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Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data

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A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 32229

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Special Issue Editors

Dr. Zhiming Zhang

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Guest Editor

Research and Modeling Group, Verisk Extreme Event Solutions, Boston, MA, USA
Interests: structural health monitoring (SHM); structural damage identification; structural dynamics; smart sensing; physics-guided machine learning

Dr. Mingming Song

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Guest Editor

Department of Civil and Environmental Engineering, Tongji University, Shanghai, China
Interests: structural health monitoring; Bayesian inference; Kalman filtering; system identification; uncertainty quantification

Dr. Qipei (Gavin) Mei

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Guest Editor

Civil and Environmental Engineering Department, Faculty of Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: sensing technology; smart infrastructure; construction automation; artificial intelligence; genera-tive design
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Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM) plays an important role in improving the safety and resilience of important structures and infrastructures by identifying structural conditions and evaluating potential structural damage or deficiencies in real time. In general, structural identification and damage evaluation methods can be broadly classified into physical model-based approaches and data-driven approaches. Model-based methods require establishing a physics-based numerical model (such as an finitie element (FE) model) and updating the model using measured structural responses. In contrast, data-driven approaches are entirely based on collected data from the monitored structure; structural damage is evaluated by statistical learning and pattern recognition. Previous studies show that either model-based or data-driven approaches have their respective merits and shortcomings. Recent studies investigated integrating physics-based models with data for improved identification results via physics-guided machine learning, physics-informed neural networks, digital twinning, hybrid modeling, etc. The aim of this Special Issue is to provide a platform for researchers and stakeholders to present their latest research and practices in structural health monitoring, especially those encouraging the integration of physics-based models with data in structural identification and damage evaluation. High-quality research articles and reviews are welcome. Papers are solicited in, but not limited to, the following and related topics:

Deterministic/stochastic FE model updating;
Machine learning and deep learning for SHM;
Physics-informed machine/deep learning for structural damage detection;
Modeling of structural systems via physics-informed machine/deep learning;
Integration of physics-based and data-science methods for fault diagnosis and failure prognosis;
Hybrid modeling for structural identification and damage detection;
Implementation of digital twin technology for strucutral identfication and simulation;
Structural identificaiton and simulation by data assimilation;
Uncertainty quantification in structural identification and damage evaluation.

Dr. Zhiming Zhang
Dr. Mingming Song
Dr. Qipei (Gavin) Mei
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

structural health monitoring (SHM)
structural damage detection
FE model updating
data-driven SHM
machine learning and pattern recognition
physics-guided machine learning
data assimilation
uncertainty quantification

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Published Papers (13 papers)

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Research

23 pages, 5491 KiB

Open AccessArticle

Bayesian Model-Updating Implementation in a Five-Story Building

by Oscar D. Hurtado, Albert R. Ortiz, Daniel Gomez and Rodrigo Astroza

Buildings 2023, 13(6), 1568; https://doi.org/10.3390/buildings13061568 - 20 Jun 2023

Cited by 2 | Viewed by 2223

Abstract

Simplifications and theoretical assumptions are usually incorporated into the numerical modeling of structures. However, these assumptions may reduce the accuracy of the simulation results. This problem has led to the development of model-updating techniques to minimize the error between the experimental response and the modeled structure by updating its parameters based on the observed data. Structural numerical models are typically constructed using a deterministic approach, whereby a single best-estimated value of each structural parameter is obtained. However, structural models are often complex and involve many uncertain variables, where a unique solution that captures all the variability is not possible. Updating techniques using Bayesian Inference (BI) have been developed to quantify parametric uncertainty in analytical models. This paper presents the implementation of the BI in the parametric updating of a five-story building model and the quantification of its associated uncertainty. The Bayesian framework is implemented to update the model parameters and calculate the covariance matrix of the output parameters based on the experimental information provided by modal frequencies and mode shapes. The main advantage of this approach is that the uncertainty in the experimental data is considered by defining the likelihood function as a multivariate normal distribution, leading to a better representation of the actual building behavior. The results showed that this Bayesian model-updating approach effectively allows a statistically rigorous update of the model parameters, characterizing the uncertainty and increasing confidence in the model’s predictions, which is particularly useful in engineering applications where model accuracy is critical. Full article

