Comparative analysis of different weight matrices in subspace system identification for structural health monitoring

Subspace System Identification (SSI) is considered as one of the most reliable tools for identification of system parameters. Performance of a SSI scheme is considerably affected by the structure of the associated identification algorithm. Weight matrix is a variable in SSI that is used to reduce the dimensionality of the state-space equation. Generally one of the weight matrices of Principle Component (PC), Unweighted Principle Component (UPC) and Canonical Variate Analysis (CVA) are used in the structure of a SSI algorithm. An increasing number of studies in the field of structural health monitoring are using SSI for damage identification. However, studies that evaluate the performance of the weight matrices particularly in association with accuracy, noise resistance, and time complexity properties are very limited. In this study, the accuracy, noise-robustness, and time-efficiency of the weight matrices are compared using different qualitative and quantitative metrics. Three evaluation metrics of pole analysis, fit values and elapsed time are used in the assessment process. A numerical model of a mass-spring-dashpot and operational data is used in this research paper. It is observed that the principal components obtained using PC algorithms are more robust against noise uncertainty and give more stable results for the pole distribution. Furthermore, higher estimation accuracy is achieved using UPC algorithm. CVA had the worst performance for pole analysis and time efficiency analysis. The superior performance of the UPC algorithm in the elapsed time is attributed to using unit weight matrices. The obtained results demonstrated that the process of reducing dimensionality in CVA and PC has not enhanced the time efficiency but yield an improved modal identification in PC

Health monitoring of civil infrastructures by subspace system identification method: an overview

Structural health monitoring (SHM) is the main contributor of the future's smart city to deal with the need for safety, lower maintenance costs, and reliable condition assessment of structures. Among the algorithms used for SHM to identify the system parameters of structures, subspace system identification (SSI) is a reliable method in the time-domain that takes advantages of using extended observability matrices. Considerable numbers of studies have specifically concentrated on practical applications of SSI in recent years. To the best of author's knowledge, no study has been undertaken to review and investigate the application of SSI in the monitoring of civil engineering structures. This paper aims to review studies that have used the SSI algorithm for the damage identification and modal analysis of structures. The fundamental focus is on data-driven and covariance-driven SSI algorithms. In this review, we consider the subspace algorithm to resolve the problem of a real-world application for SHM. With regard to performance, a comparison between SSI and other methods is provided in order to investigate its advantages and disadvantages. The applied methods of SHM in civil engineering structures are categorized into three classes, from simple one-dimensional (1D) to very complex structures, and the detectability of the SSI for different damage scenarios are reported. Finally, the available software incorporating SSI as their system identification technique are investigated

Uncertainties consideration in empirical frequency response function data for damage identification based on artificial neural network

The modern application of frequency response function (FRF) with artificial neural networks (ANN) has become one of the leading methods in vibration-based damage detection approach. However, since full-size empirically obtained FRF data is used as ANN input, a broad composition ANN input layer series would occur. Consequently, principal component analysis (PCA) is adopted to compress the FRF data magnitude. Despite this, PCA alone is unable to select the important FRF data features effectively, due to the exceedingly FRF data size in addition with existing uncertainties. Therefore, this study proposed the merger of a non-probabilistic analysis and ANN approach with PCA by considering the uncertainties effect and the inefficiency of using empirical FRF data. The empirical FRF data is obtained from a steel truss bridge structure. The results show that the PoDE values above 95% are measured at the particular executed damage locations and the DMI values show the damage severity at the actual damage locations. Overall, the results show that the proposed method is capable in considering the uncertainties effect on the empirical FRF data for structural damage identification

Factors influencing natural frequencies in a prestressed concrete panel for damage detection

Modal parameters such as natural frequencies, mode shapes, and damping ratios are widely used as damage indicators in the field of vibration-based damage detection. These modal parameters can be easily obtained by conducting the modal test on the actual structure or from the finite element model. However, many publications are focusing only on the relationship between the modal parameters and the changes in structural properties for damage detection. There are a limited number of publications discussing on the factors that may affect the modal parameters for damage detection. Hence, this paper provides a study on the level of influence of several factors on the natural frequencies of a prestressed concrete panel. The factors that are considered in this study are the size of element used in the numerical model, the dimension of the structural element, and the prestressing force applied in the prestressed concrete panel. The natural frequencies computed from the finite element model are also verified with the actual measured natural frequencies that are determined through the modal test conducted in the laboratory

A comparative study of the data-driven stochastic subspace methods for health monitoring of structures: a bridge case study

Subspace system identification is a class of methods to estimate state-space model based on low rank characteristic of a system. State-space-based subspace system identification is the dominant subspace method for system identification in health monitoring of the civil structures. The weight matrices of canonical variate analysis (CVA), principle component (PC), and unweighted principle component (UPC), are used in stochastic subspace identification (SSI) to reduce the complexity and optimize the prediction in identification process. However, researches on evaluation and comparison of weight matrices' performance are very limited. This study provides a detailed analysis on the effect of different weight matrices on robustness, accuracy, and computation efficiency. Two case studies including a lumped mass system and the response dataset of the Alamosa Canyon Bridge are used in this study. The results demonstrated that UPC algorithm had better performance compared to two other algorithms. It can be concluded that though dimensionality reduction in PC and CVA lingered the computation time, it has yielded an improved modal identification in PC

