Vasilis Sarhosis

Bio: Vasilis Sarhosis is an academic researcher from University of Leeds. The author has contributed to research in topics: Masonry & Arch. The author has an hindex of 23, co-authored 91 publications receiving 1544 citations. Previous affiliations of Vasilis Sarhosis include Newcastle University & Cardiff University.

Modeling Strategies for the Computational Analysis of Unreinforced Masonry Structures: Review and Classification

Abstract: Masonry structures, although classically suitable to withstand gravitational loads, are sensibly vulnerable if subjected to extraordinary actions such as earthquakes, exhibiting cracks even for events of moderate intensity compared to other structural typologies like as reinforced concrete or steel buildings. In the last half-century, the scientific community devoted a consistent effort to the computational analysis of masonry structures in order to develop tools for the prediction (and the assessment) of their structural behavior. Given the complexity of the mechanics of masonry, different approaches and scales of representation of the mechanical behavior of masonry, as well as different strategies of analysis, have been proposed. In this paper, a comprehensive review of the existing modeling strategies for masonry structures, as well as a novel classification of these strategies are presented. Although a fully coherent collocation of all the modeling approaches is substantially impossible due to the peculiar features of each solution proposed, this classification attempts to make some order on the wide scientific production on this field. The modeling strategies are herein classified into four main categories: block-based models, continuum models, geometry-based models, and macroelement models. Each category is comprehensively reviewed. The future challenges of computational analysis of masonry structures are also discussed.

A macro-modelling approach for the analysis of infilled frame structures considering the effects of openings and vertical loads

Abstract: During the last decades, several macro-models have been proposed for the modelling of the infill panels' contribution to the lateral strength of frames Despite all this effort, a robust model, which takes into account the influence of the vertical load, is not yet available Furthermore, the influence of the very common case of infill walls with openings, such as windows and doors, has been neglected in all the code provisions that have been published so far In this paper, an updated macro-model, based on the equivalent pin-jointed diagonal compressive strut, is presented The proposed macro-model is able to represent the stiffening effect of the infill panel with openings by taking into account both the size of the opening and the vertical load acting on the frame Detailed and in-depth parametrical investigation, based on finite element analysis, shows that the proposed mathematical macro-model can be used as a reliable and useful tool for the determination of the equivalent compressive strut width since it accounts for a large number of parameters, which are not generally accounted for by the already available models in the literature

Closure of "Rocking of Rigid Blocks Due to Harmonic Shaking"

Abstract: The dynamic behavior of rigid-block structures resting on a rigid foundation subjected to horizontal harmonic excitation is examined. For slender structures, the nonlinear equation of motion is approximated by a piecewise linear equation. Using this approximation for an initially quiescent structure, safe or no-toppling and unsafe regions are identified in an excitation amplitude versus excitation frequency plane. Furthermore, several possible modes of steady-state response are detected, and analytical procedures are developed for determining the amplitudes of the predominant modes and for performing stability analyses. It is shown that the produced stability diagrams can be beneficial to assessing the toppling potential of a rigid-block structure under a given amplitude-frequency combination of harmonic excitation; in this manner the integration of the equation of motion is circumvented.

Modeling Strategies for the Computational Analysis of Unreinforced Masonry Structures: Review and Classification

Abstract: Masonry structures, although classically suitable to withstand gravitational loads, are sensibly vulnerable if subjected to extraordinary actions such as earthquakes, exhibiting cracks even for events of moderate intensity compared to other structural typologies like as reinforced concrete or steel buildings. In the last half-century, the scientific community devoted a consistent effort to the computational analysis of masonry structures in order to develop tools for the prediction (and the assessment) of their structural behavior. Given the complexity of the mechanics of masonry, different approaches and scales of representation of the mechanical behavior of masonry, as well as different strategies of analysis, have been proposed. In this paper, a comprehensive review of the existing modeling strategies for masonry structures, as well as a novel classification of these strategies are presented. Although a fully coherent collocation of all the modeling approaches is substantially impossible due to the peculiar features of each solution proposed, this classification attempts to make some order on the wide scientific production on this field. The modeling strategies are herein classified into four main categories: block-based models, continuum models, geometry-based models, and macroelement models. Each category is comprehensively reviewed. The future challenges of computational analysis of masonry structures are also discussed.

Prediction of the fundamental period of infilled RC frame structures using artificial neural networks

Abstract: The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value.

A Plastic-Fracture Model for Concrete

Abstract: Abstract The paper summarizes recent efforts in formulating an elastic-plastic-fracture model for the finite-element analysis of concrete structures. Based on the geometrical considerations, a four-parameter fracture (or yielding) criterion was proposed which embraces some of the simpler existing models. Isotropic elastic and anisotropic elastic behaviors were proposed for the initial loading and the post-failure behaviors. A plastic model displaying mixed hardening was proposed to describe material behaviors between the initial yielding and the fracture failure. Incremental stress-strain relationships were derived based on the associated flow rule and Ziegler's kinematic hardening rule. Three different types of failure modes were considered. A simple crushing coefficient was defined based on a dual criterion to identify the crushing type, the cracking type and the mixed type of failure. Material parameters required for each element of the plastic-fracture model were determined. An important feature of the paper is that matrix formulations for all the constitutive equations were derived and are available for finite-element implementations.