(This article belongs to the Special Issue Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data)

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15 pages, 2927 KiB

Open AccessArticle

Bayesian RC-Frame Finite Element Model Updating and Damage Estimation Using Nested Sampling with Nonlinear Time History

by Kunyang Wang, Yukihide Kajita and Yaoxin Yang

Buildings 2023, 13(5), 1281; https://doi.org/10.3390/buildings13051281 - 14 May 2023

Cited by 1 | Viewed by 1512

Abstract

This paper proposes a Bayesian RC-frame finite element model updating (FEMU) and damage state estimation approach using the nonlinear acceleration time history based on nested sampling. Numerical RC-frame finite element model (FEM) parameters are selected through nested sampling, and their probability density is estimated using nonlinear time history. In the first step, we estimate the error standard deviation and select the FEM parameters that are required to be updated by FEMU. In the second step, we estimate the probability density of the selected parameters and realize the FEMU through the resampling method and kernel density estimation (KDE). Additionally, we propose a damage state estimate approach, which is a derivative method of the FEMU sample. The numerical results demonstrate that the proposed approach is reliable for the Bayesian FEMU and damage state estimation using nonlinear time history. Full article

(This article belongs to the Special Issue Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data)

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22 pages, 8022 KiB

Open AccessArticle

Long Short-Term Memory Network for Predicting Wind-Induced Vibration Response of Lightning Rod Structures

by Guifeng Zhao, Kaifeng Xing, Yang Wang, Hui Qian and Meng Zhang

Buildings 2023, 13(5), 1256; https://doi.org/10.3390/buildings13051256 - 10 May 2023

Cited by 1 | Viewed by 1554

Abstract

Lightning rod structures are susceptible to wind loads due to their high slenderness ratio, high flexibility, and light weight. The wind-induced dynamic response of a lightning rod is critical for structural safety and reliability. The traditional methods for this response, including observation and simulation, focus on structural health monitoring (SHM), wind tunnel tests (WTTs), or fluid–structure interaction (FSI) simulations. However, all these approaches require considerable financial or computational investment. Additionally, problems such as data loss or data anomalies in the sensor monitoring process often occur during SHM or WTTs. This paper proposes an algorithm based on a long short-term memory (LSTM) network to predict the wind-induced dynamic response and to solve the problem of data link fracture caused by abnormal sensor data transmission or wind-induced damage to lightning rod structures under different wind speeds. The effectiveness and applicability of the proposed framework are demonstrated using actual monitoring data. Root-mean-squared error (RMSE), determination of coefficient (R²), variance accounted for (VAF), and the refined Willmott index (RWI) are employed as performance assessment indices for the proposed network model. At the same time, the random forest algorithm is adopted to analyze the correlation between the data of the different measurement points on the lightning rod structure. The results show that the LSTM method proposed in this paper has a high accuracy for the prediction of “missing” strain data during lightning rod strain monitoring under wind speeds of 15.81~31.62 m/s. Even under the extreme wind speed of 31.62 m/s, the values of RMSE, MAE, R², RWI and VAF are 0.24053, 0.18213, 0.94539, 0.88172 and 0.94444, respectively, which are within the acceptable range. Using the data feature importance analysis function, it is found that the predicted strain data of the measurement point on the top part of the lightning rod structure are closely related to the test strain data of the two adjacent sections of the structure, and the effect of the test strain data of the measurement points that are far from the predicted measurement point can be ignored. Full article

(This article belongs to the Special Issue Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data)

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22 pages, 43451 KiB

Open AccessArticle

An Analytical Algorithm for Determining Optimal Thin-Walled Hollow Pier Configuration with Sunlight Temperature Differences

by Lipeng An, Dejian Li, Peng Yuan and Peng Chen

Buildings 2023, 13(5), 1208; https://doi.org/10.3390/buildings13051208 - 2 May 2023

