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CONTENTS
Volume 1, Number 3, September 2014
 

Abstract
This paper presents a unique study of Structural Health Monitoring (SHM) for the maintenance decision making about a real life movable bridge. The mechanical components of movable bridges are maintained on a scheduled basis. However, it is desired to have a condition-based maintenance by taking advantage of SHM. The main objective is to track the operation of a gearbox and a rack-pinion/open gear assembly, which are critical parts of bascule type movable bridges. Maintenance needs that may lead to major damage to these components needs to be identified and diagnosed timely since an early detection of faults may help avoid unexpected bridge closures or costly repairs. The fault prediction of the gearbox and rack-pinion/open gear is carried out using two types of Artificial Neural Networks (ANNs): 1) Multi-Layer Perceptron Neural Networks (MLP-NNs) and 2) Fuzzy Neural Networks (FNNs). Monitoring data is collected during regular opening and closing of the bridge as well as during artificially induced reversible damage conditions. Several statistical parameters are extracted from the time-domain vibration signals as characteristic features to be fed to the ANNs for constructing the MLP-NNs and FNNs independently. The required training and testing sets are obtained by processing the acceleration data for both damaged and undamaged condition of the aforementioned mechanical components. The performances of the developed ANNs are first evaluated using unseen test sets. Second, the selected networks are used for long-term condition evaluation of the rack-pinion/open gear of the movable bridge. It is shown that the vibration monitoring data with selected statistical parameters and particular network architectures give successful results to predict the undamaged and damaged condition of the bridge. It is also observed that the MLP-NNs performed better than the FNNs in the presented case. The successful results indicate that ANNs are promising tools for maintenance monitoring of movable bridge components and it is also shown that the ANN results can be employed in simple approach for day-to-day operation and maintenance of movable bridges.

Key Words
monitoring; maintenance; movable bridges; artificial neural networks; anomaly detection

Address
Mustafa Gul:Department of Civil and Environmental Engineering, University of Alberta,9105 116th St. Edmonton, Alberta, Canada Edmonton, Alberta, Canada
Taha Dumlupinar and Necati Catbas:Department of Civil Environmental and Construction Engineering, University of Central Florida,12800 Pegasus Drive, Suite 211, Orlando, Florida FL, USA
Hiroshi Hattori:Department of Civil and Earth Resources Engineering, Kyoto University, Saikyo-ku, Kyoto City, Japan

Abstract
The effectiveness of a retrofitting method for concrete columns with particular weaknesses is experimentally evaluated and presented in this paper. Structural deficiencies namely the inadequacy of transverse reinforcement and short length of lap splices are very common in columns found in structures built prior to the 1960s and 1970s. Recent earthquakes worldwide have caused severe damages and collapses of these structures. Nevertheless, the importance of improving the load transfer capacity between the deficiently lap-spliced bars is usually underestimated during the strengthening procedures applied in old buildings, though critical for the safety of the residents\' lives. Thus, the seismic performance of the enhanced columns is frequently overestimated. The retrofitting approach presented herein involves reinforced concrete jacketing of the column sub-assemblages and welding of the lap-spliced bars to prevent the splice failure and conform to the provisions of modern design Codes. The cyclic lateral loading response of poorly confined original column specimens with insufficient lap splices and the seismic behavior of the retrofitted columns are compared. Test results clearly demonstrate that the retrofitting procedure followed is an effective way of significantly improving the seismic performance of substandard columns found in old buildings.

Key Words
reinforced concrete; columns; R/C jackets; welding; lap splice; reversed cycling loading; seismic retrofitting

Address
George I. Kalogeropoulos and Alexander G. Tsonos: Department of Civil Engineering, Aristotle University of Thessaloniki, GR-54-124 Thessaloniki, Greece

Abstract
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).

Key Words
aeronautic; communications; electrical power supply; structural health monitoring; sensor network

Address
Pedro M. Monje and Gerardo Aranguren:Electronic Design Group, Faculty of Engineering of Bilbao, University of the Basque Country, Bilbao, Spain

Abstract
Detection of fatigue cracks at an early stage of their development is important in structural health monitoring. The breathing of cracks in a structure generates higher harmonic components of the exciting frequency in the frequency spectrum. Previously, the residual operational deflection shape (R-ODS) method was successfully applied to beams with a single crack. The method is based on the ODSs at the exciting frequency and its higher harmonic components which consider both amplitude and phase information of responses to map the deflection pattern of structures. Although the R-ODS method shows the location of a single crack clearly, its identification for the location of multiple cracks in a structure is not always obvious. Therefore, an improvement to the R-ODS method is presented here to make the identification process distinct for the beams with multiple cracks. Numerical and experimental examples are utilised to investigate the effectiveness of the improved method.

Key Words
multiple-crack detection; crack breathing; dynamic characterisation; frequency spectrum; operational deflection shape

Address
Erfan Asnaashari and Jyoti K. Sinha:Dynamics Laboratory, School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Sackville Street, Manchester M13 9PL, UK

Abstract
In this study a new innovative method of earthquake-resistant strengthening of reinforced concrete structures is presented for the first time. Strengthening according to this new method consists of the construction of steel fiber ultra-high-strength concrete jackets without conventional reinforcement which is usually applied in the construction of conventional reinforced concrete jackets. An innovative solution is proposed also for the first time that ensures a satisfactory seismic performance of existing reinforced concrete structures, strengthened by using composite materials. The weak point of the use of such materials in repairing and strengthening of old R/C structures is the area of beam-column joints. According to the proposed solution, the joints can be strengthened with a steel fiber ultra-high-strength concrete jacket, while strengthening of columns can be achieved by using CFRPs. The experimental results showed that the performance of the subassemblage strengthened with the proposed mixed solution was much better than that of the subassemblage retrofitted completely with CFRPs.

Key Words
steel fiber ultra high-strength concrete; reinforced concrete jackets; fiber reinforced polymers;beam-column joints; columns; cyclic loads

Address
Alexander G. Tsonos: Department of Civil Engineering, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece

Abstract
This study has been motivated to evaluate the practicality of numerical simulation of impedance monitoring for damage detection in steel column connection. In order to achieve the objective, the following approaches are implemented. Firstly, the theory of electro-mechanical (E/M) impedance responses and impedance-based damage monitoring method are outlined. Secondly, the feasibility of numerical simulation of impedance monitoring is verified for several pre-published experimental examples on steel beams, cracked aluminum beams, and aluminum round plates. Undamaged and damaged steel and aluminum beams are simulated to compare to experimental impedance responses. An aluminum round plate with PZT patch in center is simulated to investigate sensitive range of impedance responses. Finally, numerical simulation of the impedance-based damage monitoring is performed for a steel column connection in which connection bolts are damaged. From the numerical simulation test, the applicability of the impedance-based monitoring to the target steel column connection can be evaluated.

Key Words
electro-mechanical impedance; PZT sensor; damage monitoring; numerical simulation; steelcolumn connection; bolted connection

Address
Duc-Duy Ho and Thanh-Mong Ngo: Faculty of Civil Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam
Jeong-Tae Kim: Department of Ocean Engineering, Pukyong National University, Busan 608-737, Korea


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