A novel structural damage identification scheme based on deep learning framework

Wang, Xinwei; Zhang, Xun’an; Shahzad, Muhammad Moman

doi:10.1016/j.istruc.2020.12.036

Cited by 32 publications

(13 citation statements)

References 39 publications

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“…[10] proposed a method by parameter sensitivity to identify structural damage and damping defects of nonclassical damping shear buildings. [11] proposed a new method for structural damage identification by the low accuracy of structural damage recognition by time series data, combined with the advantages of Hilbert-Huang Transform and deep neural networks. Gao and Mosalam (2018) [12] implemented the most advanced deep learning technology in the application of civil engineering, that is, identifying structural damage from images.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Historic Building Survey Technology under Artificial Intelligence Wireless Network Technology Environment

Tan

Fen

2021

Wireless Communications and Mobile Computing

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In order to optimize the technology of the building, the damage identification of the building structure is studied. Firstly, back propagation neural network (BPNN) and information fusion technology are used to build neural network models. Secondly, the established model is trained. Finally, the displacement mode, natural frequency, Modal Assurance Criterion (MAC), and three kinds of information fusion with only one characteristic information are used as input data to analyse the results of BPNN identification damage. The results show that when the natural frequency is used as the sensitive feature of damage, the accuracy is the highest. The difference between the network output value and the expected value is the smallest, the network output is the most stable, and the network recognition effect is the best. The network output of a mixture of two damage depths is compared with the output of a single damage depth. The data of the network training set composed of the feature data with damage depth of 20 mm and 5 mm has higher accuracy and more accurate damage recognition. This research provides a reference for the optimization of building survey technology and has certain practical value.

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Section: Literature Reviewmentioning

confidence: 99%

Optimization of Historic Building Survey Technology under Artificial Intelligence Wireless Network Technology Environment

Tan

Fen

2021

Wireless Communications and Mobile Computing

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“…In the SHM domain, ML approaches are increasingly attracting attention along with the advancements of the highly potential Artificial Neural Networks (ANN) in data-driven Deep Leaning (DL) architectures. 19,20 A variety of cases have been reported [7][8][9][10][11] where processing of vibration signals by DL can excel in SHM tasks for damage or anomaly detection and fault classification. The main requirement however remains the availability of large datasets of different health states in order properly train the mathematical models.…”

Section: Introductionmentioning

confidence: 99%

Model-based damage identification with simulated transmittance deviations and deep learning classification

Seventekidis

Giagopoulos

2022

Structural Health Monitoring

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Damage detection and identification is one of the main tasks in vibration based Structural Health Monitoring (SHM). The robustness of such SHM applications depends among others on the amount and quality of data that can be acquired. Model-based SHM methods may offer such data in unlimited numbers by simulating different structural states; however the main drawback remains the accuracy of models especially for small damages and early detection scenarios. In the present work, a method is presented where SHM data is generated though Finite Element (FE) models, simulating transmittance deviations from reference healthy states. The method is tested on a Carbon Fiber Reinforced Polymer truss for multiple damage scenarios of relatively realistic and small magnitude, affecting different truss members. The transmittance deviations for each scenario are approximated in the FE model by reducing the stiffness of the corresponding components simulating in parallel different uncertainties, resulting in a rich training dataset. The simulated data is finally passed to a Deep Learning (DL) classifier which is later validated on the experimental damages. The dataset is proven to provide to the DL classifier the appropriate information to generalize on the experimental states and the method has potential to contribute to model-based SHM applications. The numerical to experimental generalization is proven to depend on the uncertainty simulation of various model parameters.

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“…However, real estate companies still have some economic problems [5][6][7][8], such as (1) demand is determined by supply, and urbanization planning is divorced from the process of marketization. Under the condition of market economy, the site selection, scale, scope and timing of the transfer of urban land use right should be determined by the demand of the market rather than the supply of the government.…”

Section: Introductionmentioning

confidence: 99%

Economic Crisis Early Warning of Real Estate Companies Based on PSO-Optimized SVM

2022

Journal of Sensors

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For the possible economic crisis of real estate companies, by analyzing the shortcomings of support vector machine (SVM) model, after optimization with particle swarm optimization (PSO), the PSO-SVM model was established by changing economic condition parameters and using data to warn the real estate economic crisis. Then, this model is used to warn the economic capacity of four real estate companies in Beijing. The results show that this model can further predict and analyze the solvency index, operating ability index, development ability index, profitability index, cash flow index, and comprehensive financial index of real estate companies and come to the conclusion that profitability index is the most important, and higher profitability can resist the harm brought by economic crisis. Finally, it analyzes the economic crisis that real estate companies may encounter and puts forward specific suggestions.

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A novel structural damage identification scheme based on deep learning framework

Cited by 32 publications

References 39 publications

Optimization of Historic Building Survey Technology under Artificial Intelligence Wireless Network Technology Environment

Optimization of Historic Building Survey Technology under Artificial Intelligence Wireless Network Technology Environment

Model-based damage identification with simulated transmittance deviations and deep learning classification

Economic Crisis Early Warning of Real Estate Companies Based on PSO-Optimized SVM

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