Cited by 1 | Viewed by 1804

Abstract

Formulas for computing the line shape of a thin-walled hollow pier body based on structural characteristics and measured sunlight temperature difference are derived using an analytical algorithm. In a case study of the No. 5 pier of a newly constructed continuous beam bridge on a mountainous expressway of Guizhou Province in China, the pier top’s displacement calculated by the analytical algorithm, currently accepted code, and a FEM program were each compared to its measured values. Furthermore, the effects of sunlight temperature difference, pier height, and wall thickness on the line shape of the pier body were explored, and the results show that the calculation values from these formulas were closer to the measured values than the currently accepted code, with a maximum error of 0.507 mm, demonstrating that the formulas have a more dependable result, higher precision, and more specific applicability. Thus, the algorithm provides a better method for the line shape calculation and construction control of thin-walled hollow piers because it can accurately account for sunlight temperature differences and pier height. Full article

(This article belongs to the Special Issue Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data)

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29 pages, 21409 KiB

Open AccessArticle

Novel Physics-Informed Artificial Neural Network Architectures for System and Input Identification of Structural Dynamics PDEs

by Sarvin Moradi, Burak Duran, Saeed Eftekhar Azam and Massood Mofid

Buildings 2023, 13(3), 650; https://doi.org/10.3390/buildings13030650 - 28 Feb 2023

Cited by 20 | Viewed by 4919

Abstract

Herein, two novel Physics Informed Neural Network (PINN) architectures are proposed for output-only system identification and input estimation of dynamic systems. Using merely sparse output-only measurements, the proposed PINNs architectures furnish a novel approach to input, state, and parameter estimation of linear and nonlinear systems with multiple degrees of freedom. These architectures are comprised of parallel and sequential PINNs that act upon a set of ordinary differential equations (ODEs) obtained from spatial discretization of the partial differential equation (PDE). The performance of this framework for dynamic system identification and input estimation was ascertained by extensive numerical experiments on linear and nonlinear systems. The advantage of the proposed approach, when compared with system identification, lies in its computational efficiency. When compared with traditional Artificial Neural Networks (ANNs), this approach requires substantially smaller training data and does not suffer from generalizability issues. In this regard, the states, inputs, and parameters of dynamic state-space equations of motion were estimated using simulated experiments with “noisy” data. The proposed framework for PINN showed excellent great generalizability for various types of applications. Furthermore, it was found that the proposed architectures significantly outperformed ANNs in generalizability and estimation accuracy. Full article

(This article belongs to the Special Issue Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data)

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27 pages, 6657 KiB

Open AccessArticle

A Two-Step FE Model Updating Approach for System and Damage Identification of Prestressed Bridge Girders

by Niloofar Malekghaini, Farid Ghahari, Hamed Ebrahimian, Matthew Bowers, Eric Ahlberg and Ertugrul Taciroglu

Buildings 2023, 13(2), 420; https://doi.org/10.3390/buildings13020420 - 2 Feb 2023

Cited by 5 | Viewed by 2098

Abstract

This study presents a two-step FE model updating approach for health monitoring and damage identification of prestressed concrete girder bridges. To reduce the effects of modeling error in the model updating process, in the first step, modal-based model updating is used to estimate linear model parameters mainly related to the stiffness of boundary conditions and material properties. In the second step, a time-domain model updating is carried out using acceleration data to refine parameters accounting for the nonlinear response behavior of the bridge. In this step, boundary conditions are fixed at their final estimates using modal-based model updating. To prevent the convergence of updating algorithm to local solutions, the initial estimates for nonlinear material properties are selected based on the first-step model updating results. To validate the applicability of the two-step FE model updating approach, a series of forced-vibration experiments are designed and carried out on a pair of full-scale decommissioned and deteriorated prestressed bridge I-girders. In the first step, parameters related to boundary conditions, including stiffness of supports and coupling beams, as well as material properties, including initial stiffness of concrete material, are estimated. In the second step, concrete compressive strength and damping properties are updated. The final estimates of the concrete compressive strength are used to infer the extent of damage in the girders. The obtained results agree with the literature regarding the extent of reduction in concrete compressive strength in deteriorated concrete structures. Full article

(This article belongs to the Special Issue Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data)

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18 pages, 4068 KiB

Open AccessArticle

A New Drive-by Method for Bridge Damage Inspection Based on Characteristic Wavelet Coefficient

by Tingpeng Zhang, Jin Zhu, Ziluo Xiong, Kaifeng Zheng and Mengxue Wu

Buildings 2023, 13(2), 397; https://doi.org/10.3390/buildings13020397 - 1 Feb 2023

