Issue Topics - updated

One recurring problem in reinforced concrete structures is hidden corrosion. Detection of hidden corrosion is difficult due to incomplete information that can be measured. To overcome this problem, inverse analysis can be employed. Inverse analysis uses incomplete information from field measurement, and employs optimization to infer further information. Previous research has demonstrated the inference capability of inverse analysis for hidden corrosion detection in reinforced concrete. However, question remains on the limit of incompleteness of measurement data. The aim of this study is to simulate and examine the limit of incomplete information that can be allowed for an effective inverse analysis. The inverse analysis of hidden corrosion in reinforced concrete is formulated as a PDE-constrained optimization. The objective function of the optimization is the distance function between measurement data and FEM simulation. The search space is the PDE boundary conditions configuration. A FEM simulation is used to simulate measurement data by half-cell potential mapping on a concrete block of 2 by 1 meters. To simulate incomplete information, the number of measurement points is varied from 2 to 20 points on the surface of the concrete block. Results show that 20 measurement points are needed to successfully infer hidden corrosion profile. However, when the number of measurement points are below this, the effectiveness of inverse analysis varies and does not seem to correlate to the amount of information (i.e., number of measurement points). Further study is needed if this is due to a fluke or choice of measurement point’s location.

This paper deals with an inverse problem for estimating the magnitude of the residual stress in a butt-welded plate from measured data. The welding residual stresses in the butt-welded plate are caused by inherent strain. Thus, the welding residual stress in the butt-welded plate can be calculated if the inherent strains are estimated from measurement data. In previous works, a method was proposed to estimate the inherent strains in the butt-welded plate using thermo-elastic boundary element inverse analysis. It was found in the paper that the evaluated value of the inherent strains in the butt-welded plate depended on the location or the number of measurement points in the butt-welded plate. The accuracy of the evaluated inherent strains was low when the location of the measurement point was far from the welding line and the number of measurement points was small. Thus, this paper uses Tikhonov's regularization to improve the accuracy of the evaluated values of the inherent strains in the butt-welded plate. Numerical simulations were conducted for an analysis model which was the same with that used in the previous paper. In the simulations, measurement errors were included in stresses at measurement points. The maximum value of the error was 4.9 (MPa). The results of the calculations showed that the inherent strains in the butt-welded plate were evaluated in high accuracy from a few measured data by using Tikhonov's regularization. The discrepancy principle was applied for estimating a proper value of the regularization parameter. It was found that Tikhonov's method with the discrepancy principle was useful in evaluating the residual stress in the butt-welded plate.

Traditional structural system identification and damage detection methods use vibration responses under single excitation. This paper presents an auto/cross-correlation function-based method using acceleration responses under multiple ambient white noise or impact excitations. The auto/cross-correlation functions are divided into two parts. One is associated with the structural parameters and the other with the energy of the excitation. These two parts are updated sequentially using a two-stage method. Numerical and experimental studies are conducted to demonstrate the accuracy and robustness of the proposed method. The effects of measurement noise and number of measurement points on the identification results are also studied.

This paper presents an efficient inverse analysis technique for the identification of an unknown boundary condition in a finite element method (FEM) on a small memory capacity machine such as a personal computer. The author has previously presented the FEM of inverse analysis introducing an inverse compliance matrix that uses an influence function into the stiffness matrix for the unknown boundary. Since the stiffness matrix of the FEM becomes singular, following two calculation processes are adopted: (1) a calculation process which obtains an approximate solution by decreasing the effect of the inverse compliance in the stiffness, and (2) an iterative calculation process computing a correct solution from the approximation using (1). First, a penalty function method is used to decrease the rank of a singular stiffness matrix in (1). Then, the number for computing LDU decomposed matrix becomes twice in comparison with fourth times in the previous paper. Next, an efficient calculation method is presented using an attack technique based on both accelerative moving lower and upper bounds in (2). Moreover, a zooming identification method is developed, owing to an improvement of node displacements on and near the edges. Therefore, this technique makes it possible to establish an efficient inverse analysis for the identification of the unknown boundary. Finally, the validity of the inverse analysis technique is confirmed from the numerical solution of a three-dimensional inverse finite element analysis model, as an example of an elastic structure with reinforced members under tension, carried out on a small memory capacity machine.

