Related papers: Machine Learning-Based Assessment of Energy Behavi…
The recent surge in earthquake engineering is the use of machine learning methods to develop predictive models for structural behavior. Complex black-box models are typically used for decision making to achieve high accuracy; however, as…
Backbone curves are used to characterize nonlinear responses of structural elements by simplifying the cyclic force-deformation relationships. Accurate modeling of cyclic behavior can be achieved with a reliable backbone curve model. In…
Post-earthquake damage evaluation has indicated that although the buildings with shear walls exhibited an appropriate overall performance in the recent severe earthquakes, in some cases, however, the columns and the shear walls were…
The presented study aims to provide an efficient and reliable tool for rapid estimation of the shear capacity of a TRM-strengthened masonry wall. For this purpose, a data-driven methodology based on a machine learning system is proposed…
Seismic assessment of buildings and determination of their structural damage is at the forefront of modern scientific research. Since now, several researchers have proposed a number of procedures, in an attempt to estimate the damage…
The high occupancy rates in urban multi-story buildings, combined with present safety concerns, necessarily prompt a reassessment of performance goals. Given the notable seismic damage and instances of weak-story failures that have been…
Traditional energy-based learning models associate a single energy metric to each configuration of variables involved in the underlying optimization process. Such models associate the lowest energy state to the optimal configuration of…
Accurate prediction of shear strength parameters in Municipal Solid Waste (MSW) remains a critical challenge in geotechnical engineering due to the heterogeneous nature of waste materials and their temporal evolution through degradation…
Developing a rapid, but also reliable and efficient, method for classifying the seismic damage potential of buildings constructed in countries with regions of high seismicity is always at the forefront of modern scientific research. Such a…
Accurately predicting the dynamic responses of building structures under seismic loads is essential for ensuring structural safety and minimizing potential damage. This critical aspect of structural analysis allows engineers to evaluate how…
During the past earthquakes, different low ductile failure modes are observed in the gravity design structures and thus, the most of existing damage indices may fail to assess the damage of gravity design structures accurately in referring…
In any domain involving some stressed solids, that is, from seismology to general engineering, the strength of matter is a paramount feature to understand. We here discuss the ability of a simple thermally activated sub-critical model, that…
Strong-motion duration and number of cycles of motion are the well-known parameters with which earthquake engineers currently use to take into account the influence of motion duration for the dynamic analysis of structures. In addition to…
Understanding the behavior of materials under irradiation is crucial for the design and safety of nuclear reactors, spacecraft, and other radiation environments. The threshold displacement energy (Ed) is a critical parameter for…
Reservoir characterization involves the estimation petrophysical properties from well-log data and seismic data. Estimating such properties is a challenging task due to the non-linearity and heterogeneity of the subsurface. Various attempts…
The standard model of infrastructure resilience, the resilience triangle, has been the primary way of characterizing and quantifying infrastructure resilience. However, the theoretical model merely provides a one-size-fits-all framework for…
The last decade has shown a tremendous success in solving various computer vision problems with the help of deep learning techniques. Lately, many works have demonstrated that learning-based approaches with suitable network architectures…
Seismic inversion refers to the process of estimating reservoir rock properties from seismic reflection data. Conventional and machine learning-based inversion workflows usually work in a trace-by-trace fashion on seismic data, utilizing…
In earthquake-prone zones, the seismic performance of reinforced concrete cantilever (RCC) retaining walls is significant. In this study, the seismic performance was investigated using horizontal and vertical pseudo-static coefficients. To…
Energy dissipation in sheared dry and wet granulates is considered in the presence of an externally applied confining pressure. Discrete element simulations reveal that for sufficiently small confining pressures, the energy dissipation is…