Cited by 9 | Viewed by 2416

Abstract

The drive-by method has become a popular indirect approach for bridge damage inspection (BDI) because of its simplicity in deployment by evaluating the bridge health status solely via the vehicle dynamic response. Derived from the vehicle dynamic response, the recent proposed contact-point response involves no vibration signal with the vehicle frequency, bearing great potential for drive-by BDI. However, an appropriate methodology for the application of contact-point response in drive-by BDI remains lacking. The present study proposes a novel drive-by method, in which a new damage factor index, i.e., the characteristic wavelet coefficient (CWC), is established for bridge damage identification in an efficient and accurate manner. The CWC is obtained by analyzing the contact-point response via the continuous wavelet transform (CWT) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) techniques. CEEMDAN is introduced to overcome the issue of modal aliasing and pseudo-frequency. First, the general framework of the proposed drive-by BDI method is introduced. Then, a demonstration case study is carried out to examine the effectiveness of the proposed method. Subsequently, a parametric study is carried out to explore the effects of several parameters on the performance of BDI including the scale factor, vehicle speed, environmental noise, and boundary effect. The results indicate that the proposed drive-by BDI method can better eliminate the mode mixing and pseudo-frequency problems during the extraction of the CWC, compared with the traditional ensemble empirical mode decomposition method. The extracted CWC curve is smooth, convenient for damage inspection, and has strong anti-noise performance. After adding white noise with a signal-to-noise ratio of 20, a bridge girder with a damage severity of 20% can be identified successfully. In addition, the selection of the scale factor is critical for bridge damage inspection based on the extracted CWC. The effective scale factor of the CWC extracted using the proposed method has a wide range, which improves the inspection efficiency. Finally, a low vehicle speed is beneficial to alleviate the adverse effect of the boundary effect on the damage inspection of bridge girder ends. Full article

(This article belongs to the Special Issue Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data)

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16 pages, 3379 KiB

Open AccessArticle

Automated Detection for Concrete Surface Cracks Based on Deeplabv3+ BDF

by Yonggang Shen, Zhenwei Yu, Chunsheng Li, Chao Zhao and Zhilin Sun

Buildings 2023, 13(1), 118; https://doi.org/10.3390/buildings13010118 - 3 Jan 2023

Cited by 10 | Viewed by 3062

Abstract

Concrete cracks have always been the focus of research because of the serious damage they cause to structures. With the updating of hardware and algorithms, the detection of concrete structure surface cracks based on computer vision has received extensive attention. This paper proposes an improved algorithm based on the open-source model Deeplabv3+ and names it Deeplabv3+ BDF according to the optimization strategy used. Deeplabv3+ BDF first replaces the original backbone Xception with MobileNetv2 and further replaces all standard convolutions with depthwise separable convolutions (DSC) to achieve a light weight. The feature map of a shallow convolution layer is additionally fused to improve the detail segmentation effect. A new strategy is proposed, which is different from the two-stage training. The model training is carried out in the order of transfer learning, coarse-annotation training and fine-annotation training. The comparative test results show that Deeplabv3+ BDF showed good performance in the validation set and achieved the highest mIoU and detection efficiency, reaching real-time and accurate detection. Full article

(This article belongs to the Special Issue Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data)

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29 pages, 6740 KiB

Open AccessArticle

Estimation of Soil–Structure Model Parameters for the Millikan Library Building Using a Sequential Bayesian Finite Element Model Updating Technique

by Hamed Ebrahimian, Abdelrahman Taha, Farid Ghahari, Domniki Asimaki and Ertugrul Taciroglu

Buildings 2023, 13(1), 28; https://doi.org/10.3390/buildings13010028 - 22 Dec 2022

