No abstract

No abstract

No abstract

In seismic regions, steel braced frames are one of the most commonly used seismic lateral force resisting systems for their reliable performance. This study presents a comparative seismic performance of different braced steel frames at their system levels. Three types of bracings for steel frames are investigated: Buckling Restrained Bracing (BRB), Superelastic Shape Memory Alloy (SMA) bar reinforced Piston Based Self Centering (named as PBSC) bracing, and Friction Spring Based Piston Bracing (named as SBPB). A methodology to evaluate the structural response of the building in a probabilistic framework is used. The procedure to estimate the probability of exceeding certain limit states conditioned on the ground motion intensity is applied to the structures. Emphasis is given to the estimation of the probability of exceedance of peak Interstory Drift Ratios (IDR). The peak interstory drift ratio provides a way to estimate the damage to structural components. For this purpose, four, six, eight, and twelve-story structures, designed with the three bracing types are used. A large number of Incremental Dynamic Analyses are performed to derive three-dimensional (3D) vulnerability functions that involve building heights and bracing types. This versatile 3D format enables the interpolation of results to arrive at the seismic fragilities of structures with different stories. The results show that the SBPB and PBSC frames outperformed the BRB frames in terms of damage probability.

No abstract

No abstract

Superelastic shape memory alloy exhibits flag-shaped hysteresis with self-centering capability. Nevertheless, shape memory alloy undergoes some residual deformation after large plastic strain, especially under repeated cyclic loading. In order to accurately simulate this behavior during nonlinear dynamic time-history analysis, a shape memory alloy flag-shaped hysteresis model with sliding response has been developed. This article shows the gradual development process of this new hysteresis model and provides analysis and verification results to support this claim. A MATLAB-based superelastic uniaxial shape memory alloy material hysteresis model has been developed and was incorporated into a finite element program specifically designed for the piston-based self-centering bracing. This piston-based self-centering bracing system uses superelastic shape memory alloy bars for its energy dissipation and self-centering capability. A proof-of-concept brace specimen was fabricated and tested where numerical and experimental results showed excellent matching. The finite element program was utilized to capture the varying nonlinear quasi-static response of the piston-based self-centering brace. Finally, the piston-based self-centering brace responses from this analysis were used to develop a novel shape memory alloy flag-shaped hysteresis model with sliding response, which was implemented in finite element analysis and design software, S-FRAME. Nonlinear dynamic time-history analysis proves the effectiveness of such bracing in steel frames in reducing interstory drift.

Periodical vision-based inspection is a principal form of structural health monitoring (SHM) technique. Over the last decades, vision-based artificial intelligence (AI) has successfully facilitated an effortless inspection system owing to its exceptional ability of accuracy of defects’ pattern recognition. However, most deep learning (DL)-based methods detect one specific type of defect, whereas DL has a high proficiency in multiple object detection. This study developed a dataset of two types of defects, i.e., concrete crack and spalling, and applied various pre-built convolutional neural network (CNN) models, i.e., VGG-19, ResNet-50, InceptionV3, Xception, and MobileNetV2 to classify these concrete defects. The dataset developed for this study has one of the largest collections of original images of concrete crack and spalling and avoided the augmentation process to replicate a more real-world condition, which makes the dataset one of a kind. Moreover, a detailed sensitivity analysis of hyper-parameters (i.e., optimizers, learning rate) was conducted to compare the classification models’ performance and identify the optimal image classification condition for the best-performed CNN model. After analyzing all the models, InceptionV3 outperformed all the other models with an accuracy of 91%, precision of 83%, and recall of 100%. The InceptionV3 model performed best with optimizer stochastic gradient descent (SGD) and a learning rate of 0.001.