Structural damage identification is essentially an inverse problem. Ill-posedness is a common obstacle encountered in solving such an inverse problem, especially in the context of a sensitivity-based model updating for damage identification. Tikhonov regularization, also termed as ℓ2-norm regularization, is a common approach to handle the ill-posedness problem and yields an acceptable and smooth solution. Tikhonov regularization enjoys a more popular application as its explicit solution, computational efficiency, and convenience for implementation. However, as the ℓ2-norm term promotes smoothness, the solution is sometimes over smoothed, especially in the case that the sensor number is limited. On the other side, the solution of the inverse problem bears sparse properties because typically, only a small number of components of the structure are damaged in comparison with the whole structure. In this regard, this paper proposes an alternative way, sparse regularization, or specifically ℓ1-norm regularization, to handle the ill-posedness problem in response sensitivity-based damage identification. The motivation and implementation of sparse regularization are firstly introduced, and the differences with Tikhonov regularization are highlighted. Reweighting sparse regularization is adopted to enhance the sparsity in the solution. Simulation studies on a planar frame and a simply supported overhanging beam show that the sparse regularization exhibits certain superiority over Tikhonov regularization as less false-positive errors exist in damage identification results. The experimental result of the overhanging beam further demonstrates the effectiveness and superiorities of the sparse regularization in response sensitivity-based damage identification. Copyright © 2015 John Wiley & Sons, Ltd.

Wavelet packet energy spectrum is used to identify the damage of a large-scale steel platform using output-only data of structural dynamic response for the particularity of enduring reiterative impact loading. By way of removing the effects of environment factors to the results of damage identification, a method combining wavelet packet energy spectrum and factor analysis belonging to multivariate statistic methods is developed. At last the validity and veracity of the new method are validated by numerical simulation experiments. The results of simulation experiments show that, the damage of the structure can be identified exactly by the method, the damage extent of the structure can be identified qualitatively through the value of damage index, and the results of damage identification can't be affected by the sampling location.

This study presents a two-stage approach based on residual force vector and response sensitivity analysis for structural damage identification in axially functionally graded (AFG) beams. The local damage is simulated by a reduction in the elemental Young’s modulus of the beam. The residual force vector is used to find the suspicious damaged elements in the beam at first. Then, a hybrid objective function is established and a sensitivity-based model updating method is adopted to identify the perturbation of the stiffness parameter from the measured acceleration responses. Two numerical examples are investigated to illustrate the correctness and efficiency of the proposed method. The effects of measurement noise on the identification results are investigated. Studies in this paper indicate that the proposed method is efficient and robust for identifying damages in the axially functionally graded beams. The advantage of the present approach lies in that only a few number of acceleration measurements and the first several natural frequencies of the beam are needed in the identification.

This paper presents a two-step approach for structural damage identification in beam structure using the influence line and bird mating optimizer (BMO). Local damage is simulated as the reduction of the elemental Young’s modulus and mass of beam element. The technique for damage localization based on influence line and its derivatives before and after damage for beam structure was outlined. An objective function comprised of dynamic acceleration is utilized for BMO algorithm. The dynamic response data under external force is calculated by Newmark integration method. Numerical examples of a simply supported beam was investigated. Effect of measurement noise is studied. Studies in the paper indicate that the proposed method is efficient and robust for identifying damages in beam structures.

Optimizing location of damage within an enclosed area defined by an algorithm based on the Lamb wave response data

In this paper, a heating process control law with steam supply is designed for a fluid in a heat exchanger. The process is described by a linear hyperbolic equation of the first order with a nonlocal boundary condition with a time-delayed argument. The temperature of the supplied steam is found as a linear dependence on fluid temperature values at measurement points in the heat exchanger. Explicit formulas are obtained for the gradient of the objective functional of the control problem in the space of the feedback coefficients (parameters) of this dependence. A numerical scheme is developed for determining the feedback parameters based on these formulas. Finally, an algorithm is proposed for determining the rational (optimal) number of measurement points.