Cited by 5 | Viewed by 2176

Abstract

We present a finite element model updating technique for soil–structure system identification of the Millikan Library building using the seismic data recorded during the 2002 Yorba Linda earthquake. A detailed finite element (FE) model of the Millikan Library building is developed in OpenSees and updated using a sequential Bayesian estimation approach for joint parameter and input identification. A two-step system identification approach is devised. First, the fixed-base structural model is updated to estimate the structural model parameters (including effective elastic modulus of structural components, distributed floor mass, and Rayleigh damping parameters) and some uncertain components of the foundation-level motion. Then, the identified structural model is used for soil–structure model updating wherein the Rayleigh damping parameters, the stiffness and viscosity of the soil subsystem (modeled using a substructure approach), and the foundation input motions (FIMs) are estimated. The identified model parameters are compared with state-of-practice recommendations. While a specific application is made for the Millikan Library, the present work offers a framework for integrating large-scale FE models with measurement data for model inversion. By utilizing this framework for different civil structures and earthquake records, key structural model parameters can be estimated from the real-world recorded data, which can subsequently be used for assessing and improving, as necessary, state-of-the-art seismic analysis and structural modeling techniques. This paper presents an effort towards using real-world measurements for large-scale FE model updating in the soil and structure, uniform soil time domain for joint parameter and input estimation, and thus paves the way for future applications in system identification, health monitoring, and diagnosis of civil structures. Full article

(This article belongs to the Special Issue Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data)

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15 pages, 1337 KiB

Open AccessArticle

Numerical Study on Elastic Parameter Identification of Large-Span Steel Truss Structures Based on Strain Test Data

by Yuxin Zhang, Ao Zhou, Helong Xu and Hexin Zhang

Buildings 2022, 12(11), 1861; https://doi.org/10.3390/buildings12111861 - 3 Nov 2022

Cited by 1 | Viewed by 1510

Abstract

Large-span steel trusses are widely used in public buildings such as large-span factory buildings, exhibition halls, gymnasiums, and bridges because of their fast construction speed and easy industrial manufacturing. Due to construction errors and environmental factors, the material properties may change during their service life, and it is an important prerequisite for the structural safety assessment to identify the true material parameters of the structure. Among the many parameters, the elastic modulus is one that has the greatest impact on the accuracy of structural safety analysis. In this paper, a mathematical analysis model of elastic modulus identification was constructed, based on the strain test data and the improved gradient regularization method. The relationship between the strain test data and elastic moduli was established. A common finite element program based on the method was developed to identify the elastic modulus. A series of numerical simulations was carried out on a 53-element steel truss model to study the availability and numerical stability of the method. The effects of different initial values, numbers of strain tests, and locations of the strain test as well as the number of unknown parameters on the identification results were studied. The results showed that the proposed method had very high accuracy and computational efficiency. For the case of 53 unknown parameters without considering the test error, the identification accuracy could reach a 1 × 10⁻¹⁰ order of magnitude after only several iterations. This paper provides an effective solution to obtain the actual values of the elastic modulus of steel truss structures in practical engineering. Full article

(This article belongs to the Special Issue Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data)

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19 pages, 3059 KiB

Open AccessFeature PaperArticle

Deep Learning-Enriched Stress Level Identification of Pretensioned Rods via Guided Wave Approaches

by Zi Zhang, Fujian Tang, Qi Cao, Hong Pan, Xingyu Wang and Zhibin Lin

Buildings 2022, 12(11), 1772; https://doi.org/10.3390/buildings12111772 - 22 Oct 2022

Cited by 5 | Viewed by 2179

Abstract

By introducing pre-compression/inverse moment through prestressing tendons or rods, prestressed concrete (PC) structures could overcome conventional concrete weakness in tension, and thus, these tendons or rods are widely accepted in a variety of large-scale, long-span structures. Unfortunately, prestressing tendons or rods embedded in concrete are vulnerable to degradation due to corrosion. These embedded members are mostly inaccessible for visual or direct destructive assessments, posing challenges in determining the prestressing level and any corrosion-induced damage. As such, ultrasonic guided waves, as one of the non-destructive examination methods, could provide a solution to monitor and assess the health state of embedded prestressing tendons or rods. The complexity of the guided wave propagation and scattering in nature, as well as high variances stemming from the structural uncertainty and noise interference PC structures may experience under complicated operational and harsh environmental conditions, often make traditional physics-based methods invalid. Alternatively, the emerging machine learning approaches have potential for processing the guided wave signals with better capability of decoding structural uncertainty and noise. Therefore, this study aimed to tackle stress level prediction and the rod embedded conditions of prestressed rods in PC structures through guided waves. A deep learning approach, convolutional neural network (CNN), was used to process the guided wave dataset. CNN-based prestress level prediction and embedding condition identification of rods were established by the ultrasonic guided wave technique. A total of fifteen scenarios were designed to address the effectiveness of the stress level prediction under different noise levels and grout materials. The results demonstrate that the deep learning approaches exhibited high accuracy for prestressing level prediction under structural uncertainty due to the varying surrounding grout materials. With different grout materials, accuracy could reach up to 100% under the noise level of 90 dB, and still maintain the acceptable range of 75% when the noise level was as high as 70 dB. Moreover, the t-distributed stochastic neighbor embedding technology was utilized to visualize the feature maps obtained by the CNN and illustrated the correlation among different categories. The results also revealed that the proposed CNN model exhibited robustness with high accuracy for processing the data even under high noise interference. Full article

