Vision Device Applied to Damage Identification in Civil Engineer Structures

Busca, Giorgio; Cigada, Alfredo; Zappa, Emanuele

doi:10.1007/978-3-319-04570-2_22

Cited by 7 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The modal parameters (damping ratios, frequencies and mode shapes) of the monitored structures are considered as extracted features in parametric machine learning-based methods. 64 On other hand, other feature techniques such as wavelet transform, 65 autoregressive modelling, 66 basic statistical analysis (using the variance and mean of the signals), 67 time-frequency methods 68 and PCA 69 are implemented in non-parametric machine learning-based methods.…”

Section: Crack Assessment Of Smart Structures With Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

Compatibility and challenges in machine learning approach for structural crack assessment

Omar

Khan

Starr

2022

Structural Health Monitoring

View full text Add to dashboard Cite

Structural health monitoring and assessment (SHMA) is exceptionally essential for preserving and sustaining any mechanical structure’s service life. A successful assessment should provide reliable and resolute information to maintain the continuous performance of the structure. This information can effectively determine crack progression and its overall impact on the structural operation. However, the available sensing techniques and methods for performing SHMA generate raw measurements that require significant data processing before making any valuable predictions. Machine learning (ML) algorithms (supervised and unsupervised learning) have been extensively used for such data processing. These algorithms extract damage-sensitive features from the raw data to identify structural conditions and performance. As per the available published literature, the extraction of these features has been quite random and used by academic researchers without a suitability justification. In this paper, a comprehensive literature review is performed to emphasise the influence of damage-sensitive features on ML algorithms. The selection and suitability of these features are critically reviewed while processing raw data obtained from different materials (metals, composites and polymers). It has been found that an accurate crack prediction is only possible if the selection of damage-sensitive features and ML algorithms is performed based on available raw data and structure material type. This paper also highlights the current challenges and limitations during the mentioned sections.

show abstract

Section: Crack Assessment Of Smart Structures With Machine Learningmentioning

confidence: 99%

“…The modal parameters (damping ratios, frequencies and mode shapes) of the monitored structures are considered as extracted features in parametric machine learning-based methods. 64…”

Section: Crack Assessment Of Smart Structures With Machine Learningmentioning

confidence: 99%

Compatibility and challenges in machine learning approach for structural crack assessment

Omar

Khan

Starr

2022

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…To prevent structural damage from causing property loss and casualties, many infrastructures are equipped with structural health monitoring (SHM) systems that assess operational status by collecting and analyzing structural vibration data [1] . With the advancement of Artificial Intelligence (AI), Machine Learning (ML) has been widely used in SHM [2] . Damage identification through ML typically includes two steps: feature extraction and damage identification.…”

Section: Introductionmentioning

confidence: 99%

Study of structural damage identification through various convolutional neural network models: exploring the accuracy and efficiency

Li,

Cao

2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

View full text Add to dashboard Cite

In recent years, Artificial Intelligence (AI) has found widespread application in various fields, owing to its efficient data processing capabilities. In Structural Health Monitoring (SHM), AI methods are employed to analyze structural vibration frequency signals, thereby enabling the identification of structural damage. However, due to the existence of numerous methods, further research is essential to investigate the impact of different approaches of damage identification results. In this study, three types of convolutional neural networks (CNNs) were employed to identify structural damage of the ASCE benchmark structure. Additionally, an efficient time-history data processing technique was incorporated into these CNNs. Subsequently, the damage identification results, considering different input data and network structures, were compared. The results reveal the following key findings: (1) The three utilized networks (1D-CNN, 2D-CNN, and PCNN) exhibit high identification accuracy when dealing with relatively simple damage scenarios. ( 2) The transformation of onedimensional vector data into frequency-domain signals through the Fast Fourier Transform (FFT), followed by inputting this FFT signal into the 2D-CNN, results in a significant improvement in damage identification accuracy, particularly in more complex damage scenarios. (3) The PCNN_FFT model demonstrates high accuracy when dealing with relatively simple damage scenarios, but its accuracy can be substantially reduced in more complex situations.

show abstract

“…14,15 Machine learning (ML) approaches have been introduced in the field of structural property prediction and damage detection. [16][17][18][19][20][21][22][23] Damage detection using ML techniques often requires a procedure of feature extraction followed by damage classification. For feature extraction, structural modal parameters are used in ML-based parametric damage detection methods 24 ; in ML-based non-parametric damage detection methods, multiple features can be defined by users and extracted through different techniques, such as statistical analysis, 25 regression analysis, 26 principal component analysis, 27 and wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

Parallel convolutional neural network toward high efficiency and robust structural damage identification

Cao²,

Liu

et al. 2023

Structural Health Monitoring

View full text Add to dashboard Cite

Artificial intelligence has been implemented recently for processing and analyzing monitored data for damage detection and identification in the field of structural health monitoring (SHM). Existing machine learning methods such as convolutional neural networks (CNNs) usually rely on inputs from a single domain (time or frequency), which may only provide partial information for damage identification. To address this issue, this work proposes a parallel convolutional neural network (P-CNN) that extracts multidimensional features assisted by a computer vision technique. The proposed network comprises a one-dimensional (1D) CNN branch, a two-dimensional (2D) CNN branch, and several fully connected layers. The efficiency and robustness of the proposed network were validated by a public experimental dataset. Our results show that (1) the features extracted by the P-CNN were separated more easily compared with those by 1D-CNN or 2D-CNN; (2) when detecting structural damages, the accuracy of the P-CNN is above 99.4%; (3) the P-CNN exhibits a robust performance when subjected to a high (5 dB) signal-to-noise ratio of the original data; and (4) when compared with traditionally used methods such as GoogLenet and Resnet, the P-CNN outperforms on many aspects of damage identification. We envision that the proposed P-CNN can be integrated into advanced SHM systems with high fidelity and intelligence.

show abstract

Vision Device Applied to Damage Identification in Civil Engineer Structures

Cited by 7 publications

References 31 publications

Compatibility and challenges in machine learning approach for structural crack assessment

Compatibility and challenges in machine learning approach for structural crack assessment

Study of structural damage identification through various convolutional neural network models: exploring the accuracy and efficiency

Parallel convolutional neural network toward high efficiency and robust structural damage identification

Contact Info

Product

Resources

About