• Smart Structures and Systems
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• v.20 no.2
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• pp.207-217
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• 2017

Because of the inevitable uncertainties such as structural parameters, external excitations and measurement noises, the effects of uncertainties should be taken into consideration in structural damage detection. In this paper, two probabilistic structural damage detection approaches are proposed to account for the underlying uncertainties in structural parameters and external excitation. The first approach adopts the statistical moment-based structural damage detection (SMBDD) algorithm together with the sensitivity analysis of the damage vector to the uncertain parameters. The approach takes the advantage of the strength SMBDD, so it is robust to measurement noise. However, it requests the number of measured responses is not less than that of unknown structural parameters. To reduce the number of measurements requested by the SMBDD algorithm, another probabilistic structural damage detection approach is proposed. It is based on the integration of structural damage detection using temporal moments in each time segment of measured response time history with the sensitivity analysis of the damage vector to the uncertain parameters. In both approaches, probability distribution of damage vector is estimated from those of uncertain parameters based on stochastic finite element model updating and probabilistic propagation. By comparing the two probability distribution characteristics for the undamaged and damaged models, probability of damage existence and damage extent at structural element level can be detected. Some numerical examples are used to demonstrate the performances of the two proposed approaches, respectively.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.1
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• pp.14-26
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• 2014

This paper provides the prediction of ultimate longitudinal strengths of the hull girders of a very large crude carrier considering probabilistic damage extent due to collision and grounding accidents based on IMO Guidelines (2003). The probabilistic density functions of damage extent are expressed as a function of non-dimensional damage variables. The accumulated probabilistic levels of 10%, 30%, 50%, and 70% are taken into account for the estimation of damage extent. The ultimate strengths have been calculated using the in-house software called Ultimate Moment Analysis of Damaged Ships which is based on the progressive collapse method, with a new convergence criterion of force vector equilibrium. Damage indices are provided for several probable heeling angles from $0^{\circ}$ (sagging) to $180^{\circ}$ (hogging) due to collision- and grounding-induced structural failures and consequent flooding of compartments. This paper proves from the residual strength analyses that the second moment of area of a damage section can be a reliable index for the estimation of the residual ultimate strength. A simple polynomial formula is also proposed based on minimum residual ultimate strengths.

• Earthquakes and Structures
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• v.27 no.4
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• pp.303-315
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• 2024

This study proposes a comprehensive methodology for estimating accumulative damage of bridge structures under multiple seismic excitations, in the framework of site-specific probabilistic hazard analysis. Specifically, a typical earthquake-prone region in China is chosen to perform probabilistic seismic hazard analysis (PSHA) to find the mean annual rate (MAR) of ground motion intensity at a specific level, based on which, a mass of ground motion observations is selected to construct random earthquake sequences with various number of shocks. Then, nonlinear time history analysis is implemented on the finite element (FE) model of a RC girder bridge at the site of interest, to investigate structural responses under different earthquake sequences, and to develop predictive model for cumulative damage computation, in which, a scalar seismic intensity measure (IM) is adopted and its performance in damage prediction is discussed by an experimental column. Furthermore, a mathematic model is established to calculate occurrence probability of earthquakes with various number of shocks, based on PSHA and homogenous Poisson random process, and a modified cumulative damage indicator is proposed, accounting for probabilistic occurrence of various earthquake scenarios. At end, the applicability of the proposed methodology to main shock and aftershock scenarios is validated, and characteristics of damage accumulation under different multiple earthquake scenarios are discussed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.8 s.239
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• pp.1102-1108
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• 2005

The integrity of nuclear piping systems has to be maintained sufficiently all the times during operation. In order to maintain the integrity, reliable assessment procedures including fracture mechanics analysis, etc, are required. Up to now, the integrity assessment has been performed using conventional deterministic approach even though there are lots of uncertainties to hinder a rational evaluation. In this respect, probabilistic approach is considered as an appropriate method for piping system evaluation. The objectives of this paper are to develop a probabilistic assessment program using reliability index and simulation technique and to estimate the damage probability of wall-thinned pipes in secondary systems. The probabilistic assessment program consists of three evaluation modules which are first order reliability method, second order reliability method and Monte Carlo simulation method. The developed program has been applied to evaluate damage probabilities of wall-thinned pipes subjected to internal pressure, global bending moment and combined loading. The sensitivity analysis results as well as prototypal evaluation results showed a promising applicability of the probabilistic integrity assessment program.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.779-788
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• 2017

System identification and damage detection for structural health monitoring have received considerable attention. Various time domain analysis methodologies based on measured vibration data of structures have been proposed. Among them, recursive least-squares estimation of structural parameters which is also known as parametric Kalman filter (PKF) approach has been studied. However, the conventional PKF requires that all the external excitations (inputs) be available. On the other hand, structural uncertainties are inevitable for civil infrastructures, it is necessary to develop approaches for probabilistic damage detection of structures. In this paper, a parametric Kalman filter with unknown inputs (PKF-UI) is proposed for the simultaneous identification of structural parameters and the unmeasured external inputs. Analytical recursive formulations of the proposed PKF-UI are derived based on the conventional PKF. Two scenarios of linear observation equations and nonlinear observation equations are discussed, respectively. Such a straightforward derivation of PKF-UI is not available in the literature. Then, the proposed PKF-UI is utilized for probabilistic damage detection of structures by considering the uncertainties of structural parameters. Structural damage index and the damage probability are derived from the statistical values of the identified structural parameters of intact and damaged structure. Some numerical examples are used to validate the proposed method.

