• Structural Engineering and Mechanics
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• v.53 no.2
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• pp.355-372
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• 2015

This paper identifies the effects of infill wall existence and arrangement in the seismic response of steel frame structures. The methodology followed was based on the utilisation of overall seismic response indicators that distil the complexity of structural response in a single value hence enabling their straightforward comparative and statistical post process. The overall structure damage index after Park/Ang ($OSDI_{PA}$) and the maximum inter-story drift ratio (MISDR) have been selected as widely utilized structural seismic response parameters in contemporary state of art. In this respect a set of 225 Greek antiseismic code (EAK) spectrum compatible artificial accelerograms have been created and a series of non-linear dynamic analyses have been executed. Data were obtained through nonlinear dynamic analyses carried on an indicative steel frame structure with 5 different infill wall topologies. Results indicated the significant overall contribution of infill walls with a reduction that ranged 35-47% of the maximum and 74-81% of the average recorded $OSDI_{PA}$ values followed by an overall reduction of 64-67% and 58-61% for the respective maximum and average recorded MISDR values demonstrating the relative benefits of infill walls presence overall as well as localised with similar reductions observed in 1st level damage indicators.

• Smart Structures and Systems
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• v.31 no.4
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• pp.351-363
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• 2023

Visual structural inspections are an inseparable part of post-earthquake damage assessments. With unmanned aerial vehicles (UAVs) establishing a new frontier in visual inspections, there are major computational challenges in processing the collected massive amounts of high-resolution visual data. We propose twin deep learning models that can provide accurate high-resolution structural components and damage segmentation masks efficiently. The traditional approach to cope with high memory computational demands is to either uniformly downsample the raw images at the price of losing fine local details or cropping smaller parts of the images leading to a loss of global contextual information. Therefore, our twin models comprising Trainable Resizing for high-resolution Segmentation Network (TRS-Net) and DmgFormer approaches the global and local semantics from different perspectives. TRS-Net is a compound, high-resolution segmentation architecture equipped with learnable downsampler and upsampler modules to minimize information loss for optimal performance and efficiency. DmgFormer utilizes a transformer backbone and a convolutional decoder head with skip connections on a grid of crops aiming for high precision learning without downsizing. An augmented inference technique is used to boost performance further and reduce the possible loss of context due to grid cropping. Comprehensive experiments have been performed on the 3D physics-based graphics models (PBGMs) synthetic environments in the QuakeCity dataset. The proposed framework is evaluated using several metrics on three segmentation tasks: component type, component damage state, and global damage (crack, rebar, spalling). The models were developed as part of the 2^nd International Competition for Structural Health Monitoring.

• International Journal of Concrete Structures and Materials
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• v.10 no.4
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• pp.407-424
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• 2016

This study tests ten full-size simple-supported beam specimens with the high-strength reinforcing steel bars (SD685 and SD785) using the four-point loading. The measured compressive strength of the concrete is in the range of 70-100 MPa. The main variable considered in the study is the shear-span to depth ratio. Based on the experimental data that include maximum shear crack width, residual shear crack width, angle of the main crack and shear drift ratio, a simplified equation are proposed to predict the shear deformation of the high-strength reinforced concrete (HSRC) beam member. Besides the post-earthquake damage assessment, these results can also be used to build the performance-based design for HSRC structures. And using the allowable shear stress at the peak maximum shear crack width of 0.4 and 1.0 mm to suggest the design formulas that can ensure service-ability (long-term loading) and reparability (short-term loading) for shear-critical HSRC beam members.

• Smart Structures and Systems
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• v.33 no.6
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• pp.449-463
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• 2024

Automated crack detection is crucial for structural health monitoring and post-earthquake rapid damage detection. However, realizing high precision automatic crack detection in the absence of corresponding manual labeling presents a formidable challenge. This paper presents a novel crack segmentation transfer learning method and a novel crack segmentation model called Swin-CrackFormer. The proposed method facilitates efficient crack image style transfer through a meticulously designed data preprocessing technique, followed by the utilization of a GAN model for image style transfer. Moreover, the proposed Swin-CrackFormer combines the advantages of Transformer and convolution operations to achieve effective local and global feature extraction. To verify the effectiveness of the proposed method, this study validates the proposed method on three unlabeled crack datasets and evaluates the Swin-CrackFormer model on the METU dataset. Experimental results demonstrate that the crack transfer learning method significantly improves the crack segmentation performance on unlabeled crack datasets. Moreover, the Swin-CrackFormer model achieved the best detection result on the METU dataset, surpassing existing crack segmentation models.