In recent years, on account of their excellent mechanical properties, composite materials (made of epoxy-bonded carbon, glass, or aramid fibers) have been used to reinforce masonry walls against in-plane actions. These materials have proven to be an effective solution for the strengthening of unreinforced masonry (URM) walls. Lately, research has shifted to the study of different types of fibers to avoid the use of epoxy adhesives, whose long-term behavior and compatibility with masonry are poor. This paper describes an experimental program that investigated the behavior of URM shear walls strengthened with two types of commercially available polypropylene products: short fibers (fiber length = 12 mm) and polypropylene nets. This investigation aimed to evaluate the influence of polypropylene reinforcement, embedded into an inorganic matrix, in terms of the improvement of the lateral load-carrying capacity, failure mechanism, ductility, and energy dissipation capacity of URM wall panels, where nine walls were subjected to in-plane loads using a racking test setup. The study showed that using two layers of polypropylene fibers embedded into a cementitious matrix greatly increased the in-plane load capacity of the brickwork masonry. On the other hand, the test results indicated that polypropylene nets, used as a repair method for cracked shear walls, cannot improve the structural performance of the walls.
In this paper, it is presented the experimental results of a campaign on diagonal compression tests, as of ASTM E519- IntroductionUnreinforced masonry (URM) buildings are one of the most used construction type in Europe, around the Mediterranean basin and Balkan peninsula. These regions are characterized with medium-to-high levels of seismic hazard. A vast number of URM buildings in these regions are vulnerable against earthquakes which are acknowledged to be one of the major cause of their damage, often even for their collapse. In such event, the load bearing walls are subjected to a combination of lateral seismic forces, that are in the form of out-of-plane or in-plane loading depending on the orientation of the building with seismic loading direction.Masonry structures, under such type of loading condition, manifest a brittle behavior, a relatively poor performance and are susceptible to high degrees of structural damage. Since the out-of-plane failure could be avoided by additional structural elements, the overall seismic performance of URM buildings depends on the capacity of in-plane walls to safely transfer the lateral loads to foundations, providing the post-earthquake stability necessary to avoid collapse of the entire structure [1].Moreover, during their lifespan, many of those buildings have suffered from the combined effects of inadequate construction techniques, seismic and wind loads, foundation settlements and deterioration of construction materials [2].To increase low parameters of masonry such as tensile and shear strength, as well as to improve the poor structural performance of URM structures under seismic actions, various strengthening techniques have been developed and applied throughout history of construction. The earliest techniques, the so-called traditional, consist of applying the reinforcement in form of: (i) filling cracks and voids by grouting; ii) stitching of large cracks and weak areas with metallic or brick elements; iii) external or internal post-tensioning with steel ties; iv) shotcrete jacketing; v) ferrocement and vi) center core etc. [3,4].Some successful examples of ferrocement jacketing have been observed in concrete structures [5][6][7][8] as well as in masonry structures where ferrocement provided a considerable increase in ductility, improvement of crack resistance [9,10], and increased stiffness and load carrying capacity as well as increase of in-plane resistance [11][12][13][14][15].
This paper addresses the problem of sustainability in remediation, retrofit, and seismic upgrading of historic masonry structures. Different rehabilitation techniques and some successful applications throughout the Balkans and Italy are described, with particular emphasis to the shear reinforcement of wall panels. The selected techniques aim at improving the seismic performance, preserving the structures for future generations, having the least impact in altering the architectural and heritage values, as well as being sustainable, in terms of reduced carbon dioxide emissions, reversibility, and low energy consumption. The use of cross-laminated timber (CLT), natural fibers, and fiber-reinforced Polymers (FRP) jacketing with natural lime coatings are discussed. The paper concludes by summarizing key successes of the proposed rehabilitation solutions in conservation engineering and suggests areas in which these could be used with great advantage.
Autonomous vehicles have gained popularity in recent years, but they are still not compatible with other vulnerable components of the traffic system, including pedestrians, bicyclists, motorcyclists, and occupants of smaller vehicles such as passenger cars. This incompatibility leads to reduced system performance and undermines traffic safety and comfort. To address this issue, the authors considered pedestrian crosswalks where vehicles, pedestrians, and micro-mobility vehicles collide at right angles in an urban road network. These road sections are areas where vulnerable people encounter vehicles perpendicularly. In order to prevent accidents in these areas, it is planned to introduce a warning system for vehicles and pedestrians. This procedure consists of multi-stage activities by sending warnings to drivers, disabled individuals, and pedestrians with phone addiction simultaneously. This collective autonomy is expected to reduce the number of accidents drastically. The aim of this paper is the automatic detection of a pedestrian crosswalk in an urban road network, designed from both pedestrian and vehicle perspectives. Faster R-CNN (R101-FPN and X101-FPN) and YOLOv7 network models were used in the analytical process of a dataset collected by the authors. Based on the detection performance comparison between both models, YOLOv7 accuracy was 98.6%, while the accuracy for Faster R-CNN was 98.29%. For the detection of different types of pedestrian crossings, YOLOv7 gave better prediction results than Faster R-CNN, although quite similar results were obtained.
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