• Computers and Concrete
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• v.29 no.1
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• pp.1-14
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• 2022

Several aspects influence corrosive processes in reinforced concrete (RC) structures such as environmental conditions, structural geometry and mechanical properties. Since these aspects present large randomnesses, probabilistic models allow a more accurate description of the corrosive phenomena. Besides, the definition of limit states in the reliability assessment requires a proper mechanical model. In this context, this study proposes a straightforward methodology for the mechanical-probabilistic modelling of RC structures subjected to reinforcements' corrosion. An improved damage approach is proposed to define the limit states for the probabilistic modelling, considering three main degradation phenomena: concrete cracking, rebar yielding and rebar corrosion caused either by chloride or carbonation mechanisms. The stochastic analysis is evaluated by the Monte Carlo simulation method due to the computational efficiency of the Lumped Damage Model for Corrosion (LDMC). The proposed mechanical-probabilistic methodology is implemented in a computational framework and applied to the analysis of a simply supported RC beam and a 2D RC frame. Curves illustrate the probability of failure evolution over a service life of 50 years. Moreover, the proposed model allows drawing the probability of failure map and then identifying the critical failure path for progressive collapse analysis. Collapse path changes caused by the corrosion phenomena are observed.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.2
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• pp.124-131
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• 2012

This paper provides prediction of ultimate longitudinal strengths of hull girder of a VLCC considering probabilistic damage extents due to collision and grounding accidents based on IMO Guideline(2003). The probability density functions of damage extents are expressed as a function of nondimensional damage variables. The accumulated probability levels of 10%, 30%, 50%, and 70% are taken into account for the damage extent estimation. The ultimate strengths have been calculated using in-house software UMADS (Ultimate Moment Analysis of Damaged Ships) which is based on the progressive collapse method. Damage indices are provided for all heeling angles due to any possible flooding of compartments from $0^{\circ}$ to $180^{\circ}$ which represent from sagging to hogging conditions, respectively. The analysis results reveal that minimum damage indices show different values according to heeling angles and damage levels.

• Structural Engineering and Mechanics
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• v.17 no.3_4
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• pp.483-492
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• 2004

This paper presents an efficient algorithm for the estimation of damage location and severity in bridge structures using Probabilistic Neural Network (PNN). Generally, the Back Propagation Neural Network (BPNN)-based damage detection methods need a lot of training patterns for neural network learning process and the optimum architecture of a BPNN is selected by trial and error. In this paper, the PNN instead of the conventional BPNN is used as a pattern classifier. The modal properties of damaged structure are somewhat different from those of undamaged one. The basic idea of proposed algorithm is that the PNN classifies a test pattern which consists of the modal characteristics from damaged structure, how close it is to each training pattern which is composed of the modal characteristics from various structural damage cases. In this algorithm, two PNNs are sequentially used. The first PNN estimates the damage location using mode shape and the results of the first PNN are put into the second PNN for the damage severity estimation using natural frequency. The proposed damage assessment algorithm using the PNN is applied to a cable-stayed bridge to verify its applicability.

• Smart Structures and Systems
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• v.15 no.3
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• pp.699-715
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• 2015

A modal parameter based damage sensitive feature (DSF) is defined to mimic the relative change in any diagonal element of the stiffness matrix of a model of a structure. The damage assessment is performed in a statistical pattern recognition framework using empirical complementary cumulative distribution functions (ECCDFs) of the DSFs extracted from measured operational vibration response data. Methods are discussed to perform probabilistic structural health assessment with respect to the following questions: (a) "Is there a change in the current state of the structure compared to the baseline state?", (b) "Does the change indicate a localized stiffness reduction or increase?", with the latter representing a situation of retrofitting operations, and (c) "What is the severity of the change in a probabilistic sense?". To identify a range of normal structural variations due to environmental and operational conditions, lower and upper bound ECCDFs are used to define the baseline structural state. Such an approach attempts to decouple "non-damage" related variations from damage induced changes, and account for the unknown environmental/operational conditions of the current state. The damage assessment procedure is discussed using numerical simulations of ambient vibration testing of a bridge deck system, as well as shake table experimental data from a 4-story steel frame.

• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.4
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• pp.169-179
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• 2002

This paper presents an efficient algorithm for the estimation of damage location and severity in structure using Probabilistic Neural Network (PNN). Artificial neural network has been being used for damage assessment by many researchers, but there are still some barriers that must be overcome to improve its accuracy and efficiency. The major problems with the conventional neural network are the necessity of many training data for neural network learning and ambiguity in the relation of neural network architecture with convergence of solution. In this paper, PNN is used as a pattern classifier to overcome those problems in the conventional neural network. The basic idea of damage assessment algorithm proposed in this paper is that modal characteristics from a damaged structure are compared with the training patterns which represent the damage in specific element to determine how close it is to training patterns in terms of the probability from PNN. The training pattern that gives a maximum probability implies that the element used in producing the training pattern is considered as a damaged one. The proposed damage assessment algorithm using PNN is applied to a 2-span continuous beam model structure to verify the algorithm.