• Structural Monitoring and Maintenance
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• v.1 no.1
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• pp.89-110
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• 2014

Structural Health Monitoring (SHM) is an effective alternative to conventional inspections which are time-consuming and subjective. SHM can detect damage early and reduce maintenance cost and thereby help reduce the likelihood of catastrophic structural events to infrastructure such as bridges. After reviewing the Damage Index Method (DIM), an Iterative Damage Index Method (IDIM) is proposed to improve the accuracy of damage detection. These two damage detection techniques are compared based on damage on two structures, a simply supported beam and a pedestrian bridge. Compared to the traditional damage detection algorithm, the proposed IDIM is shown to be less arbitrary and more accurate.

• Smart Structures and Systems
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• v.4 no.6
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• pp.759-777
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• 2008

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

• Structural Monitoring and Maintenance
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• v.3 no.1
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• pp.51-69
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• 2016

Monitoring the performance and estimating the remaining useful life of aging civil infrastructure in the United States has been identified as a major objective in the civil engineering community. Structural health monitoring has emerged as a central tool to fulfill this objective. This paper presents a review of the major structural monitoring programs that have been recently implemented in the United States, focusing on the integrity and performance assessment of large-scale structural systems. Applications where response data from a monitoring program have been used to detect and correct structural deficiencies are highlighted. These applications include (but are not limited to): i) Post-earthquake damage assessment of buildings and bridges; ii) Monitoring of cables vibration in cable-stayed bridges; iii) Evaluation of the effectiveness of technologies for retrofit and seismic protection, such as base isolation systems; and iv) Structural damage assessment of bridges after impact loads resulting from ship collisions. These and many other applications show that a structural health monitoring program is a powerful tool for structural damage and condition assessment, that can be used as part of a comprehensive decision-making process about possible actions that can be undertaken in a large-scale civil infrastructure system after potentially damaging events.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.225-231
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• 2006

The purpose of this study is to develop a promising hybrid structural health monitoring system for structural joints. For this propose, the combined use of vibration-based techniques and electro-mechanical impedance technique is employed. For the verification of the proposed health monitoring scheme, a series of damage scenarios are designed to simulate various situations at which the connection joints can experience during their service life. The obtained experimental results, modal parameters and electro-magnetic impedance signatures, are carefully analyzed to recognize the connecting states and the target damage locations. From the analysis. it is shown that the proposed hybrid health monitoring system is successful for acquiring global and local damage information on the structural joints.

• Smart Structures and Systems
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• v.22 no.5
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• pp.587-609
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• 2018

There is greater significance in identifying the incipient damages in structures at the time of their initiation as timely rectification of these minor incipient cracks can save huge maintenance cost. However, the change in the global dynamic characteristics of a structure due to these subtle damages are insignificant enough to detect using the majority of the current damage diagnostic techniques. Keeping this in view, we propose a hybrid damage diagnostic technique for detection of minor incipient damages in the structures. In the proposed automated hybrid algorithm, the raw dynamic signatures obtained from the structure are decomposed to uni-modal signals and the dynamic signature are reconstructed by identifying and combining only the uni-modal signals altered by the minor incipient damage. We use these reconstructed signals for damage diagnostics using ARMAX model. Numerical simulation studies are carried out to investigate and evaluate the proposed hybrid damage diagnostic algorithm and their capability in identifying minor/incipient damage with noisy measurements. Finally, experimental studies on a beam are also presented to compliment the numerical simulations in order to demonstrate the practical application of the proposed algorithm.

• Journal of Wind Energy
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• v.9 no.4
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• pp.16-23
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• 2018

A damage detection method for the tripod support structure of offshore wind turbines is presented for structural health monitoring. A finite element model of a prototype tripod support structure is established and the modal properties are calculated. The degree and location of the damage are estimated based on the neural network technique using the changes of natural frequencies and mode shape due to the damage. The stress distribution occurring in the support structure is obtained by a dynamic analysis for the wind turbine system to select the output data of the neural network. The natural frequencies and mode shapes for 36 possible damage scenarios were used for the input data of the learned neural network for damage assessment. The estimated damages agreed reasonably well with the accurate ones. The presented method could be effectively applied for damage detection and structural health monitoring of various types of support structures of offshore wind turbines.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.550-557
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• 2004

In this paper, structural damage in PSC bridges is monitored by using model-based damage detection methods. First numerical experiments on the test structure are described. Dynamic responses of the test structures are obtained fur several damage scenarios. The change in natural frequency and the change in nude shape curvature are selected as features to represent the states of the structure. Next a damage localization algorithm from monitoring the changes in natural frequency is outlined. Also, the damage localization algorithm from monitoring the changes in nude shapes is outlined. Finally, the damage localization algorithms are used to predict damage in the test structure. The results of the analysis indicate that the model-based damage detection methods correctly predicted damage in the test structure.

• Smart Structures and Systems
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• v.6 no.2
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• pp.135-146
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• 2010

This paper presents the perspective of the Structural Mechanics program of the Air Force Office of Scientific Research (AFOSR) on the damage assessment of structures for the period 2006-2009 when the author was serving as Program Manager at AFOSR. It is found that damage assessment of structures plays a very important role in assuring the safety and operational readiness of US Air Force fleet. The current fleet has many aging aircraft, which poses a considerable challenge for the operators and maintainers. The nondestructive evaluation technology is rather mature and able to detect damage with considerable reliability during the periodic maintenance inspections. The emerging structural health monitoring methodology has great potential, because it will use on-board damage detection sensors and systems, will be able to offer on-demand structural health bulletins. Considerable fundamental and applied research is still needed to enable the development, implementation, and dissemination of structural health monitoring technology.

• Structural Monitoring and Maintenance
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• v.10 no.1
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• pp.63-86
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• 2023

Structural health monitoring and damage detection are essential for assessing, maintaining, and rehabilitating structures. Most of the existing damage detection approaches compare the current state structural response with the undamaged vibrational structural response, which is unsuitable for old and existing structures where undamaged vibrational responses are absent. One of the approaches for existing structures, numerical model updating/inverse modelling, available in the literature, is limited to numerical studies with high-end software. In this study, an attempt is made to study the effectiveness of the model updating technique, simplify modelling complexity, and economize its usability. The optimization-based detection problem is addressed by using programmable open-sourced code, OpenSees^® and a derivative-free optimization code, NOMAD^®. Modal analysis is used for damage identification of beam-like structures with several damage scenarios. The performance of the proposed methodology is validated both numerically and experimentally. The proposed method performs satisfactorily in identifying both locations and intensity of damage in structures.

• International Journal of Railway
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• v.7 no.4
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• pp.94-99
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• 2014

Urban railway systems are located under populated areas and are mostly constructed for underground structures which demand high standards of structural safety. However, the damage progression of underground structures is hard to evaluate and damaged underground structures may not effectively stand against successive earthquakes. This study attempts to examine initial damage-stage and to access structural damage condition of the ground structures using Earthquake Damage Monitoring (EDM) system. For actual underground structure, vulnerable damaged member of Ulchiro-3ga station is chosen by finite element analysis using applied artificial earthquake load, and then damage pattern and history of damaged members is obtained from measured acceleration data introduced unsupervised learning recognition. The result showed damage index obtained by damage scenario establishment using acceleration response of selected vulnerable members is useful. Initial damage state is detected for selected vulnerable member according to established damage scenario. Stiffness degrading ratio is increasing whereas the value of reliability interval is decreasing.