major challenges, which are related to a UAV's flight, image data acquisition, and damage identification, respectively, are identified from a practical perspective (e.g., localization of a UAV under the bridge, high-quality image capture, etc.) and their possible solutions are discussed by examining recently developed or currently developing techniques such as the graph-based localization algorithm, and the image quality assessment and enhancement strategy. In particular, deep learning based algorithms such as R-CNN and Mask R-CNN for classifying, localizing and quantifying several damage types (e.g., cracks, corrosion, spalling, efflorescence, etc.) in an automatic manner are discussed. This strategy is based on a huge amount of image data obtained from unmanned inspection equipment consisting of the UAV and imaging devices (vision and IR cameras).

• Smart Structures and Systems
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• v.19 no.2
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• pp.195-202
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• 2017

The aim of this paper is to develop a novel method to determine the severity of a damage in a thin plate. This paper presents a novel fault detection and diagnosis approach employing a new electromagnetic acoustic transducer, called EMAT, together with a complex signal processing method. The method consists in the recognition of a fault that exists within the structure, the fault location, i.e. the identification of the geometric position of damage, and the determining the significance of the damage, which indicates the importance or severity of the defect. The main scientific novelties presented in this paper is: to develop of a new type of electromagnetic acoustic transducer; to incorporate wavelet transforms for signal representation enhancements; to investigate multi-parametric analysis for noise identification and defect classification; to study attenuation curves properties for defect localization improvement; flaw sizing and location algorithm development.

• Computers and Concrete
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• v.25 no.1
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• pp.15-27
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• 2020

During the past decades, the application of acoustic emission techniques (AET) through the diagnosis and monitoring of the fracture process in materials has been attracting considerable attention. AET proved to be operative among the other non-destructive testing methods for various reasons including their practicality and cost-effectiveness. Concrete and rock structures often demand thorough and real-time assessment to predict and prevent their damage nucleation and evolution. This paper presents an overview of the work carried out on the use of AE as a monitoring technique to form a comprehensive insight into its potential application in brittle materials. Reported properties in this study are crack growth behavior, localization, damage evolution, dynamic character and structures monitoring. This literature review provides practicing engineers and researchers with the main AE procedures to follow when examining the possibility of failure in civil/resource structures that rely on brittle materials.

• Archives of Pharmacal Research
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• v.21 no.5
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• pp.496-502
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• 1998

The present study was carried out to determine the possible use of cTn-I in the cardiac myofibrillar architecture, as a potential target for in vivo radioimmunodetection of cardiac damage in a brain death pig model. Radioiodiantion of the anti-cTn-I 5F4 McAb was carried out by lactoperoxidase method. the percentage iodine incorporation achieved was 70-75%. The radioiodinated McAbs were purified on Sephadex G-25 column and characterised by Paper chromatography, Phast Gel electrophoresis and electroimmunoblotting. Radioiodinated anticTn-I 5F4 McAbs were employed alongside Pyrophosphate($Tc_{99m}$-PPi$) and $Thallium^{201}$ chloride($TI^{201}$) in 24 landrace pigs (brain-dead=18 & sham-operated=6). The percentage cardiac uptake of the radiolabelled antibody injected dose was significantly higher in the brain dead animals(0.196%) as compared to that of sham-operated animals (0.11%). Specific in vivo localization of radiolabelled McAbs in the infarcted cardiac tissue was confirmed by computer-aided reconstruction of 3-D images of the isolated heart. The preliminary results of the study revealed preferential uptake of radiolabelled antibody at the site of myocyte damage resulting from artificially induced brain death.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.168-177
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• 2008

Path finding is a key element in the navigation of a mobile robot. To find a path, robot should know their position exactly, since the position error exposes a robot to many dangerous conditions. It could make a robot move to a wrong direction so that it may have damage by collision by the surrounding obstacles. We propose a method obtaining an accurate robot position. The localization of a mobile robot in its working environment performs by using a vision system and Virtual Reality Modeling Language(VRML). The robot identifies landmarks located in the environment. An image processing and neural network pattern matching techniques have been applied to find location of the robot. After the self-positioning procedure, the 2-D scene of the vision is overlaid onto a VRML scene. This paper describes how to realize the self-positioning, and shows the overlay between the 2-D and VRML scenes. The suggested method defines a robot's path successfully. An experiment using the suggested algorithm apply to a mobile robot has been performed and the result shows a good path tracking.

• The Plant Pathology Journal
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• v.35 no.1
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• pp.19-31
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• 2019

Bud rot (BR) is the most devastating disease affecting oil palm (Elaeis guineensis) crops in Colombia. Its causal agent, Phytophthora palmivora, initiates the infection in immature oil palm leaflets producing necrotic lesions, followed by colonization of opportunistic necrotrophs, which increases disease damage. To improve the characterization of the disease, we transformed P. palmivora using Agrobacterium tumefaciens-mediated transformation (ATMT) to include the fluorescent proteins CFP-SKL (peroxisomal localization), eGFP and mRFP1 (cytoplasmic localization). The stability of some transformants was confirmed by Southern blot analysis and single zoospore cultures; additionally, virulence and in vitro growth were compared to the wild-type isolate to select transformants with the greatest resemblance to the WT isolate. GFP-tagged P. palmivora was useful to identify all of the infective structures that are commonly formed by hemibiotrophic oomycetes, including apoplastic colonization and haustorium formation. Finally, we detected cell death responses associated with immature oil palm tissues that showed reduced susceptibility to P. palmivora infection, indicating that these tissues could exhibit age-related resistance. The aim of this research is to improve the characterization of the initial disease stages and generate cell biology tools that may be useful for developing methodologies for early identification of oil palm materials resistant or susceptible to BR.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.159-160
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• 2019

It would be very important to localize people at risk as soon as possible in order to minimize the damage. Generally the infrastructure should be deployed additionally for indoor positioning system. In this paper, we proposed an indoor localization system for people at risk using the existing fire alarm networks. The system detects the signal of smart devices of people in danger immediately and let the main alarm controller ring all alarms in vessel and display the position. Thus, the proposed system can make the burden much less to deploy additional network and infrastructure.