(This article belongs to the Special Issue Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data)

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14 pages, 3119 KiB

Open AccessArticle

A Joint State-Parameter Identification Algorithm of a Structure with Non-Diagonal Mass Matrix Based on UKF with Unknown Mass

by Shiyu Wang and Ying Lei

Buildings 2022, 12(6), 826; https://doi.org/10.3390/buildings12060826 - 14 Jun 2022

Cited by 3 | Viewed by 1936

Abstract

Inaccurate mass estimates have been recognized as an important source of uncertainty in structural identification, especially for large-scale structures with old ages. Over the past decades, some identification algorithms for structural states and unknown parameters, including unknown mass, have been proposed by researchers. However, most of these identification algorithms are based on the simplified mechanical model of chain-like structures. For a chain-like structure, the mass matrix and its inverse matrix are diagonal matrices, which simplify the difficulty of identifying the structure with unknown mass. However, a structure with a non-diagonal mass matrix is not of such a simple characteristic. In this paper, an online joint state-parameter identification algorithm based on an Unscented Kalman filter (UKF) is proposed for a structure with a non-diagonal mass matrix under unknown mass using only partial acceleration measurements. The effectiveness of the proposed algorithm is verified by numerical examples of a beam excited by wide-band white noise excitation and a two-story one-span plane frame structure excited by filtered white noise excitation generated according to the Kanai–Tajimi power spectrum. The identification results show that the proposed algorithm can effectively identify the structural state, unknown stiffness, damping and mass parameters of the structures. Full article

(This article belongs to the Special Issue Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data)

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15 pages, 2379 KiB

Open AccessArticle

Swarm Intelligent Optimization Conjunction with Kriging Model for Bridge Structure Finite Element Model Updating

by Jie Wu, Fan Cheng, Chao Zou, Rongtang Zhang, Cong Li, Shiping Huang and Yu Zhou

Buildings 2022, 12(5), 504; https://doi.org/10.3390/buildings12050504 - 19 Apr 2022

Cited by 8 | Viewed by 2302

Abstract

For the simple bridge structure, the finite element model established by drawing and elastic mechanics method is accurate. However, when faced with large and complex long-span bridge structures, there are inevitable differences between the finite element model and the physical model, where the model has to be updated. It is problematic that the updating structural matrix cannot be fed back into the existing general finite element calculation software in the traditional structural matrix updating method. In this paper, a parameter-type updating method based on the “Kriging model + swarm intelligence” optimization is proposed. The Kriging model, based on Genetic Algorithm (GA), Bird Mating Optimizer (BMO), and Particle Swarm Optimization algorithm (PSO), is introduced into the finite element model, updating this to correct the design parameters of the finite element model. Firstly, a truss structure was used to verify the effectiveness of the proposed optimization method, and then a cable-stayed bridge was taken as an example. Three methods were used to update the finite element model of the bridge, and the results of the three optimization algorithms were compared and analyzed. The results show that, compared with the other two methods, the GA-based model updating method has the least time due to the small computation. The results of the BMO-based model were time consuming compared to the other two algorithms, and the parameter identification results were better than the GA algorithm. The PSO algorithm-based model updating method to solve the finite element model was repeated, which required a large amount of computation and was more time consuming; however, it had the highest parameter correction accuracy. Full article

(This article belongs to the Special Issue Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data)

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