Nowadays, structural health monitoring has attracted much attention, due to the construction of important and complex structures and their safety. One branch of structural health monitoring is damage detection, estimating the location and extent of eventual damages in the structural systems. In this study, an efficient method is introduced to determine the location and extent of damage in the frame, beam and truss structures using time domain response and an optimization algorithm. First of all, the structural damage detection problem is formulated as a standard optimization problem. So that, the optimization objective function is defined by using the acceleration of damaged structures and analytical model acceleration. The acceleration is obtained using Newmark method. Damage is simulated by reducing the elasticity modulus of structural members. Then, damage problem converted to an optimization problem are solved by teaching–learning-based optimization (TLBO) algorithm therefore, the exact location and extent of damage in structure can be determined. In order to show the capabilities of the proposed approach for identifying structural damage, four illustrative test examples are considered with considering measurement noise effect. The results clearly show that the proposed method is as a powerful method to detect multiple damage in structures.

Measurement points supporting data high-speed communication in complex dc distribution networks are insufficient, meanwhile, fault characteristics are greatly affected by control strategies of power electronic equipment. These may cause failure of traditional fault location methods when applied to complex dc distribution networks. Thus, this paper proposes a novel dc pole-to-pole short-circuit fault location algorithm for complex dc distribution networks. First, according to the high frequency transient current loops, this paper constructs the high frequency impedance equivalent models of module multilevel converter (MMC) and dc/dc converter, which are not affected by the control strategies and offer stable impedance values during fault process. Then high frequency transient voltages of sparse measurement points are extracted by wavelet transform to form the node high frequency transient voltage equation. Finally, the node high frequency transient current sparse vector is solved to locate fault position by the node high frequency transient voltage equation combined with the Bayesian compressed sensing (BCS) theory. The proposed algorithm has low requirements on the number of measurement points and does not require data to be measured strictly synchronously. It is also not affected by converter’s control strategies and transition resistances. A 32-node dc distribution network is built in PSCAD/EMTDC and simulation results verify the accuracy of the proposed location algorithm.

In this study, a numerical simulation using inverse pipeline analysis is conducted for the localization and quantification of leak using minimum number of measurement points of a leaky pipeline. The friction coefficient and leak cross section area were also identified by window marching technique using only the pressure head at one end of the pipeline and the flow rate measurements at the other end of the pipeline. The Monte Carlo simulation approach is applied to identify the pipeline parameters by minimizing an objective function that represents the mismatch between the measured and numerically modeled pipeline variables. The simulation results are able to identify and localize the leak location as well as the friction coefficient and leak across section area. The effect of additive measurement noise on the leak localization accuracy is also investigated.

An inverse boundary analysis of surface radiation in an axisymmetric cylindrical enclosure has been conducted in this study. Net energy exchange method was used to calculate the radiative heat flux on each surface, and a hybrid genetic algorithm was adopted to minimize an objective function, which is expressed by sum of square errors between estimated and measured or desired heat fluxes on the design surface. We have examined the effects of the measurement error as well as the number of measurement points on the estimation accuracy. Furthermore, the effect of a variation in one boundary condition on the other boundary conditions was also investigated to get the same desired heat flux and temperature distribution on the design surface.

A novel domain propulsion and adaptive modified inversion method based on the least square error method and the precise integration finite element method is firstly proposed to identify the geometries of two-dimensional and three-dimensional models made of functional gradient materials. After introducing concept of virtual boundary, the inverse geometry problems can be solved only by the two processes, which are the domain propulsion process and the adaptive modified process. The first process is getting a better shape and position of virtual boundary through propulsion scheme. After that, the geometry can be identified by modifying the shape of virtual boundary and searching the isotherm or isothermal surface. To improve the ill-posedness of problems, the truncated singular value decomposition method and the coefficient expending scheme are adopted in inversing process. The results of some typical numerical examples show that the present method can obtain the great performance on both the inversing accuracy and the computing efficiency by discussing the influence of some factors such as the different approximation functions, the measurement errors, the number of measurement points and so on.