Structural Damage Detection in Civil Engineering with Machine Learning: Current State of the Art

Avcı, Onur; Abdeljaber, Osama; Kıranyaz, Serkan

doi:10.1007/978-3-030-75988-9_17

Cited by 11 publications

(3 citation statements)

References 60 publications

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“…Artificial intelligence (AI) methods have various applications in civil and structural engineering [2,3], in several areas such as: Structural health monitoring [4], structural damage identification [5,6], structural design optimization [7], structural modelling [8][9][10], predictive maintenance [11,12], construction planning and management [13], risk assessment [14,15], predicting strength and other structural characteristics [16][17][18], and energy efficiency [19], among others. AI methods have also been recently used in wall surface defect classification [20].…”

Section: Introductionmentioning

confidence: 99%

Classification of Wall Defects for Maintenance Purposes Using Image Processing

Qayyum,

Ehtisham,

Plevris

et al. 2023

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Stakeholders are increasingly interested in finding efficient methods for regularly surveying and reporting on the state of their building assets, focusing on accuracy, consistency, and ease of use. High-rise buildings can exhibit wall surface defects and flaws such as cracks and spalls, which can significantly affect the structures' safety and appearance. Such problems need to be taken care of in a timely manner, before they become too hazardous or costly to fix. In this research work, images of several types of wall damage are classified into three main categories: (i) Undamaged; (ii) Cracked; and (iii) Miscellaneous. In total, 6000 images were used in the dataset, equally subdivided into the three categories. In machine learning, convolutional neural networks (CNNs) stand out as a form of neural network that excels at image classification. A transfer learning approach was implemented to classify wall surface defect images using three pre-trained CNN models, namely ResNet-50, ResNet-101, and Inception V3. 70% of the data set was used for training purposes, and the remaining 30% was used for validation. Several metrics including accuracy, precision, recall, and F1-score were computed for each model, in an attempt to find the best model for the damage classification task at hand. According to the results, Inception V3 demonstrated superior performance compared to the ResNet-50 and ResNet-101 models, achieving an overall accuracy of 87.1%. In contrast, ResNet-101 and Res-Net-50 obtained overall accuracies of 85.3% and 78.3%, respectively. The suggested methodology offers several benefits and a clear potential for broader adoption in the future as it can significantly reduce the time and effort required for manual inspection and classification of defects, allowing for more efficient maintenance and repair processes.

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Section: Introductionmentioning

confidence: 99%

Classification of Wall Defects for Maintenance Purposes Using Image Processing

Qayyum,

Ehtisham,

Plevris

et al. 2023

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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“…Artificial intelligence (AI) methods have seen several applications in civil and structural engineering [6,7], in many different areas such as: Structural modelling [8-10], structural design optimization [11], predictive maintenance [12,13], SHM [14], risk assessment [15,16], predicting strength [17,18] and other structural characteristics [19], energy efficiency [20], construction planning and management [21], and others. AI methods have also been increasingly utilized in the field of structural engineering for damage detection, offering improved accuracy and efficiency compared to traditional methods [22,23].…”

Section: Introductionmentioning

confidence: 99%

Classification and Computing the Defected Area of Knots in Wooden Structures Using Image Processing and CNN

Ehtisham,

Qayyum,

Plevris

et al. 2023

15th International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control

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Knots in wooden structures are common natural features in wood that result from where branches once joined the trunk of a tree. While they can add to the aesthetic appeal of wood, knots are often considered structural defects in construction because they can significantly affect the mechanical properties of wood. If knots are present in structural members, they cannot be ignored. Identifying the presence of knots and finding the corresponding defected area of a structural member is important to be able to reinforce the member, compensate for the reduced strength and ensure that it is safe and suitable for its intended use. In this study, the Inception-ResNet-V2 pre-trained Convolutional Neural Network (CNN) model is trained and validated with 2000 images for the classification of knots, and the defected area is calculated through Image Processing (IP) and other soft computing techniques. The images of knots are collected and equally classified into two categories: 1000 "Single knot" and 1000 "Multiple knots" images. 70% of the dataset is used for training, and 30% for model validation. Four statistical parameters, namely accuracy, precision, recall, and F1 score, are calculated to check the model performance for the classification task, as well as the corresponding confusion matrix. The model exhibited an overall accuracy of 84% in an independent evaluation with a new testing dataset of 200 images, while the defects could be properly quantified using IP techniques.The research work shows the potential of AI-based methodologies in structural health monitoring and damage identification. These methods can drastically improve our ability to assess the condition of structures and structural elements, offering enhanced precision and accuracy, real-time and cost-effective monitoring, predictive capabilities, and automation opportunities.

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“…In recent years, a lot of papers have discussed data-driven methods. Reich [33] and Avci et al [34] discussed the application of data-driven damage identification methods in civil engineering in detail. Yuan et al [35] proposed an artificial neural network (ANN) which can be used for seismic estimation and damage assessment.…”

Section: Introductionmentioning

confidence: 99%

Seismic Damage Identification of Composite Cable-Stayed Bridges Using Support Vector Machines and Wavelet Networks

2022

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A seismic damage identification method for composite cable-stayed bridges has been developed. The proposed method is based on a Support Vector Machine (SVM) and Wavelet Network (WN). A shaking table test of a composite cable-stayed bridge is employed to verify the identification accuracy of the WNSVM method; the test results show that the nonlinear Finite Element Model (FEM) can correctly simulate the single-tower cable-stayed bridge, and the learning samples of WNSVM can be produced based on the nonlinear FEM. The structural damage results identified by the WNSVM method are in good agreement with those obtained by the shaking table test, and the maximum error is less than 8%. Therefore, the WNSVM method can be used for the seismic damage identification of composite cable-stayed bridges.

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Structural Damage Detection in Civil Engineering with Machine Learning: Current State of the Art

Cited by 11 publications

References 60 publications

Classification of Wall Defects for Maintenance Purposes Using Image Processing

Classification of Wall Defects for Maintenance Purposes Using Image Processing

Classification and Computing the Defected Area of Knots in Wooden Structures Using Image Processing and CNN

Seismic Damage Identification of Composite Cable-Stayed Bridges Using Support Vector Machines and Wavelet Networks

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