Detection of concrete spalling using changes in modal flexibility

This paper presents a study in the effect of spalling to dynamic parameters such as natural frequencies and mode shapes. Numerical example of a slab is used as an example in this study. The slab will be modelled using ANSYS 11.0 and various types of spalling are imposed. The changes of vibration parameters are monitored and compared. To compare the sensitivity of modal parameters to spalling is determined using the flexibility method. Based on the results it is found that by incorporating mode shapes using flexibility method, damage location and severity can be obtained

Influence of natural climate curing treatment on corrosion activity of reinforced concrete

Objective: This paper explained the experimental investigation conducted on reinforced concrete specimens to ascertain the effect of natural climate curing treatment on the corrosion activity. Methods/Statistical Analysis: Concrete specimens were prepared and given different curing treatments for 28 days. Sodium chloride was added to the concrete mix to accelerate corrosion. Two sets of the specimen were moisture cured for 3 days, then, one set out of the two was exposed to the sheltered environment, and the other set to the unsheltered environment to give the concrete a natural climate curing treatment. The specimens were subjected to different exposure conditions after the curing treatments. The half-cell potential and the nominal corrosion density were measured to study the effect of the natural climate curing treatment. Findings: The results revealed variation of the reinforced concrete corrosion process due to the natural climate curing treatment. There was a rapid shift of the trend of the corrosion activity for the specimens that underwent natural climate curing treatment from the fifth month of exposure duration; the corrosion activity in the specimens became higher than the specimens that were cured normal for 28 days in water at the six months of the exposure. The finding was attributed to the high rate of temperature and rainfall fluctuation within the tropical region, which caused massive imbalance in the early stage strength development of the concrete. The high temperature cum rainfall fluctuation rate disturbed the bonding of the concrete matrix which affected the reaction of the concrete to corrosion of the reinforcing steel bar eventually. Application/Improvement: The findings could find application in reinforced concrete durability analysis

Steel fibre self-compacting concrete under biaxial loading

This study involves the investigation of the effects of steel fibre content volume on the biaxial stress behaviour of steel fibre self-compacting concrete (SFSCC) at different stress ratios. This study covers compression-compression, compression-tension, and tension-tension stress regions. The results are discussed in terms of the uniaxial and biaxial strength, stress-strain relationship, and failure mode of SFSCC specimens. In terms of strength, 1.0% fibre volume fractions showed the highest increment in biaxial compression and compression-tension, which were 55% and 84%, respectively, when compared to plain concrete. This improvement was due to the integration of steel fibre. In contrast with compression strength, biaxial tension strength decreased in comparison to uniaxial tensile strength. Additionally, based on the octahedral stress space, the failure criteria of concrete for each region were proposed in a quadratic polynomial equation, and the parameters were derived from a regression analysis

Non-probabilistic method to consider uncertainties in frequency response function for vibration-based damage detection using artificial neural network

Artificial neural network (ANN) has become a popular computational approach in the field of vibration-based damage detection, based on its ability to relate the nonlinear relationship between structural vibration characteristics and damage information. In the meantime, frequency response function (FRF) estimation has been proven effective as a dynamic parameter for damage detection due to its prevention of information leakage. In this regard, FRF is chosen as the input variable for ANN to detect structural damage in this study. However, the main concern in damage detection using FRF with ANN is the size of FRF data. A full-size FRF data will result in a wide composition range of the ANN input layer, thus affecting the iteration divergence in the network training process and resulting in the computational inefficiency. In most applications, principal component analysis (PCA) has been used to reduce the size of the FRF data before being fed to an ANN model. However, as the structures become more complex, the FRF data size also increases. The large size of FRF data may result in the PCA not being effective in selecting important information from the actual FRF data, leading to false damage detection. Moreover, the existence of uncertainties from modelling error and measurement error may also amplify the error in damage detection. Hence, this study proposes a combination of a non-probabilistic method with PCA to consider the problem of the existing uncertainties and the inefficiency of using FRF data in ANN-based damage detection. In this study, ANN is used to relate the FRF data to a damage feature. The input data for the network are the compressed real FRFs and the outputs are the elemental stiffness parameter (ESP). The compressed FRF data obtained from PCA provide a new damage index (DI) that is used as the input layer of the ANN. Based on the interval analysis method, the uncertainties in the new DI are considered to bind together to obtain the interval bound (lower and upper bounds) of the DI changes. The possibility of damage existence (PoDE) is designed to ascertain the relationship between the input and output parameters in the form of undamaged and damaged conditions. The verification conducted on a numerical model and a laboratory tested steel truss bridge model demonstrated that the proposed method is efficient in dealing with uncertainties using FRF for damage detection