Cyclic Behavior of Masonry Shear Walls Retrofitted with Engineered Cementitious Composite and Pseudoelastic Shape Memory Alloy.

The behavior of masonry shear walls reinforced with pseudoelastic Ni-Ti shape memory alloy (SMA) strips and engineered cementitious composite (ECC) sheets is the main focus of this paper. The walls were subjected to quasi-static cyclic in-plane loads and evaluated by using Abaqus. Eight cases of strengthening of masonry walls were investigated. Three masonry walls were strengthened with different thicknesses of ECC sheets using epoxy as adhesion, three walls were reinforced with different thicknesses of Ni-Ti strips in a cross form bonded to both the surfaces of the wall, and one was utilized as a reference wall without any reinforcing element. The final concept was a hybrid of strengthening methods in which the Ni-Ti strips were embedded in ECC sheets. The effect of mesh density on analytical outcomes is also discussed. A parameterized analysis was conducted to examine the influence of various variables such as the thickness of the Ni-Ti strips and that of ECC sheets. The results show that using the ECC sheet in combination with pseudoelastic Ni-Ti SMA strips enhances the energy absorption capacity and stiffness of masonry walls, demonstrating its efficacy as a reinforcing method.

A Novel Computer-Vision Approach Assisted by 2D-Wavelet Transform and Locality Sensitive Discriminant Analysis for Concrete Crack Detection.

This study proposes FastCrackNet, a computationally efficient crack-detection approach. Instead of a computationally costly convolutional neural network (CNN), this technique uses an effective, fully connected network, which is coupled with a 2D-wavelet image transform for analyzing and a locality sensitive discriminant analysis (LSDA) for reducing the number of features. The algorithm described here is used to detect tiny concrete cracks in two noisy adverse conditions and image shadows. By combining wavelet-based feature extraction, feature reduction, and a rapid classifier based on deep learning, this technique surpasses other image classifiers in terms of speed, performance, and resilience. In order to evaluate the accuracy and speed of FastCrackNet, two prominent pre-trained CNN architectures, namely GoogleNet and Xception, are employed. Findings reveal that FastCrackNet has better speed and accuracy than the other models. This study establishes performance and computational thresholds for classifying photos in difficult conditions. In terms of classification efficiency, FastCrackNet outperformed GoogleNet and the Xception model by more than 60 and 80 times, respectively. Furthermore, FastCrackNet's dependability was proved by its robustness and stability in the presence of uncertainties produced by network characteristics and input images, such as input image size, batch size, and input image dimensions.

Sustainable Earthquake Resilience with the Versatile Shape Memory Alloy (SMA)-Based Superelasticity-Assisted Slider.

Earthquakes threaten humanity globally in complex ways that mainly include various socioeconomic consequences of life and property losses. Resilience against seismic risks is of high importance in the modern world and needs to be sustainable. Sustainable earthquake resilience (SER) from the perspective of structural engineering means equipping the built environment with appropriate aseismic systems. Shape memory alloys (SMAs) are a class of advanced materials well suited for fulfilling the SER demand of the built environment. This article explores how this capability can be realized by the innovative SMA-based superelasticity-assisted slider (SSS), recently proposed for next-generation seismic protection of structures. The versatility of SSS is first discussed as a critical advantage for an effective SER. Alternative configurations and implementation styles of the system are presented, and other advantageous features of this high-tech isolation system (IS) are studied. Results of shaking table experiments, focused on investigating the expected usefulness of SSS for seismic protection in hospitals and conducted at the structural earthquake engineering laboratory of the University of Bonab, are then reported. SSS is compared with currently used ISs, and it is shown that SSS provides the required SER for the built environments and outperforms other ISs by benefitting from the pioneered utilization of SMAs in a novel approach.

Impact of Polypropylene Fibers on the Mechanical and Durability Characteristics of Rubber Tire Fine Aggregate Concrete.

In this research, the consequence of using rubber tire aggregates (RTA) on the durability and mechanical characteristics of polypropylene fibers (PF) reinforced concrete is evaluated. Fifteen concrete mixtures were produced and tested in the laboratory. RTA was utilized instead of fine natural aggregates (FNA) to the concrete at concentrations of 0%, 5%, 10%, 15%, and 20% by a volumetric fraction; also, the contents of PF in the concrete mixtures were 0%, 1%, and 2% by weight fraction. Finally, the following parameters were tested for all the mixtures: compressive and tensile resistances, fracture, changes in drying shrinkage, bulk electrical resistivity, elastic moduli, and resonance occurrences. The control sample was the one without RTA and PF. According to the results, by adding RTA to the mixtures, the shrinkage deformation amplified, but the PF addition caused a decrease in the shrinkage deformation. Furthermore, adding 0%, 5%, 10%, and 15% RTA, with 2% PF leads to an upsurge in the flexural resistance by 34%, 24%, 16%, and 6%, respectively, relative to the control sample without PF and RTA. Moreover, the fracture energy of mixtures increased by utilizing PF and RTA simultaneously.

Development of seismic vulnerability index methodology for reinforced concrete buildings based on nonlinear parametric analyses.

This paper presents a simplified method in the seismic vulnerability assessment of reinforced concrete (RC) buildings based on proposed seismic vulnerability index (SVI) methodology. The employed procedure is derived with some modifications from the Italian GNDT and the European Macro-seismic approaches. Eight parameters were modeled in three distinct vulnerability classes to estimate the vulnerability indices of RC structures. The vulnerability classes were categorized based on the earthquake resistant design (ERD) defined as; (Low, Moderate, and High)-ERDs. Nonlinear time history analysis (NL-THA) and nonlinear static analysis (NL-SA) were carried out to define the weight of each parameter in order to calculate the seismic vulnerability index in a specific intensity (PGA) of an earthquake event. Knowing that it ranges from 0 to 1 from less vulnerable to most vulnerable with respect to the seismic intensity. In addition, the engineering demand parameter (EDP) used to determine the vulnerability index as the maximum top displacement of the structure. After determining the (SVI), The mean damage states were developed to evaluate the estimated physical damage of buildings in distinct seismic intensities. •This simplified methodology helps to manage and implements strategies for the safety of the communities before earthquake takes place by investigating the vulnerability classes for each building type.•Modeling the parameters that have an influence on the structural behavior without considering the past-damages observations through an analytical approach.•Developing the seismic vulnerability index can reduce or limit the role of the rapid visual screening methods, which is based on expert opinion decisions, and depends on observations of damages caused by earthquakes, and can be a useful framework criterion in earthquake filed.